Effect of Inoculum Age, Carbon and Nitrogen Sources on the Production of Lipase by Candida Cylindracea 2031 in Batch Fermentation

Production of extracellular lipase by Candida cylindracea DSMZ 2031  was studied in a seven liters batch bioreactor, using palm oil (PO), palmitic acid (PA), lauric acid (LA), olive oil (OO) and cooking oil (CO) as carbon source.   The effect of  carbon and nitrogen sources  were studied by measuring the lipase activity.  The maximum lipase activity was found to be 12.7 kLU on palm oil as carbon source, urea as nitrogen sources and at 36 h inoculum age. This was achieved at a temperature of 30o C, pH of 6.0, agitation speed of 500 rpm and aeration of 1vvm

Empirical Determination of Threshold Partial Wave Amplitudes in pp→ppωp p \to p p \omega

Using the model independent irreducible tensor approach to $\omega$ production in $pp$ collisions, we show theoretically that, it is advantageous to measure experimentally the polarization of $\omega$, in addition to the proposed experimental study employing a polarized beam and a polarized target.Comment: 6 pages, 1 Table, Latex-2

Production of Medium Chain Length Polyhydroxyalkanoates From Oleic Acid Using Pseudomonas Putida Pga1 by Fed Batch Culture

Bacterial polyhydroxyalkanoates (PHAs) are a class of polymers currently receiving much attention because of theirpotential as renewable and biodegradable plastics. A wide variety of bacteria has been reported to produce PHAsincluding Pseudomonas strains. These strains are known as versatile medium chain length PHAs (PHAs-mcl) producersusing fatty acids as carbon source. Oleic acid was used to produce PHAs-mcl using Pseudomonas putida PGA 1 bycontinuous feeding of both nitrogen and carbon source, in a fed batch culture. During cell growth, PHAs alsoaccumulated, indicating that PHA production in this organism is growth associated. Residual cell increased until thenitrogen source was depleted. At the end of fermentation, final cell concentration, PHA content, and productivity were30.2 g/L, 44.8 % of cell dry weight, and 0.188 g/l/h, respectively

Exploring Zeptosecond Quantum Equilibration Dynamics: From Deep-Inelastic to Fusion-Fission Outcomes in 58^{58}Ni+60^{60}Ni Reactions

Energy dissipative processes play a key role in how quantum many-body systems dynamically evolve towards equilibrium. In closed quantum systems, such processes are attributed to the transfer of energy from collective motion to single-particle degrees of freedom; however, the quantum many-body dynamics of this evolutionary process are poorly understood. To explore energy dissipative phenomena and equilibration dynamics in one such system, an experimental investigation of deep-inelastic and fusion-fission outcomes in the $^{58}$Ni+$^{60}$Ni reaction has been carried out. Experimental outcomes have been compared to theoretical predictions using Time Dependent Hartree Fock and Time Dependent Random Phase Approximation approaches, which respectively incorporate one-body energy dissipation and fluctuations. Excellent quantitative agreement has been found between experiment and calculations, indicating that microscopic models incorporating one-body dissipation and fluctuations provide a potential tool for exploring dissipation in low-energy heavy ion collisions.Comment: 11 pages, 9 figures, 1 table, including Supplemental Material - Version accepted for publication in Physical Review Letter

Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers

To implement the tool condition monitoring system in a metal cutting process, it is necessary to have sensors which will be able to detect the tool conditions to initiate remedial action. There are different signals for monitoring the cutting process which may require different sensors and signal processing techniques. Each of these signals is capable of providing information about the process at different reliability level. To arrive a good, reliable and robust decision, it is necessary to integrate the features of the different signals captured by the sensors. In this paper, an attempt is made to fuse the features of acoustic emission and vibration signals captured in a precision high speed machining center for monitoring the tool conditions. Tool conditions are classified using machine learning classifiers. The classification efficiency of machine learning algorithms are studied in time-domain, frequencydomain and time-frequency domain by feature level fusion of features extracted from vibration and acoustic emission signature

A predictive processing theory of sensorimotor contingencies: explaining the puzzle of perceptual presence and its absence in synesthesia

Normal perception involves experiencing objects within perceptual scenes as real, as existing in the world. This property of “perceptual presence” has motivated “sensorimotor theories” which understand perception to involve the mastery of sensorimotor contingencies. However, the mechanistic basis of sensorimotor contingencies and their mastery has remained unclear. Sensorimotor theory also struggles to explain instances of perception, such as synesthesia, that appear to lack perceptual presence and for which relevant sensorimotor contingencies are difficult to identify. On alternative “predictive processing” theories, perceptual content emerges from probabilistic inference on the external causes of sensory signals, however, this view has addressed neither the problem of perceptual presence nor synesthesia. Here, I describe a theory of predictive perception of sensorimotor contingencies which (1) accounts for perceptual presence in normal perception, as well as its absence in synesthesia, and (2) operationalizes the notion of sensorimotor contingencies and their mastery. The core idea is that generative models underlying perception incorporate explicitly counterfactual elements related to how sensory inputs would change on the basis of a broad repertoire of possible actions, even if those actions are not performed. These “counterfactually-rich” generative models encode sensorimotor contingencies related to repertoires of sensorimotor dependencies, with counterfactual richness determining the degree of perceptual presence associated with a stimulus. While the generative models underlying normal perception are typically counterfactually rich (reflecting a large repertoire of possible sensorimotor dependencies), those underlying synesthetic concurrents are hypothesized to be counterfactually poor. In addition to accounting for the phenomenology of synesthesia, the theory naturally accommodates phenomenological differences between a range of experiential states including dreaming, hallucination, and the like. It may also lead to a new view of the (in)determinacy of normal perception

A Safety Transport Model for Validation of UK Coach Operators for School Journeys

Coaches are considered to be one of the safest modes of transport for children in the UK. In the last 10 years alone, 1191 children were injured in 371 coach crashes. Though the government has strict regulations to maintain road worthiness of the coaches, operator non-compliance was the major reason for these accidents. In last year alone, 137 coach operator licenses have been revoked due to operator non-compliance in the UK. Currently, there is no process to reliably mitigate the safety risks of children travelling by coaches. This has created a requirement to validate all the coach operators before using their coaches for school trips. This paper proposes a novel safety model for validation of coach operators prior to commencement of coach journeys

Feature Mapping Techniques for Improving the Performance of Fault Diagnosis of Synchronous Generator

Support vector machine (SVM) is a popular machine learning algorithm used extensively in machine fault diagnosis. In this paper, linear, radial basis function (RBF), polynomial, and sigmoid kernels are experimented to diagnose inter-turn faults in a 3kVA synchronous generator. From the preliminary results, it is observed that the performance of the baseline system is not satisfactory since the statistical features are nonlinear and does not match to the kernels used. In this work, the features are linearized to a higher dimensional space to improve the performance of fault diagnosis system for a synchronous generator using feature mapping techniques, sparse coding and locality constrained linear coding (LLC). Experiments and results show that LLC is superior to sparse coding for improving the performance of fault diagnosis of a synchronous generator. For the balanced data set, LLC improves the overall fault identification accuracy of the baseline RBF system by 22.56%, 18.43% and 17.05% for the R, Y and Bphase faults respectively

Omega Production in pp Collisions

A model-independent irreducible tensor formalism which has been developed earlier to analyze measurements of $\vec{p}\vec{p}\to pp \pi^\circ$, is extended to present a theoretical discussion of $\vec{p}\vec{p}\to pp \omega$ and the polarization of $\omega$ in $pp\to pp \vec{\omega}$. The recent measurement of unpolarized differential cross section for $pp\to pp \omega$ is analyzed using this theoretical formalism.Comment: 5 pages (double column), no figures, uses revtex