Remote preconditioning by aortic constriction: affords cardioprotection as classical or other remote ischemic preconditioning? Role of iNOS

Dose remote preconditioning by aortic constriction (RPAC) affords cardioprotection similar to classical or other remote ischemic preconditioning stimulus? Moreover study was also designed to investigate role of inducible nitric oxide synthase in remote preconditioning by aortic constriction. There are sufficient evidences that "ischemic preconditioning" has surgical applications and afford clinically relevant cardioprotection. Transient occlusion of circumflex artery, renal artery, limb artery or mesenteric artery preconditions the myocardium against ischemia reperfusion injury in case of ischemic heart disease leading to myocardial infraction. Here abdominal aorta was selected to produce RPAC. Four episodes of Ischemia-reperfusion of 5 min each to abdominal aorta produced RPAC by assessment of infract size, LDH and CK. These studies suggest RPAC produced acute (FWOP) and delayed (SWOP) cardioprotective effect. RPAC demonstrated a significant decrease in Ischemia-reperfusion induced release of LDH, CK and extent of myocardial infract size. L-NAME (10 mg/Kg i.v.), Aminoguanidine (150 mg/Kg s.c.), Aminoguanidine (300 mg/Kg s.c.), S-methyl isothiourea (3 mg/Kg i.v.), 1400W (1 mg/Kg i.v.) administered 10 min. before global ischemia reperfusion produced no marked effect. Aminoguanidine (150 mg/Kg s.c.), Aminoguanidine (300 mg/Kg s.c.), S-methyl isothiourea (3 mg/Kg i.v.), 1400W (1 mg/Kg i.v.) pretreatment after RPAC produced no significant effect on acute RPAC induced decrease in LDH, CK and infract size, whereas L-NAME (10 mg/Kg i.v.) increased RPAC induced decrease in LDH, CK and infract size. Most interesting observation is in delayed RPAC, where all NOS inhibitors pretreatment attenuate RPAC induced decrease in LDH, CK and infract size. In conclusions, "Remote preconditioning by aortic constriction" (RPAC) affords cardioprotection similar to classical or other remote ischemic preconditioning stimulus. Moreover, late or delayed phase of RPAC has been mediated by inducible nitric oxide synthase (iNOS) whereas it has not involved in acute RPAC

Loan pricing model : design and implementation

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaf 40).by Ashish Sharma.M.Eng

Dynamic Code Checksum Generator

A checksum (i.e., a cryptographic hash) of a file can be used as an integrity check, if an attacker tries to change the code in an executable file, a checksum can be used to detect the tampering. While it is easy to compute a checksum for any static file, it is possible for an attacker to tamper with an executable file as it is being loaded into memory, or after it has been loaded. Therefore, it would be more useful to checksum an executable file dynamically only after the file has been loaded into memory. However, checksumming dynamic code is much more challenging than dealing with static code – the code can be loaded into different locations in memory, and parts of the code will change depending on where the code resides in memory (addresses, labels, etc.).Windows Vista and later versions of Windows include a new technology known as Address Space Layout Randomization (ASLR). ASLR, which serves as a defense against buffer overflow attacks, causes the executable file to be loaded at a randomly-selected location in memory. The goal of this project is to develop a robust and efficient technique for computing the cryptographic hash of a dynamic executable in the presence of ASLR

Techniques and algorithms for immersive and interactive visualization of large datasets

Advances in computing power have made it possible for scientists to perform atomistic simulations of material systems that range in size, from a few hundred thousand atoms to one billion atoms. An immersive and interactive walkthrough of such datasets is an ideal method for exploring and understanding the complex material processes in these simulations. However rendering such large datasets at interactive frame rates is a major challenge. A scalable visualization platform is developed that is scalable and allows interactive exploration in an immersive, virtual environment. The system uses an octree based data management system that forms the core of the application. This reduces the amount of data sent to the pipeline without a per-atom analysis. Secondary algorithms and techniques such as modified occlusion culling, multiresolution rendering and distributed computing are employed to further speed up the rendering process. The resulting system is highly scalable and is capable of visualizing large molecular systems at interactive frame rates on dual processor SGI Onyx2 with an InfinteReality2 graphics pipeline

Energy harvesting using photovoltaic and betavoltaic devices

There is an important need for improvement in both cost and efficiency of photovoltaic cells. For improved efficiency, a better understanding of solar cell performance is required. An analytical model of thin-film silicon solar cell, which can provide an intuitive understanding of the effect of illumination on its charge carriers and electric current, is proposed. The separate cases of homogeneous and inhomogeneous charge carrier generation rates across the device are investigated. This model also provides for the study of the charge carrier transport within the quasi-neutral and depletion regions of the device, which is of an importance for thin-film solar cells. Two boundary conditions, one based on a fixed charge carrier surface recombination velocities at the electrodes and another based on intrinsic conditions for large size devices are explored. The device\u27s short circuit current and open circuit voltage are found to increase with a decrease of surface recombination velocity at the electrodes. The power conversion efficiency of thin film solar cells is observed to depend strongly on impurity doping concentrations. The developed analytical model can be used to optimize the design and performance of thin-film solar cells without involving highly complicated numerical codes to solve the corresponding drift-diffusion equations. The third generation polymer photovoltaic solar cells, the first generation includes monocrystalline silicon solar cells and second generation being thin-film solar cells, and photodetectors are researched widely in the last few years due to their low device processing cost, mechanical flexibility, and lightweight. Organic photovoltaic materials such as poly(3-hexylthiophene):[6,6]-phenyl-C61-butyric acid methyl ester (P3HT: PCBM) blend are usually cheaper than inorganic materials, but have a limitation of lower power conversion efficiency (PCE) than their inorganic (for example, Si) counterparts. These organic devices need to be optimized to achieve the maximum possible PCE. One way to do this is to achieve the optimal thickness of the optically active layer of P3HT:PCBM while fabricating these organic photovoltaic devices. The influence of the active layer\u27s thickness of P3HT:PCBM blend on performance of polymer solar cells and photodetectors are experimentally investigated. The fabricated device structure is glass/ITO/PEDOT:PSS/P3HT:PCBM/A1, where ITO is the indium tin oxide, and PEDOT:PSS stands for poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) used as a buffer layer to collect holes effectively at the ITO anode. Aluminum is used as a cathode. Chlorobenzene is used as a solvent to prepare the polymer-fullerene blend. Spin coating technique was utilized to deposit the active layer and the concentration of P3HT, PCBM, and spin-coating speeds were varied to achieve a wide range of the active layer\u27s thicknesses from 20 mn to 345 mn. The PCE of solar cell devices and the external quantum efficiency ( EQE) of the photodetectors are found to increase with the thickness of the active layer. The maximum PCE of 1.09% is obtained for the active layer\u27s thickness of 345 mn. The ongoing advanced space exploration requires the novel energy sources that can generate power for extreme duration without need of refill. The need for such extreme-duration lightweight power sources for space and terrestrial applications motivates the study and development of polymer-based betavoltaic devices. The betavoltaic devices based on the semiconductive polymer-fullerene blend of P3HT:ICBA, where ICBA is indene-C60 bisadduct, are demonstrated here for the first time. Both direct and indirect energy conversion methods were explored. For the indirect conversion method, a scintillator intermediate layer of cerium-doped yttrium aluminum garnet (Ce:YAG) was used. A high open circuit voltage of 0.56 V has been achieved in the betavoltaic device fabricated on polyethylene terephthalate (PET) substrate with the indirect energy conversion method at 30 keV electron kinetic energy. The directional and external interaction losses are significantly reduced using thin PET substrates. The maximum output electrical power of 62 nW was achieved at 30 keV input electron beam energy. The highest betavoltaic PCE of 0.78% was achieved at 10 keV of electron beam energy. The performance of two different scintillators, Ce:YAG and Thallium doped Cesium Iodide (CsI:TI), were compared in the indirect conversion betavoltaic devices experimentally and the interaction of electron beam with Ce:YAG and CsI:TI was studied using Monte Carlo simulations. The catholuminescence profiles from simulation showed that CsI:TI is more-efficient to generate photons when hit by electron beam compared to Ce:YAG, which is further verified experimentally with 20% PCE enhancement using CsI:TI at 30 kV e-beam compared to betavoltaic devices with Ce:YAG. The directional loss in the indirect conversion devices is further reduced by applying thin reflecting aluminum film on top of the scintillator. The PCE increased by 26.7% with 30 nm thin aluminum film on top of Ce:YAG scintillator at 30 keV electron beam energy. The experimental results showed that the output electrical power from betavoltaic devices increased with the increase in incident electron beam energy

Identification of Unknown Landscape Types Using CNN Transfer Learning

Unknown image type identification is the problem of identifying unknown types of images from the set of already provided images that are considered to be known, where the known and unknown sets represent different content types. Solving this problem has a lot of security applications such as suspicious object detection during baggage scanning at airport customs, border protection via remote sensing, cancer detection, weather and disaster monitoring, etc. In this thesis, we focus on identification of unknown landscape images. This application has a huge relevance to the context of a smart nation where it can be applied to major national security tasks such as monitoring the borders or the detection of unknown and potentially dangerous landscapes in critical locations. We propose effective semi-supervised novelty detection approaches for the unknown image type identification problem using Convolutional Neural Network (CNN) Transfer Learning. Recently, the CNN Transfer Learning approach has been very successful in various visual recognition tasks especially in cases where large training data is not available. Our main idea is to use pre-trained CNNs (i.e. already trained on large datasets like ImageNet [10]) that are then used to train new models specifically applicable to the landscape image dataset. Features extracted from these domain-specific trained CNN are then used with standard semi-supervised novelty detection algorithms like Gaussian Mixture Model, Isolation Forest, One-class Support Vector Machines (SVM) and Bayesian Gaussian Mixture Models to identify the unknown landscape images. We provide two fine-tuning approaches: supervised and unsupervised. Supervised fine-tuning approach simply uses the the class categories (landscape classes, e.g. airport, stadium, etc.) of the known images dataset. The unsupervised fine tuning approach on the other hand learns the class categories from the known images using the unsupervised clustering-based algorithm. We conducted extensive experiments that prove the effectiveness of our approaches. Our best values of AUROC and average precision scores for the identification problem are 0.96 and 0.94, respectively. In particular, we statistically prove that both fine-tuning methods significantly increase the performance of the identification with respect to the non fine-tuned CNN, and unsupervised and supervised fine tuning approaches are comparable