Document Collection Visualization and Clustering Using An Atom Metaphor for Display and Interaction

Visual Data Mining have proven to be of high value in exploratory data analysis and data mining because it provides an intuitive feedback on data analysis and support decision-making activities. Several visualization techniques have been developed for cluster discovery such as Grand Tour, HD-Eye, Star Coordinates, etc. They are very useful tool which are visualized in 2D or 3D; however, they have not simple for users who are not trained. This thesis proposes a new approach to build a 3D clustering visualization system for document clustering by using k-mean algorithm. A cluster will be represented by a neutron (centroid) and electrons (documents) which will keep a distance with neutron by force. Our approach employs quantified domain knowledge and explorative observation as prediction to map high dimensional data onto 3D space for revealing the relationship among documents. User can perform an intuitive visual assessment of the consistency of the cluster structure

Squamous Cell Carcinoma Response To Cisplatin Exposure

Purpose: This thesis attempted to quantitatively analyze the individual cell fate choice in resistant head and neck UM-SCC38 cells exposed to cisplatin using the most current techniques available. Methods: UM-SCC-38 cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS). They were treated with cisplatin and ATM/ATR inhibitors of known dosages. Using live cell imaging, one hundred cells were tracked in each experiment and their behaviors were analyzed and entered into Microsoft Excel Spreadsheet to generate cell profile graphs. HaCaT cells, non-tumorigenic keratinocyte cell line, were also analyzed using live cell imaging and their cell fate profiles generated to better understand the resistance of SCC-38 to cisplatin. Results: Our study revealed a highly heterogeneous pattern of cell fate choices in SCC-38, in comparison to that of the control, HaCaT, cells. In both SCC-38 and HaCaT cell lines, the majority of cell death occurred in the immediate interphase without mitotic entry, whereas significant portions of SCC-38 cells survived the treatment via either checkpoint arrest or checkpoint slippage. Cells that exhibited checkpoint slippage were primarily treated or exposed to cisplatin at late-S and G2 phases. Our study also revealed cells in M-phase were hypersensitive to cisplatin. Moreover, although the cisplatin resistant progression of mitosis exhibited no delay in general, greatly prolonged mitosis correlated with the induction of cell death in mitosis. This finding suggested a combinatorial treatment using cisplatin and an agent that blocks mitotic exit, Mg-132. Consistently, we showed a strong synergy between cisplatin and the proteasome inhibitor Mg-132. Finally, targeting DNA damage checkpoint using ATR inhibitor effectively sensitized SCC-38 to cisplatin treatment. To our surprise, targeting checkpoint eliminated both checkpoint arrest and checkpoint slippage, and augmented the induction of cell death in interphase without mitotic entry. Conclusion: The diverse cell fate choices of SCC-38 and HaCaT cells were confirmed using live cell imaging. Our results showed the majority of cell death occurred in interphase without mitotic entry and a significantly smaller portion of SCC-38 cells died after the cisplatin treatment when compared to HaCaT. On the other hand, analysis of the surviving SCC-38 cells revealed the co-existence of checkpoint arrest and checkpoint slippage. However, caffeine was shown to abolish these surviving mechanisms in cisplatin treated cells. Moreover, our combination therapy of cisplatin plus MG-132 showed strong synergistic effect on SCC-38 cell death. Overall, our study revealed new insights into chemoresistance and suggested combinatorial strategies that potentially overcome cancer resistance

Effect of the green-emitting CaF2:Ce3+,Tb3+ phosphor particles’ size on color rendering index and color quality scale of the in-cup packaging multichip white LEDs

In this paper, we investigate the effect of the green-emitting CaF2:Ce (3+), Tb (3+) phosphor particle's size on the color rendering index (CRI) and the color quality scale (CQS) of the in-cup packaging multichip white LEDs (MCW-LEDs). For this purpose, 7000K and 8500K in-cup packaging MCW-LEDs is simulated by the commercial software Light Tools. Moreover, scattering process in the phosphor layers is investigated by using Mie Theory with Mat Lab software. Finally, the research results show that the green-emitting CaF2: Ce (3+), Tb (3+) phosphor's size crucially influences on the CRI and CQS. From that point of view, CaF2: Ce (3+), Tb (3+) can be proposed as a potential practical direction for manufacturing the in-cup packaging phosphor WLEDs.Web of Science13235134

Defect-engineered metal-organic frameworks (MOF-808) towards the improved adsorptive removal of organic dyes and chromium (vi) species from water

In this work, two defective zirconium-based metal-organic frameworks (Zr-MOFs), MOF-808-OH and MOF-808-NH2, were synthesized by partially replacing the 1,3,5-benzenetricarboxylate building block with 5-hydroxyisophthalate and 5-aminoisophthalate, respectively. The structural features of the defective materials were analyzed by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), nitrogen physisorption at 77 K, and thermogravimetric analysis (TGA). Importantly, the number of defect sites determined via proton nuclear magnetic resonance (1H-NMR) analysis of the digested materials was approximately 7 mol% for MOF-808-OH and 3 mol% for MOF-808-NH2. The presence of the defect sites increased the number of acidic centers on Zr-clusters originating from missing-linker nodes which accounted for a remarkable adsorption capacity towards various anionic organic dyes and chromium (vi) species. Compared to standard MOF-808, the defect-engineered ones showed significant increments by 30-60% in trapping capacity for anionic contaminants including sunset yellow, quinoline yellow, methyl orange, and potassium dichromate, while they exhibited modest improvements by 5-15% in the removal of cationic dyes, namely malachite green and methylene blue

AUTOMATIC DETECTION OF ACCESS POINT LOCATION USING MACHINE LEARNING AND PRIOR MODEL CALIBRATION

Knowledge of the correct location of an access point (AP) is of vital importance within a wireless ecosystem. Techniques are presented herein that support a new AP location identification method that uses prior model calibrations and machine learning (ML) techniques to detect an AP’s location using either received signal strength indicator (RSSI) data or fine time measurement (FTM) protocol data. Among other things, the new method is faster and more accurate than conventional trilateration methods

Model Reduction for Vehicle Systems Modelling

The full model of a double-wishbone suspension has more than 30 differential-algebraic equations which takes a remarkably long time to simulate. By contrast, the look-up table for the same suspension is simulated much faster, but may not be very accurate. Therefore, developing reduced models that approximate complex systems is necessary because model reduction decreases the simulation time in comparison with the original model, enables real time applications, and produces acceptable accuracy. In this research, we focus on model reduction techniques for vehicle systems such as suspensions and how they are approximated by models having lower degrees of freedom. First, some existing model reduction techniques, such as irreducible realization procedures, balanced truncation, and activity-based reduction, are implemented to some vehicle suspensions. Based on the application of these techniques, their disadvantages are revealed. Then, two methods of model reduction for multi-body systems are proposed. The first proposed method is 2-norm power-based model reduction (2NPR) that combines 2-norm of power and genetic algorithms to derive reduced models having lower degrees of freedom and fewer number of components. In the 2NPR, some components such as mass, damper, and spring are removed from the original system. Afterward, the values of the remaining components are adjusted by the genetic algorithms. The most important advantage of the 2NPR is keeping the topology of multi-body systems which is useful for design purposes. The second method uses proper orthogonal decomposition. First, the equations of motion for a multi-body system are converted to explicit second-order differential equations. Second, the projection matrix is obtained from simulation or experimental data by proper orthogonal decomposition. Finally, the equations of motion are transferred to a lower-dimensional state coordinate system. The implementation of the 2NPR to two double-wishbone suspensions and the comparison with other techniques such as balanced truncation and activity-based model reduction also demonstrate the efficiency of the new reduction technique

Integrating Systems Health Management with Adaptive Controls for a Utility-Scale Wind Turbine

Increasing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. Systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage. Advanced adaptive controls can provide the mechanism to enable optimized operations that also provide the enabling technology for Systems Health Management goals. The work reported herein explores the integration of condition monitoring of wind turbine blades with contingency management and adaptive controls. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine