Artificial neural network based prediction of heat transfer in a vertical thermosiphon reboiler

Paper presented at the 6th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, South Africa, 30 June - 2 July, 2008.The present study deals with the prediction of heat transfer coefficients for water and benzene using ANN in a vertical thermosiphon reboiler. The experimental data from the literature were used for training of feed forward artificial neural network with error back propagation technique. Different training algorithms have been applied with different hidden layers and nodes to train the network. It was observed that the heat transfer coefficients predicted was close to the experimental data within the maximum error of ± 20 %. If more exhaustive input data were fed then error would have become still lesser. It has been observed that some algorithms are very efficient with respect to training time in comparison to other algorithms.vk201

Private information via the Unruh effect

In a relativistic theory of quantum information, the possible presence of horizons is a complicating feature placing restrictions on the transmission and retrieval of information. We consider two inertial participants communicating via a noiseless qubit channel in the presence of a uniformly accelerated eavesdropper. Owing to the Unruh effect, the eavesdropper's view of any encoded information is noisy, a feature the two inertial participants can exploit to achieve perfectly secure quantum communication. We show that the associated private quantum capacity is equal to the entanglement-assisted quantum capacity for the channel to the eavesdropper's environment, which we evaluate for all accelerations.Comment: 5 pages. v2: footnote deleted and typos corrected. v3: major revision. New capacity (single-letter!) theorem and implicit assumption lifte

Parallel Cellular Automata-based Simulation of Laser Dynamics using Dynamic Load Balancing

We present an analysis of the feasibility of executing a parallel bioinspired model of laser dynamics, based on cellular automata (CA), on the usual target platform of this kind of applications: a heterogeneous non-dedicated cluster. As this model employs a synchronous CA, using the single program, multiple data (SPMD) paradigm, it is not clear in advance if an appropriate efficiency can be obtained on this kind of platform. We have evaluated its performance including artificial load to simulate other tasks or jobs submitted by other users. A dynamic load balancing strategy with two main differences from most previous implementations of CA based models has been used. First, it is possible to migrate load to cluster nodes initially not belonging to the pool. Second, a modular approach is taken in which the model is executed on top of a dynamic load balancing tool – the Dynamite system – gaining flexibility. Very satisfactory results have been obtained, with performance increases from 60% to 80%.Ministerio de Ciencia e Innovación TIN2007-68083-C02Junta de Extremadura PRI06A22

Quantum Communication in Rindler Spacetime

A state that an inertial observer in Minkowski space perceives to be the vacuum will appear to an accelerating observer to be a thermal bath of radiation. We study the impact of this Davies-Fulling-Unruh noise on communication, particularly quantum communication from an inertial sender to an accelerating observer and private communication between two inertial observers in the presence of an accelerating eavesdropper. In both cases, we establish compact, tractable formulas for the associated communication capacities assuming encodings that allow a single excitation in one of a fixed number of modes per use of the communications channel. Our contributions include a rigorous presentation of the general theory of the private quantum capacity as well as a detailed analysis of the structure of these channels, including their group-theoretic properties and a proof that they are conjugate degradable. Connections between the Unruh channel and optical amplifiers are also discussed.Comment: v3: 44 pages, accepted in Communications in Mathematical Physic

Evaluating the performance of artificial intelligence software for lung nodule detection on chest radiographs in a retrospective real-world UK population

Objectives Early identification of lung cancer on chest radiographs improves patient outcomes. Artificial intelligence (AI) tools may increase diagnostic accuracy and streamline this pathway. This study evaluated the performance of commercially available AI-based software trained to identify cancerous lung nodules on chest radiographs. Design This retrospective study included primary care chest radiographs acquired in a UK centre. The software evaluated each radiograph independently and outputs were compared with two reference standards: (1) the radiologist report and (2) the diagnosis of cancer by multidisciplinary team decision. Failure analysis was performed by interrogating the software marker locations on radiographs. Participants 5722 consecutive chest radiographs were included from 5592 patients (median age 59 years, 53.8% women, 1.6% prevalence of cancer). Results Compared with radiologist reports for nodule detection, the software demonstrated sensitivity 54.5% (95% CI 44.2% to 64.4%), specificity 83.2% (82.2% to 84.1%), positive predictive value (PPV) 5.5% (4.6% to 6.6%) and negative predictive value (NPV) 99.0% (98.8% to 99.2%). Compared with cancer diagnosis, the software demonstrated sensitivity 60.9% (50.1% to 70.9%), specificity 83.3% (82.3% to 84.2%), PPV 5.6% (4.8% to 6.6%) and NPV 99.2% (99.0% to 99.4%). Normal or variant anatomy was misidentified as an abnormality in 69.9% of the 943 false positive cases. Conclusions The software demonstrated considerable underperformance in this real-world patient cohort. Failure analysis suggested a lack of generalisability in the training and testing datasets as a potential factor. The low PPV carries the risk of over-investigation and limits the translation of the software to clinical practice. Our findings highlight the importance of training and testing software in representative datasets, with broader implications for the implementation of AI tools in imaging

Measurement of CP asymmetries and branching fraction ratios of B− decays to two charm mesons

The $CP$ asymmetries of seven $B^-$ decays to two charm mesons are measured using data corresponding to an integrated luminosity of $9\text{fb}^{-1}$ of proton-proton collisions collected by the LHCb experiment. Decays involving a $D^{*0}$ or $D^{*-}_s$ meson are analysed by reconstructing only the $D^0$ or $D^-_s$ decay products. This paper presents the first measurement of $\mathcal{A}^{CP}(B^- \rightarrow D^{*-}_s D^0)$ and $\mathcal{A}^{CP}(B^- \rightarrow D^{-}_s D^{*0})$, and the most precise measurement of the other five $CP$ asymmetries. There is no evidence of $CP$ violation in any of the analysed decays. Additionally, two ratios between branching fractions of selected decays are measured.The CP asymmetries of seven B$^{−}$ decays to two charm mesons are measured using data corresponding to an integrated luminosity of 9 fb$^{−1}$ of proton-proton collisions collected by the LHCb experiment. Decays involving a D$^{*0}$ or ${D}_s^{\ast -}$ meson are analysed by reconstructing only the D$^{0}$ or ${D}_s^{-}$ decay products. This paper presents the first measurement of $\mathcal{A} ^{CP}$(B$^{−}$→${D}_s^{\ast -}$D$^{0}$) and $\mathcal{A} ^{CP}$(B$^{−}$→${D}_s^{-}$D$^{∗0}$), and the most precise measurement of the other five CP asymmetries. There is no evidence of CP violation in any of the analysed decays. Additionally, two ratios between branching fractions of selected decays are measured.[graphic not available: see fulltext]The $CP$ asymmetries of seven $B^-$ decays to two charm mesons are measured using data corresponding to an integrated luminosity of $9\text{ fb}^{-1}$ of proton-proton collisions collected by the LHCb experiment. Decays involving a $D^{*0}$ or $D^{*-}_s$ meson are analysed by reconstructing only the $D^0$ or $D^-_s$ decay products. This paper presents the first measurement of $\mathcal{A}^{CP}(B^- \rightarrow D^{*-}_s D^0)$ and $\mathcal{A}^{CP}(B^- \rightarrow D^{-}_s D^{*0})$, and the most precise measurement of the other five $CP$ asymmetries. There is no evidence of $CP$ violation in any of the analysed decays. Additionally, two ratios between branching fractions of selected decays are measured

Neural network analysis of boiling heat transfer in pool boiling of single component liquids

Paper presented at the 7th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Turkey, 19-21 July, 2010.The objective of this study is to use Artificial Neural Network for boiling heat transfer at various operating conditions using the experimental data for different liquids. For training the networks, the standard feed forward back propagation algorithm was used and several types of structures were tested to obtain the most suitable network for the prediction of boiling curves. In this study four network structures were used with the variation of neurons and hidden layers. The suitability of the network depends upon the type of system and data chosen for training. It was observed that the predicted results were close to the actual experimental data for all liquids. The predictability of the network is extremely good if the training data are chosen appropriately. When all the data of the system were considered together for the training of the network, the performance was extremely good. The prediction of ANN results was very close to the actual experimental values with a mean absolute relative error less than 2.0 %.ej201

Pendidikan Anti Korupsi Pendekatan Budaya, Politik, dan Teori Integritas

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