Nitrogen addition to iron powder by mechanical alloying

Nitrogen was alloyed into iron (a) by mechanical processing in a nitrogen gas environment, and (b) by mechanically alloying with iron-nitride powders to characterize resulting nano-structure and nitrogen distribution. Although the infused nitrogen concentration was significantly greater than the thermodynamic equilibrium solubility of iron, no nitrides formed, even for nitrogen concentrations as high as 4.1 wt.% However, a bctFe phase did form. Lattice expansion calculations indicate that the sum of the interstitial bcc-Fe and bctFe nitrogen concentrations was significantly less than the total measured nitrogen concentration. A considerable portion of the mechanically infused nitrogen was determined to be associated with nanograin boundaries.     

The heroic cancer patient: A critical analysis of the relationship between illusion and mental health.

Examines, in the context of cancer illness, S. E. Taylor and J. D. Brown's (see record 1988-16903-001) claim that mental health is to a degree predicated on illusory beliefs about the self, one's control, and the future. The Heroic Model (HM), a stance toward cancer characterized by belief that positive thoughts and feelings, a fighting spirit, and an optimistic outlook will promote cancer remission, is presented as paradigmatic of Taylor and Brown's thesis. Strong and weak formulations of the HM (the former affirming a direct influence of mental states over physical processes, and the latter affirming indirect effects only) are examined with respect to their potential adaptive value as true or false beliefs, with different consequences for well-being. (French abstract) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Simulated Microgravity Inhibits the Proliferation of Chang Liver Cells by Attenuation of the Major Cell Cycle Regulators and Cytoskeletal Proteins

Simulated microgravity (SMG) induced the changes in cell proliferation and cytoskeleton organization, which plays an important factor in various cellular processes. The inhibition in cell cycle progression has been considered to be one of the main causes of proliferation inhibition in cells under SMG, but their mechanisms are still not fully understood. This study aimed to evaluate the effects of SMG on the proliferative ability and cytoskeleton changes of Chang Liver Cells (CCL-13). CCL-13 cells were induced SMG by 3D clinostat for 72 h, while the control group were treated in normal gravity at the same time. The results showed that SMG reduced CCL-13 cell proliferation by an increase in the number of CCL-13 cells in G0/G1 phase. This cell cycle phase arrest of CCL-13 cells was due to a downregulation of cell cycle-related proteins, such as cyclin A1 and A2, cyclin D1, and cyclin-dependent kinase 6 (Cdk6). SMG-exposed CCL-13 cells also exhibited a downregulation of α-tubulin 3 and β-actin which induced the cytoskeleton reorganization. These results suggested that the inhibited proliferation of SMG-exposed CCL-13 cells could be associate with the attenuation of major cell cycle regulators and main cytoskeletal proteins.     

Sequencing of multi‑robot behaviors using reinforcement learning

Given a collection of parameterized multi-robot controllers associated with individual behaviors designed for particular tasks, this paper considers the problem of how to sequence and instantiate the behaviors for the purpose of completing a more complex, overarching mission. In addition, uncertainties about the environment or even the mission specifications may require the robots to learn, in a cooperative manner, how best to sequence the behaviors. In this paper, we approach this problem by using reinforcement learning to approximate the solution to the computationally intractable sequencing problem, combined with an online gradient descent approach to selecting the individual behavior parameters, while the transitions among behaviors are triggered automatically when the behaviors have reached a desired performance level relative to a task performance cost. To illustrate the effectiveness of the proposed method, it is implemented on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation.     

Optimization of sodium sulfide treatment of rice straw to increase the enzymatic hydrolysis in bioethanol production

The efficiency of sodium sulfide-assisted alkaline pulping for cellulose preparation from Oryza sativa L. rice straw in Vietnam for enzymatic saccharification was investigated. The response surface methodology was used for the determination of optimal technological parameters of alkaline pulping such as active alkali dosage, temperature and time. The optimal technological parameters were established to be active alkali dosage of 7%, treatment temperature of 100 °C and treatment time of 120 min. At these regimes, a maximal sugar yield of 51.8% (over dry rice straw) was obtained. It meant that the saccharification efficiency up to 97.1% could be achieved by using sodium sulfide-assisted alkaline pretreatment method. Addition of sodium sulfide into alkaline pretreatment resulted in higher sugar yield, higher level of depolymerization of lignin and less loss of cellulose. Moreover, liquid hydrolyzate after enzymatic hydrolysis was analyzed by HPLC to determine the compositions of sugar mixture. The fiber morphology in pretreated biomass solid was also revealed by SEM.     

Protein topology of presenilin 1

Mutations in a gene encoding a multitransmembrane protein, termed presenilin 1 (PS1), are causative in the majority of early-onset cases of AD. To determine the topology of PS1, we utilized two strategies: first, we tested whether putative transmembranes are sufficient to export a protease-sensitive substrate across a lipid bilayer; and second, we examined the binding of antibodies to specific PS1 epitopes in cultured cells selectively permeabilized with the pore-forming toxin, streptolysin-O. We document that the "loop," N-terminal, and C-terminal domains of PS1 are oriented toward the cytoplasm.     

A decision tree using ID3 algorithm for English semantic analysis

Natural language processing has been studied for many years, and it has been applied to many researches and commercial applications. A new model is proposed in this paper, and is used in the English document-level emotional classification. In this survey, we proposed a new model by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents. The semantic classification of our model is based on many rules which are generated by applying the ID3 algorithm to 115,000 English sentences of our English training data set. We test our new model on the English testing data set including 25,000 English documents, and achieve 63.6% accuracy of sentiment classification results.     

Comparison of methods for selecting personal protective equipment for arc flash hazards

In 1960, a large global chemical company began documenting and implementing requirements for preventing injuries from electric arc flash hazards, including appropriate personal protective equipment (PPE). Methods evolved from requiring a 100% natural fiber long-sleeve shirt for a specific task to a detailed analysis with multiple flame-resistant clothing options. This paper compares the PPE required by these methods, based on hazards analysis in more than 65 manufacturing and laboratory facilities. The total costs for the example company are estimated for each method.     

Estimating mechanical properties of clear wood from ten‐year‐old Melia azedarach trees using the stress wave method

The objective of this study was to examine the potential of stress wave velocity (SWV) as a rapid and non-destructive method to estimate the mechanical properties of Melia azedarach wood. The SWV, dynamic modulus of elasticity (MOE_d), modulus of elasticity (MOE), modulus of rupture (MOR, bending strength) and density were determined on ninety 20 ? 20 ? 320 mm clear wood specimens, obtained from stems of three ten-year-old M. azedarach trees, and tested at environmental equilibrium in 20°C, 60?% relative humidity (a moisture content of approximately 12?%). There was a statistically significant (0.1?% level) but weak correlation (R²?=?0.23) between the SWV and MOE, but no statistically significant correlation was found between the SWV and MOR. Much better results for prediction of static properties of M. azedarach wood were obtained when SWV and wood density (WD) were used together through calculation of MOE_d in the air-dry condition (MOE: R²?=?0.76, MOR: R²?=?0.47), although in the case of MOR a model based on WD alone is slightly better (R²?=?0.58), and WD is also almost as good as MOE_d for predicting MOE. It is concluded that SWV coupled with WD can be employed as a predicting parameter to evaluate the mechanical properties of M. azedarach wood during the manufacturing process, although WD alone is also effective. The SWV alone would not be useful due to MOE being almost directly proportional to WD at this moisture content.

     

Learning to Match the Schemas of Data Sources: A Multistrategy Approach

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are element location maps to address and price maps to listed-price. We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy.