Shelf-life of horse mackerel fish balls stored at 0-2°C

The shelf-life of standardized horse mackerel fish balls was assessed by biochemical, microbiological, organoleptic and other spoilage changes at 0-2°C. There was decrease in pH value, moisture and the organoleptic scores. Expressible water percentage, TMA-N, TVB-N and peroxide value showed increasing trends. Total plate count also increased gradually during storage. Water separation in the treated sample was observed after 12 days and slimy consistency was noticed in the control sample on the 24th day. Based on these observations, it can be concluded that fish balls can be stored at 0-2°C for 20 days

On Finding the Vertex Connectivity of Graphs

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryJoint Services Electronics Program / N00014-84-C-014

Deep learning can predict laboratory quakes from active source seismic data

Small changes in seismic wave properties foretell frictional failure in laboratory experiments and in some cases on seismic faults. Such precursors include systematic changes in wave velocity and amplitude throughout the seismic cycle. However, the relationships between wave features and shear stress are complex. Here, we use data from lab friction experiments that include continuous measurement of elastic waves traversing the fault and build data-driven models to learn these complex relations. We demonstrate that deep learning models accurately predict the timing and size of laboratory earthquakes based on wave features. Additionally, the transportability of models is explored by using data from different experiments. Our deep learning models transfer well to unseen datasets providing high-fidelity models with much less training. These prediction methods can be potentially applied in the field for earthquake early warning in conjunction with long-term time-lapse seismic monitoring of crustal faults, CO2 storage sites and unconventional energy reservoirs

An automatic abstraction technique for verifying featured, parameterised systems

A general technique combining model checking and abstraction is presented that allows property based analysis of systems consisting of an arbitrary number of featured components. We show how parameterised systems can be specified in a guarded command form with constraints placed on variables which occur in guards. We prove that results that hold for a small number of components can be shown to scale up. We then show how featured systems can be specified in a similar way, by relaxing constraints on guards. The main result is a generalisation theorem for featured systems which we apply to two well known examples

Predicting blood pressure response to fluid bolus therapy in the ICU using attention-based stacked neural networks for clinical interpretability

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages [38]-[40]).Fluid bolus therapy (FBT) is a treatment commonly administered to treat seriously ill hypotensive patients in intensive care units (ICUs). Unfortunately, only a fraction of hypotensive patients respond positively to FBT, and emergency room physicians are constantly challenged in determining whether administering FBT will result in a corresponding increase in blood pressure. In this thesis, we utilized regression models and attention-based recurrent neural network (RNN) algorithms to predict the response of hypotensive patients to FBT from a multi-clinical information system large-scale database. We investigated time-series modeling with the use of the stacked long short term memory network (LSTM) and the gated recurrent units network (GRU) models by altering the representation of our data and time-aggregated modeling using logistic regression algorithms with regularization on our original representation. Additionally, we applied the attention mechanism for clinical interpretability on our RNN models applied on the time-series representation. Among all the modeling strategies and data representations, the stacked LSTM with the attention mechanism predicted the success or failure of the FBT on hypotensive patients with a highest accuracy of 0.852 and area under the curve (AUC) value of 0.925. The aim of the study is to help identify hypotensive patients in ICUs who will experience a sufficient increase in blood pressure after FBT administration. The end goal of these results would be to develop a clinically actionable decision support tool for intensive care management.by Uma M. Girkar.M. Eng.M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc

Functional parallelism: Theoretical foundations and implementation

Thus far, parallelism at the loop level (or data-parallelism) has been almost exclusively the main target of parallelizing compilers. The variety of new parallel architectures and recent progress in interprocedural dependence analysis suggest new directions for the exploitation of parallelism across loop and procedure boundaries (or functional-parallelism). This thesis studies the problem of extracting functional parallelism from sequential programs. It presents the Hierarchical Task Graph (HTG) as an intermediate parallel program representation which encapsulates data and control dependences, and which can be used for the extraction and exploitation of functional parallelism. Control and data dependences require synchronization between tasks, and hence the problem of eliminating redundant control and data dependences is important. We show that determining precedence relationship is crucial in finding the essential data dependences for synchronization purposes, that there exists a unique minimum set of essential data dependences, and that finding this minimum set is NP-hard and NP-easy. We present heuristic algorithms, which appear to work well in practice, to find the minimum set of data dependences. The control and data dependences are used to derive execution conditions for tasks which maximize functional parallelism. We discuss the issue of optimization of such conditions and propose optimization algorithms. The hierarchical nature of the HTG facilitates efficient task-granularity control during code generation, and thus applicability for a variety of parallel architectures. The HTG has been implemented in the Parafrase-2 compiler and is used as the intermediate representation for generating parallel source code.U of I OnlyETDs are only available to UIUC Users without author permissio