Development of Microactuators Based on the Magnetic Shape Memory Effect

The giant magneto-strain effect in Ni-Mn-Ga alloys is particularly attractive for actuator applications. Two different approaches are being pursued to develop MSM microactuators. To observe large deflections of Ni-Mn-Ga microactuators, the material should be exhibiting low twinning stress and large magnetic anisotropy. In addition, design rules and boundary conditions for operating the Ni-Mn-Ga actuator material are having significant importance for evolution of performance characteristics

THROUGHPUT OPTIMIZATION AND RESOURCE ALLOCATION ON GPUS UNDER MULTI-APPLICATION EXECUTION

Platform heterogeneity prevails as a solution to the throughput and computational chal- lenges imposed by parallel applications and technology scaling. Specifically, Graphics Processing Units (GPUs) are based on the Single Instruction Multiple Thread (SIMT) paradigm and they can offer tremendous speed-up for parallel applications. However, GPUs were designed to execute a single application at a time. In case of simultaneous multi-application execution, due to the GPUs’ massive multi-threading paradigm, ap- plications compete against each other using destructively the shared resources (caches and memory controllers) resulting in significant throughput degradation. In this thesis, a methodology for minimizing interference in shared resources and provide efficient con- current execution of multiple applications on GPUs is presented. Particularly, the pro- posed methodology (i) performs application classification; (ii) analyzes the per-class in- terference; (iii) finds the best matching between classes; and (iv) employs an efficient re- source allocation. Experimental results showed that the proposed approach increases the throughput of the system for two concurrent applications by an average of 36% compared to other optimization techniques, while for three concurrent applications the proposed approach achieved an average gain of 23%

Integrated approach to detect spam in social media networks using hybrid features

Online social networking sites are becoming more popular amongst Internet users. The Internet users spend some amount of time on popular social networking sites like Facebook, Twitter and LinkedIn etc. Online social networks are considered to be much useful tool to the society used by Internet lovers to communicate and transmit information. These social networking platforms are useful to share information, opinions and ideas, make new friends, and create new friend groups. Social networking sites provide large amount of technical information to the users. This large amount of information in social networking sites attracts cyber criminals to misuse these sites information. These users create their own accounts and spread vulnerable information to the genuine users. This information may be advertising some product, send some malicious links etc to disturb the natural users on social sites. Spammer detection is a major problem now days in social networking sites. Previous spam detection techniques use different set of features to classify spam and non spam users. In this paper we proposed a hybrid approach which uses content based and user based features for identification of spam on Twitter network. In this hybrid approach we used decision tree induction algorithm and Bayesian network algorithm to construct a classification model. We have analysed the proposed technique on twitter dataset. Our analysis shows that our proposed methodology is better than some other existing techniques

Carotid intima media thickness and low high-density lipoprotein (HDL) in South Asian immigrants: could dysfunctional HDL be the missing link?

IntroductionSouth Asian immigrants (SAIs) in the US exhibit higher prevalence of coronary artery disease (CAD) and its risk factors compared with other ethnic populations. Conventional CAD risk factors do not explain the excess CAD risk; therefore there is a need to identify other markers that can predict future risk of CAD in high-risk SAIs. The objective of the current study is to assess the presence of sub-clinical CAD using common carotid artery intima-media thickness (CCA-IMT), and its association with metabolic syndrome (MS) and pro-inflammatory/dysfunctional HDL (Dys-HDL).Material and methodsA community-based study was conducted on 130 first generation SAIs aged 35-65 years. Dys-HDL was determined using the HDL inflammatory index. Analysis was completed using logistic regression and Fisher's exact test.ResultsSub-clinical CAD using CCA-IMT ≥ 0.8 mm (as a surrogate marker) was seen in 31.46%. Age and gender adjusted CCA-IMT was significantly associated with type 2 diabetes (p = 0.008), hypertension (p = 0.012), high-sensitivity C-reactive protein (p < 0.001) and homocysteine (p = 0.051). Both the presence of MS and Dys-HDL was significantly correlated with CCA-IMT, even after age and gender adjustment. The odds of having Dys-HDL with CCA-IMT were 5 times (95% CI: 1.68, 10.78).ConclusionsThere is a need to explore and understand non-traditional CAD risk factors with a special focus on Dys-HDL, knowing that SAIs have low HDL levels. This information will not only help to stratify high-risk asymptomatic SAI groups, but will also be useful from a disease management point of view

A formal synthesis of reserpine: hydrindane approach to the woodward's ring-E precursor

A new synthetic approach to a functionally and stereochemically embellished cyclohexanoid, corresponding to the Woodward's ring-E intermediate 24 of the complex indole alkaloid reserpine 1 is delineated. Our scheme emanates from a readily available endo-tricyclo[5.2.1.02,6]decane system from which cis-hydrindane and cyclohexanoid moieties are sequentially extracted. The strategy outlined here exploits the propensity of the endo-tricyclo[5.2.1.02,6]decane and cis-hydrindane systems to react from the convex face to generate the requisite stereochemical pattern. Since 24 has been previously elaborated to the natural product, the present effort constitutes a formal synthesis of rac-reserpine

Reserpine synthesis: a protocol for the stereoselective construction of the densely functionalized ring-E

A new approach to densely functionalized cyclohexanoid derivative 12, embodying the complete stereochemical pattern of ring-E of the complex indole alkaloid reserpine 1, from a readily available tricyclo[5.2.1.02,6]decane precursor 5 is described

A simple entry into enantiopure hydrindanes, hydroisoquinolones and diquinanes from 3,10-dioxygenated dicyclopentadienes: application to the synthesis of (+)-coronafacic acid and a formal synthesis of (+)-coriolin

A ready access to enantiopure 3,10-dioxygenated tricyclo[5.2.1.02,6]decane derivatives is reported. An efficient enzymatic kinetic resolution is employed through transesterification in the presence of lipase PS immobilized on Celite. Absolute configuration of the tricyclo[5.2.1.02,6]decan-10-one derivatives has been secured through correlation with (1R,2S)-1-aminoindan-2-ol. The promising utility of these enantiopure tricyclo[5.2.1.02,6]decane derivatives in synthesis has been demonstrated through the preparation of several optically pure cis-hydrindanes 15-18, employing the Haller-Bauer reaction as the key step for unbridging the trinorbornyl system. The cis-hydrindane (-)-16 has been further elaborated to the natural product (+)-coronafacic acid (+)-24. In an interesting sequence, cis-hydrindanone (+)-18 has been transformed into cis-hydroisoquinolones (+)-30 and (+)-33 via photorearrangement of the derived oxaziridines 29 and 32, respectively. The hydroisoquinolones (+)-30 and (+)-33 can serve as useful enantiopure building blocks for the synthesis of complex indole alkaloids. Oxidative cleavage of the trinorbornene double bond in the tricyclo[5.2.1.02,6]decan-10-one derivative (-)-37 and functional-group adjustments leads to the optically pure diquinane (+)-38, an advanced intermediate in the total synthesis of (+)-coriolin (+)-34

Mining Contestant From Large Unformed Datasets

We imagine the request. However, there is no doubt that the end-user star level message does not appear on many audit webpage’s. Therefore, the way to remedy indirect criticisms and investigations in interesting systems is a key issue in web expert systems and computer-assisted training. According to the clients' inclination, we add rounds of tenderness to two favorite things, so their recipe is in shape, and they will focus on a steady level. Emotional scanning is an important and ultimately important drive to lower a user's true personal tastes. In order to gain one's own reputation, kindness in auditing is very important. In general, if the nature of the element takes into account the behavioral concept, the design may be in the midst of a more advanced ability for a wide range of criteria. Within our application, we take advantage of the concept of organized theft to explain the arrangement. We got out of the fun-loving columns. Then, we have romantic ideas, which I advanced to tell the businessman. However, interesting web content does not provide classified intelligence all the time, and everyone's technology doesn't advance in unnecessary customer breaks. Expert to discover two strange and suggestive grandparents. By analyzing buyer ratings, they can place individual experts at the forefront of the victim's happiness for an appropriate pair of people. We mainly want ads with ingredients name and some character / goods / service features. LDA is actually a Gaussian group that is applied to assess, to cover topics and differences. We organize a number of experiments to determine opera in our degree according to buyer's bias. We combine presentation in our system with all live data for Yelp dataset

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop