A Physics-Based Approach to Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems

Given that observational and numerical climate data are being produced at ever more prodigious rates, increasingly sophisticated and automated analysis techniques have become essential. Deep learning is quickly becoming a standard approach for such analyses and, while great progress is being made, major challenges remain. Unlike commercial applications in which deep learning has led to surprising successes, scientific data is highly complex and typically unlabeled. Moreover, interpretability and detecting new mechanisms are key to scientific discovery. To enhance discovery we present a complementary physics-based, data-driven approach that exploits the causal nature of spatiotemporal data sets generated by local dynamics (e.g. hydrodynamic flows). We illustrate how novel patterns and coherent structures can be discovered in cellular automata and outline the path from them to climate data.Comment: 4 pages, 1 figure; http://csc.ucdavis.edu/~cmg/compmech/pubs/ci2017_Rupe_et_al.ht

END OF LIFE MANAGEMENT OF ELECTRONIC WASTE

Electronic products are becoming obsolete at a very high rate due to rapid changes in consumer demand and technological advancements. However, on other hand End-of-Life (EOL) management of electronic products is not effectively approached while these products offer huge opportunities for effective recycling. In this context, this thesis has highlighted the current practices and issues related to EOL management of electronic products focusing on their different material compositions, the uses of their raw materials in the circular economy perspective. The thesis proposes the introduction of digital technologies into the recycling process to improve efficiency. More specifically, this thesis has focused on the corona electrostatic separation process and the improvement of efficiency based on the simulation of the particle trajectories to identify the most effective parameters. Thus, in this frame, a numerical model to predict the particle trajectories in a corona electrostatic separator is developed using COMSOL Multiphysics and MATLAB software and validated with experimental trials. The recycling of electronic waste is becoming challenging due to its diverse and constantly changing material composition. In this regard, this thesis illustrates the use of non-destructive visible near-infrared hyperspectral imaging (VNIR-HSI) technique to identify material accurately; the effectiveness of VNIR-HSI is demonstrated through an experimental campaign combined with machine learning models, such as Support Vector Machine, K-Nearest Neighbors and Neural Network.Nonostante i prodotti elettronici diventino obsoleti ad un ritmo molto elevato, a causa dei rapidi cambiamenti nella domanda dei consumatori e dei progressi tecnologici, la gestione del loro fine vita (End-of-Life (EOL)) non viene affrontata in modo efficace benché offra, invece, grandi opportunità di riciclo. In questo contesto, questa tesi ha evidenziato le attuali pratiche e problematiche relative alla gestione del fine vita dei prodotti elettronici concentrandosi sulla loro diversa composizione, l’utilizzo delle materie prime seconde ricavabili in una prospettiva di economia circolare. La tesi propone l’introduzione di tecnologie digitali nel processo di riciclo per migliorarne l'efficienza. In particolare, questa tesi si è concentrata sul processo di separazione elettrostatica a corona e sul miglioramento dell'efficienza grazie alla simulazione delle traiettorie delle particelle per identificare i parametri più efficaci. Pertanto, in questo studio, utilizzando i software COMSOL Multiphysics e MATLAB, è stato sviluppato un modello numerico per prevedere le traiettorie delle particelle in un separatore elettrostatico a corona; il modello è stato poi validato con prove sperimentali. Il riciclo dei rifiuti elettronici sta diventando sempre più complesso a causa della presenza di mix di materiali diversificati e in continua evoluzione. A questo proposito, la tecnologia di visione iperspettrale non distruttiva basata su lunghezze d’onda nel visibile e nel vicino infrarosso (VNIR-HSI) è stata utilizzata in questo lavoro di tesi per identificare il materiale in modo preciso; l'efficacia di VNIR-HSI, combinato con modelli di apprendimento automatico, come la Support Vector Machine, K-Nearest Neighbors e Neural Network, viene dimostrata attraverso una campagna sperimentale

Prognostics of Insulated Gate Bipolar Transistors

Insulated gate bipolar transistors (IGBTs) are the devices of choice for medium and high power, low frequency applications. IGBTs have been reported to fail under excessive electrical and thermal stresses in variable speed drives and are considered as reliability problems in wind turbines, inverters in hybrid electric vehicles and railway traction motors. There is a need to develop methods to detect anomalous behavior and predict the remaining useful life (RUL) of IGBTs to prevent system downtime and costly failures. In this study, a framework for prognostics of IGBTs was developed to provide early warnings of failure and predict the remaining useful life. The prognostic framework was implemented on non punch through (NPT) IGBTs. Power cycling of IGBTs was performed and the gate-emitter voltage, collector-emitter voltage, collector-emitter current and case temperature was monitored in-situ during aging. The on-state collector-emitter current (ICE(ON)) and collector-emitter voltage (VCE(ON)) were identified as precursors to IGBT failure. Electrical characterization and X-ray analysis was performed before and after aging to map degradation in the devices to observed trends in the precursor parameters. A Mahalanobis distance based approach was used for anomaly detection. The initial ICE(ON) and VCE(ON) parameters were used to compute the healthy MD distance. This healthy MD distance was transformed and the mean and standard deviation of the transformed MD data was obtained. The μ+3σ upper bound obtained from the transformed healthy MD was then used as a threshold for anomaly detection. This approach was able to detect anomalous behavior in IGBTs before failure. Upon anomaly detection, a particle filter approach was used for predicting the remaining useful life of the IGBTs. A system model was developed using the degradation trend of the VCE(ON) parameter. This model was obtained by a least squares regression of the IGBT degradation curve. The tracking and prediction performance of the model with the particle filter was demonstrated

Nosocomial Infections in Patients Admitted in Intensive Care Unit of a Tertiary Health Center, India

Background: Patients in Intensive Care Units (ICUs) are a significant subgroup of all hospitalized patients, accounting for about a quarter of all hospital infections.Aim: The aim was to study, the current status of nosocomial infection, rate of infection and distribution of infection among patients admitted in Medical Intensive Care Unit (MICU) of a District Hospital. Subjects and Methods: Data were collected retrospectively from 130 patientfs records presented with symptoms of nosocomial infection in MICU of a Tertiary Health Center, Tumkur from August 2012 to May 2013. Descriptive statistics using percentage was calculated.Results: Incidence of nosocomial infections in MICU patients was 17.7% (23/130). Of which 34.8% (8/130) was urinary tract infection (UTI) being the most frequent; followed by pneumonia 21.7% (5/130), 17.4% (4/130) surgical site infection, 13.0% (3/130) gastroenteritis, 13.0% (3/130) blood stream infection and meningitis. The nosocomial infection was seen morein the 40-60 year of age. The male were more prone to nosocomial infections than the female.Conclusion: The most frequent nosocomial infections (urinary, respiratory, and surgical site) were common in geriatric patients in the MICU setting and are associated with the use of invasive device. Large-scale studies are needed to be carried out in Indian population to plan long-term strategies for prevention and management of nosocomial infections.Keywords: Medical Intensive Care Unit, Nosocomial infections, Tertiary health centr

A prospective observational study of prescription pattern of topical steroids in dermatology OPD, BRIMS, Bidar, India

Background: In dermatology practice Glucocorticoids are the most commonly prescribed drugs. Steroids produce dramatic relief in inflammatory and pruritic skin conditions, if they are irrationally used they may lead to adverse effects. Hence this study was done to observe the prescription pattern of corticosteroids.Methods: It was a prospective observational study. The prescriptions of patient attending the dermatology OPD were screened for the usage of the corticosteroids. The demographic data, chief complaints, diagnosis and the details of the drugs were collected and analysed.Results: Out of 415 patients screened, 13.97% were prescribed corticosteroids. Corticosteroids prescribed by generic name were 26.01% and brand name was 82.05%. Corticosteroids alone were prescribed in 42.02% and along with antihistaminics/antibiotics/emollients in 66.04%. Corticosteroids were prescribed topically in 86.02% of patients and systemically in 22.04% of patients. Moderately potent steroids prescribed in 80.42% followed by potent (15.07%) and very potent steroids (12.56%). No fixed dose combination drugs were prescribed.Conclusions: The corticosteroids prescribed by brand names (82.05%) were more than generic names (26.01%), information about the strength of the steroid was not mentioned and usage of emollients was less. This indicates the need for continuous medical education for the clinicians

Why a Testing Career Is Not the First Choice of Engineers

As software systems are becoming larger, more complex, and dependent on many third-party software components, the chances of their failure are increasing further. This calls for intense efforts to improve the quality of testing in the software development process