Maximal power output of a stochastic thermodynamic engine

Classical thermodynamics aimed to quantify the efficiency of thermodynamic engines, by bounding the maximal amount of mechanical energy produced, compared to the amount of heat required. While this was accomplished early on, by Carnot and Clausius, the more practical problem to quantify limits of power that can be delivered, remained elusive due to the fact that quasistatic processes require infinitely slow cycling, resulting in a vanishing power output. Recent insights, drawn from stochastic models, appear to bridge the gap between theory and practice in that they lead to physically meaningful expressions for the dissipation cost in operating a thermodynamic engine over a finite time window. Indeed, the problem to optimize power can be expressed as a stochastic control problem. Building on this framework of stochastic thermodynamics we derive bounds on the maximal power that can be drawn by cycling an overdamped ensemble of particles via a time-varying potential while alternating contact with heat baths of different temperature (Tc cold, and Th hot). Specifically, assuming a suitable bound M on the spatial gradient of the controlling potential, we show that the maximal achievable power is bounded by [Formula presented]. Moreover, we show that this bound can be reached to within a factor of [Formula presented] by operating the cyclic thermodynamic process with a quadratic potential

Chip formation mechanism using finite element simulation

Prediction of chip form produced during machining process is an important work when considering workpiece surface creation and possible damage caused by chips generated during machining. The paper presents a set of new results of cutting chip formation from the latest FEM model development. Generally three types of chips, namely, continuous, serrated and discontinuous chips, are generated during metal machining. The formation of these three types of chips is investigated in relation to various influential factors, such as rake angles and depth of cuts. Progressive damage model with damage evolution criterion is employed into the FEM model to reduce mesh dependency. It has been demonstrated that finite element simulation is a good tool for evaluation of chip formation in relation to operational parameters, tool settings as well as material properties

Effect of different parameters on grinding efficiency and its monitoring by acoustic emission

Grinding efficiency is one of the most important considerations in the selection of grinding operation conditions because it has a significant impact on the productivity, quality, energy consumption and cost of production. Focusing on the core issues of grinding process, the paper presents some fundamental research findings in relation to grinding material removal mechanisms. The grinding efficiency is analysed by considering the rubbing, ploughing and cutting three stages of a single grit grinding process. By analysing the features of acoustic emission in single grit grinding tests, an evidence based scientific foundation has been established for monitoring grinding efficiency using acoustic emission. Accordingly, the energy consumption in the grinding is considered with the grit shape. Following the discussion of the models of temperature elevation and thermal stresses in grinding, the paper provides a logic depiction that explains why acoustic emission in grinding can be used for grinding thermal performance monitoring. As a result, the paper introduces a novel acoustic emission monitoring method that is capable to monitor grinding temperature and grinding wheel wear status

An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network

IEEE Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction. This paper proposes a method to construct classifiers from multi-resolution hierarchical granular representations (MRHGRC) using hyperbox fuzzy sets. The proposed approach forms a series of granular inferences hierarchically through many levels of abstraction. An attractive characteristic of our classifier is that it can maintain a high accuracy in comparison to other fuzzy min-max models at a low degree of granularity based on reusing the knowledge learned from lower levels of abstraction. In addition, our approach can reduce the data size significantly as well as handle the uncertainty and incompleteness associated with data in real-world applications. The construction process of the classifier consists of two phases. The first phase is to formulate the model at the greatest level of granularity, while the later stage aims to reduce the complexity of the constructed model and deduce it from data at higher abstraction levels. Experimental analyses conducted comprehensively on both synthetic and real datasets indicated the efficiency of our method in terms of training time and predictive performance in comparison to other types of fuzzy min-max neural networks and common machine learning algorithms

Analysis of grinding surface creation by single grit approach

This paper presents some new research findings in the investigation of single grit grinding in terms of surface creation. The investigation demonstrated that rubbing-ploughing-cutting hypothesis of grinding material removal mechanism is valid in both experiments and simulations. A finite element model (FEM) was developed to simulate the material deformation during the grit interacts with the workpiece. It was found that the cutting mechanism is the more effective in the first half of the scratch where the grit penetrates the workpiece. The ploughing is a prominent mechanism in the second half of the scratch where the grit is climbing up along the scratch path and uplifting the material at the front and the sides of it. This observation is very important to provide a greater insight into the difference between up-cut and down-cut grinding material removal mechanisms. Multi passes scratch simulations were performed to demonstrate the influence of ploughing on the ground surface formation. Moreover, by analysing the effects of grinding conditions, the shape of cutting edges and friction in grinding zone on the grinding surface formation, some useful relations between grinding performance and controllable parameters have been identified. It has demonstrated that ploughing has significant influences on ground surface formation and concluded that the influence of grit shape, friction and grinding kinetic condition should be considered together for the ploughing behaviour control, which could provide a good guidance for the improvement of grinding efficiency

Evolving an optimal decision template for combining classifiers.

In this paper, we aim to develop an effective combining algorithm for ensemble learning systems. The Decision Template method, one of the most popular combining algorithms for ensemble systems, does not perform well when working on certain datasets like those having imbalanced data. Moreover, point estimation by computing the average value on the outputs of base classifiers in the Decision Template method is sometimes not a good representation, especially for skewed datasets. Here we propose to search for an optimal decision template in the combining algorithm for a heterogeneous ensemble. To do this, we first generate the base classifier by training the pre-selected learning algorithms on the given training set. The meta-data of the training set is then generated via cross validation. Using the Artificial Bee Colony algorithm, we search for the optimal template that minimizes the empirical 0–1 loss function on the training set. The class label is assigned to the unlabeled sample based on the maximum of the similarity between the optimal decision template and the sample’s meta-data. Experiments conducted on the UCI datasets demonstrated the superiority of the proposed method over several benchmark algorithms

Confidence in prediction: an approach for dynamic weighted ensemble.

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this work, we propose a novel dynamic weighted ensemble method. Based on the observation that each classifier provides a different level of confidence in its prediction, we propose to encode the level of confidence of a classifier by associating with each classifier a credibility threshold, computed from the entire training set by minimizing the entropy loss function with the mini-batch gradient descent method. On each test sample, we measure the confidence of each classifier’s output and then compare it to the credibility threshold to determine whether a classifier should be attended in the aggregation. If the condition is satisfied, the confidence level and credibility threshold are used to compute the weight of contribution of the classifier in the aggregation. By this way, we are not only considering the presence but also the contribution of each classifier based on the confidence in its prediction on each test sample. The experiments conducted on a number of datasets show that the proposed method is better than some benchmark algorithms including a non-weighted ensemble method, two dynamic ensemble selection methods, and two Boosting methods

Study on antioxidant activity of Echinacea purpurea L. extracts and its impact on cell viability

This study investigates the antioxidant activity of Echinacea Purpurea L. (EP) extracts and its impact on cell viability. The polysaccharides content of EP was 159.8 ± 12.4 mg/g dry weight (DW), with extracts obtained by applying 55% ethanol at 55°C containing 11.0 ±1.0 mg gallic acid equivalent/g DW of total phenolic compound. Trolox equivalent antioxidant capacity, 0.1 mg/mL of EP extracts exhibited only 30% when compared to the ascorbic acid at the same concentration. Reducing power of extractsincreased linearly with its concentration and the concentration at 2.0 mg/mL reached about 65% of ascorbic acid at 0.3 mg/mL. The chelating capacity of ferrous iron (Fe2+) was 70% as good as that of thesynthetic metal chelater EDTA when added to 5.0 mg/mL of EP extracts. The DPPH scavenging capacity showed 85.1% at 0.5 mg/mL of extracts and with half-effective doses (ED50) was measured at 0.23mg/mL. The superoxide anions scavenging capacity of EP extracts was nearly equivalent to ascorbic acid (91.1% vs 93.0%) at the same concentration of 1.6 mg/mL and ED50 was 0.32 and 0.13 mg/mL, respectively. Microculture tetrazolium assays showed extracts had 92% cell viability at 1.6 mg/mL forchicken’s peripheral blood mononuclear cells (PBMCs) and 84% for RAW 264.7 macrophages, neither reaching the IC50 level. In summary, the EP extracts had antioxidant activity similar to that of ascorbic acid, but have no serious effect on inhibiting chicken’s PBMCs viability

Morphology Analysis and Characteristics Evaluation of Typical Super Abrasive Grits in Micron Scale

Distribution characterization of geometry shape and size of abrasive grits with high quality in tight size band and exact pattern is crucial for modern tool manufacturer to make fine powder abrasive tool and other powder tools, but complex to be classified and evaluated accurately due to the lack of scientific method. In contrast to industrial methods with sieving mesh size or simplified projection criteria with circumscribed (inscribed or escribed) circle or rectangle, a set of new systemic criteria is developed and validated by measuring three representative grits samples in micron scale under 2D/3D microscopy platform. The features of micron abrasive grits under morphology classification include total four groups, six subgroups and eighteen sub-types in consideration of spatial geometry and statistical size distribution. For grinding performance analysis and simulation, it would be better to use a set of dominant volumetric geometries rather than use single simple geometry. Furthermore, the significance of abrasive grits geometries in grinding performance is discussed

Surveillance for seasonal influenza virus prevalence in hospitalized children with lower respiratory tract infection in Guangzhou, China during the post-pandemic era.

Influenza A(H1N1)pdm09, A(H3N2) and B viruses have co-circulated in the human population since the swine-origin human H1N1 pandemic in 2009. While infections of these subtypes generally cause mild illnesses, lower respiratory tract infection (LRTI) occurs in a portion of children and required hospitalization. The aim of our study was to estimate the prevalence of these three subtypes and compare the clinical manifestations in hospitalized children with LRTI in Guangzhou, China during the post-pandemic period. METHODS: Children hospitalized with LRTI from January 2010 to December 2012 were tested for influenza A/B virus infection from their throat swab specimens using real-time PCR and the clinical features of the positive cases were analyzed. RESULTS: Of 3637 hospitalized children, 216 (5.9%) were identified as influenza A or B positive. Infection of influenza virus peaked around March in Guangzhou each year from 2010 to 2012, and there were distinct epidemics of each subtype. Influenza A(H3N2) infection was more frequently detected than A(H1N1)pdm09 and B, overall. The mean age of children with influenza A virus (H1N1/H3N2) infection was younger than those with influenza B (34.4 months/32.5 months versus 45 months old; p<0.005). Co-infections of influenza A/ B with mycoplasma pneumoniae were found in 44/216 (20.3%) children. CONCLUSIONS: This study contributes the understanding to the prevalence of seasonal influenza viruses in hospitalized children with LRTI in Guangzhou, China during the post pandemic period. High rate of mycoplasma pneumoniae co-infection with influenza viruses might contribute to severe disease in the hospitalized children.published_or_final_versio