Analysis of phosphorus removal and anaerobic selector performance in a full-scale activated sludge process in Singapore.

This paper analyses the performance of the anaerobic selector (A/O process) in a full-scale activated sludge process receiving mostly industrial sewage discharge (> 60%) in Singapore. In addition to the sludge settleability, enhanced biological phosphorus removal (EBPR) was studied. The sludge volume index (SVI) reduced from 200 to 80 ml g(-1) and foaming was suppressed significantly, indicating the effectiveness of the anaerobic selector in improving sludge settleability. The phosphorus removal efficiency was 66%, and 7.5 mg HAc-COD was consumed per mg PO4(3-) -P removed. In the anaerobic compartment, 31% of the SCOD and 73% of the acetic acid in the settled sewage were removed with PO4(3-) -P release of 14.1 mg PO4(3-)-P l(-1). The linear correlation between PO4(3-) -P release in the anaerobic compartment and PO4(3-) -P uptake in the aerobic compartment indicates that there is about 0.8 mg PO4(3-) -P release in the anaerobic compartment per mg PO34(3-) -P uptake in the aerobic compartment. The fates of volatile fatty acids (VFAs) and its short chain acids (SCAs) in the process were studied and discussed.     

Computers for symbolic processing

A detailed survey on the motivations, design, applications, current status, and limitations of computers designed for symbolic processing is provided. Symbolic processing computations are performed at the word, relation, or meaning levels, and the knowledge used in symbolic applications may be fuzzy, uncertain, indeterminate, and ill represented. Various techniques for knowledge representation and processing are discussed from both the designers' and users' points of view. The design and choice of a suitable language for symbolic processing and the mapping of applications into a software architecture are then considered. The process of refining the application requirements into hardware and software architectures is treated, and state-of-art sequential and parallel computers designed for symbolic processing are discussed     

Purification and renaturation of recombinant human lymphotoxin (tumour necrosis factor beta) expressed in Escherichia coli as inclusion bodies

High level expression of recombinant human tumour necrosis factor β (rh TNF-β) in Escherichia coli results in the formation of two portions of protein, namely soluble active protein and insoluble protein which is inactive and aggregates in the form of inclusion bodies (IBs). In this study, a procedure for purification and renaturation of rh TNF-β from inclusion bodies has been designed and verified experimentally with a product purity of more than 90% and a recovery of about 30%. The procedure includes washing of IBs with specific wash buffer (Triton X-100/EDTA/lysozyme/PMSF), their solubilization with 8 mol dm^?3 alkaline urea, purification with ion-exchange columns, refolding with renaturation buffer and finally concentration and desalination with an ultrafiltration membrane. The characteristics of the renatured protein were identical with those of purified protein from the soluble fraction as demonstrated by (1) SDS-PAGE, (2) cytotoxic activity on mouse L929 cells, (3) N-terminal amino acid sequence, and (4) gel filtration chromatography.     

Deep learning approaches for speech emotion recognition: state of the art and research challenges

Speech emotion recognition (SER) systems identify emotions from the human voice in the areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and human-machine interaction. In the classical SER process, discriminative acoustic feature extraction is the most important and challenging step because discriminative features influence the classifier performance and decrease the computational time. Nonetheless, current handcrafted acoustic features suffer from limited capability and accuracy in constructing a SER system for real-time implementation. Therefore, to overcome the limitations of handcrafted features, in recent years, variety of deep learning techniques have been proposed and employed for automatic feature extraction in the field of emotion prediction from speech signals. However, to the best of our knowledge, there is no in-depth review study is available that critically appraises and summarizes the existing deep learning techniques with their strengths and weaknesses for SER. Hence, this study aims to present a comprehensive review of deep learning techniques, uniqueness, benefits and their limitations for SER. Moreover, this review study also presents speech processing techniques, performance measures and publicly available emotional speech databases. Furthermore, this review also discusses the significance of the findings of the primary studies. Finally, it also presents open research issues and challenges that need significant research efforts and enhancements in the field of SER systems.

     

Characteristic features of the hydrocarbon composition of Paleozoic oils from the southeast of Western Siberia

The biomarker composition of eight samples of Western Siberia Paleozoic oils collected from deposits dated to the Pudinskii megaswell, the Nyurol’ka depression, their junction zone, and the Krasnoleninskii dome has been studied. Several indicators of oil composition have been identified, whose value varies depending on the geological structure that accommodates the oilfield. These differences may be evidence for different local environments of the formation of petroleum hydrocarbons. According to data on the composition of aromatic hydrocarbons, the original organic matter (OM) of all the test oils was deposited in a marine environment, and the OM of the Nyurol’ka through and the junction zone accumulated in the photic zone euxinia, as evidenced by the presence of aryl isoprenoids. Data on the composition of saturated hydrocarbons suggest brackish, littoral, suboxidizing sedimentary environment and marine algae as the main biological producer.     

Postbuckling Formulation for Symmetric Laminated Panels with Various Edge Support Conditions

A study into the structural postbuckling response of symmetric laminated composite plates subjected to in-plane compressive load is presented in this contribution. Various cases of support conditions are investigated: loaded ends are either simply supported or clamped, while the unloaded edges are a combination of simple support, clamp, and free. The method of analysis used is the combination of the semienergy approach using the semianalytical finite-strip methodology and the well-known von Karman large-deflection equations. Extensive validation of the analytical solution with published theoretical solutions and experimental data are presented. The final form of the expression is quite versatile and can be readily used for design purposes.     

Phase Transformations of α-Alumina Made from Waste Aluminum via a Precipitation Technique

We report on a recycling project in which α-Al₂O₃ was produced from aluminum cans because no such work has been reported in literature. Heated aluminum cans were mixed with 8.0 M of H₂SO₄ solution to form an Al₂(SO₄)₃ solution. The Al₂(SO₄)₃ salt was contained in a white semi-liquid solution with excess H₂SO₄; some unreacted aluminum pieces were also present. The solution was filtered and mixed with ethanol in a ratio of 2:3, to form a white solid of Al₂(SO₄)₃·18H₂O. The Al₂(SO₄)₃·18H₂O was calcined in an electrical furnace for 3 h at temperatures of 400–1400 °C. The heating and cooling rates were 10 °C/min. XRD was used to investigate the phase changes at different temperatures and XRF was used to determine the elemental composition in the alumina produced. A series of different alumina compositions, made by repeated dehydration and desulfonation of the Al₂(SO₄)₃·18H₂O, is reported. All transitional alumina phases produced at low temperatures were converted to α-Al₂O₃ at high temperatures. The X-ray diffraction results indicated that the α-Al₂O₃ phase was realized when the calcination temperature was at 1200 °C or higher.     

A side-sensitive synthetic coefficient of variation chart

Control charts for monitoring the coefficient of variation (γ) are useful for processes with an inconsistent mean (μ) and a standard deviation (σ) which changes with μ, by monitoring the consistency in the ratio σ over μ. The synthetic-γ chart is one of the charts proposed to monitor γ, and its attractiveness lie in waiting until a second point to fall outside the control limits before a decision is made. However, existing synthetic-γ charts do not differentiate between the points falling outside the upper control limit (UCL) and lower control limit (LCL). Hence, this paper proposes a side-sensitive synthetic-γ chart, where successive nonconforming samples must either fall above the UCL or below the LCL. Formulae to compute the average run length (ARL), the standard deviation of the run length (SDRL) and expected average run length (EARL) are derived using the Markov chain approach, and the algorithms to obtain the optimal charting parameters are proposed. Subsequently, the optimal charting parameters, ARL, SDRL and EARL values for various numerical examples are shown. Comparisons show that the side-sensitive synthetic-γ chart consistently outperforms the existing synthetic-γ chart, especially for small shifts. The proposed chart also consistently outperforms the Shewhart-γ chart, while showing comparable or better performance than the Exponentially Weighted Moving Average (EWMA) chart for most shift sizes, except for very small shifts. Finally, this paper shows the implementation of the proposed chart on an industrial example.