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Phycospheric bacteria may be the key biological factors affecting the growth of algae. However, the studies about interaction between Isochrysis galbana and its phycospheric bacteria are limited. Here, we show that a marine heterotrophic bacterium, Alteromonas macleodii, enhanced the growth of I. galbana, and inhibited non-photochemical quenching (NPQ) and superoxide dismutase (SOD) activities of this microalgae. Further, we explored this phenomenon via examining how the entire transcriptomes of I. galbana changed when it was co-cultured with A. macleodii. Notable increase was observed in transcripts related to photosynthesis, carbon fixation, oxidative phosphorylation, ribosomal proteins, biosynthetic enzymes, and transport processes of I. galbana in the presence of A. macleodii, suggesting the introduction of the bacterium might have introduced increased production and transport of carbon compounds and other types of biomolecules. Besides, the transcriptome changed largely corresponded to reduced stress conditions for I. galbana, as inferred from the depletion of transcripts encoding DNA repair enzymes, superoxide dismutase (SOD) and other stress-response proteins. Taken together, the presence of A. macleodii mainly enhanced photosynthesis and biosynthesis of I. galbana and protected it from stress, especially oxidative stress. Transfer of fixed organic carbon, but perhaps other types of biomolecules, between the autotroph and the heterotroph might happen in I. galbana-A. macleodii co-culture. The present work provides novel insights into the transcriptional consequences of I. galbana of mutualism with its heterotrophic bacterial partner, and mutually beneficial associations existing in I. galbana-A. macleodii might be explored to improve productivity and sustainability of aquaculture algal rearing systems.

Estimating the quality of an electrical discharge machining (EDM) workpiece is challenging when attempting to extract features from the stochastic and time-consuming processes. To solve this problem, an intelligent sensing unit for EDM (ISU-EDM) is proposed to extract key machining features for estimating workpiece roughness. During machining, the ISU-EDM simultaneously samples the signals of both the discharge current and voltage, while automatically segmenting the signals according to tool location and discharge effectiveness. Furthermore, the machining features could be extracted from the segmented data by a genetic-algorithm-based distribution fitting method. After applying the features to an automated virtual metrology system, experimental results show that the mean absolute percentage error of roughness estimation is less than 15%.

We investigated the relationship between the structures of the CeO2 products (particle size, morphology and their characteristics) prepared using different amino acids. Cerium hydroxide carbonate precursors were initially prepared by a hydrothermal method and were subsequently converted to CeO2 by its thermal decomposition. Various amino acids were used as structure-directing agents in the presence of cerium nitrate and urea as precursors. The results indicate morphology selectivity using different amino acids; CeO2 structures, such as quasi-prism-sphere, straw-bundle, urchin-flower like and polyhedron prisms, indeed could be produced. Raman and photoluminescence studies indicate the presence of oxygen vacancies in the CeO2 samples. Photoluminescence spectra of CeO2 with l -Valine exhibit stronger emission compared with other amino acids utilized under this study, indicating the higher degree of defects in these particles. This study clearly indicates that the degree of defects varied in the presence of different amino acids. Improved precision to control the crystal morphology is important in various material applications and our study provides a novel method to achieve this specificity.

Evaluating workpiece quality of electrical discharge machining (EDM) process is challenging users when trying to extract features from the stochastic and time-consuming process. This study proposes an intelligent sensing unit for EDM (ISU-EDM) to extract key machining features for predicting the roughness of workpieces. In machining, the ISU-EDM can synchronously sample the waveforms of discharge current and voltage, and automatically segment the waveforms as sensing data according to tool location and waveform effectiveness. In further, the machining features are extracted from the sensing data by using a genetic-algorithm-based distribution fitting method. The experimental results show that the mean absolute percentage error of prediction accuracy of roughness is less than 15% after applying the extracted features to the automated virtual metrology system.

The process of separating oil and water from oil/water mixtures is an attractive strategy to answer the menace caused by industrial oil spills and oily wastewater. In addition, water coproduced during hydrocarbon exploitation, which can be an economic burden and risk for freshwater resources, can become an important freshwater source after suitable water-oil separation. For oil-water separation purposes, considerable attention has been paid to the preparation of hydrophobic - oleophilic materials with modified surface roughness. However, due to issues of thermodynamic instability, costly and complex methods as well as lack of ecofriendly compounds, most of hydrophobic surface modified particles are of limited practical application. The study presents a facile procedure, to synthesize crystalline particles of calcite, which is the most stable polymorph of CaCO 3 from industrial CaCO 3 using oleic acid as an additive in a one-pot synthesis method. The XRD results show that the synthesized particles were a well-crystallized form of calcite. The FTIR results reflect the appearance of the alkyl groups from the oleic acid in synthesized particles which promotes the production of calcite with ‘rice shape’ (1.64 μm) (aggregated by spherical nanoparticle of 19.56 nm) morphology with concomitant changes in its surface wettability from hydrophilic to hydrophobic. The synthesized particles exhibited near to super hydrophobicity with ∼99% active ratio and a contact angle of 143.8°. The synthesized hydrophobic calcite particles had an oleophilic nature where waste diesel oil adsorption capacity of synthesized calcium carbonate (HCF) showed a very high (>99%) and fast (7 s) oil removal from oil-water mixture. The functional group of long alkyl chain including of C O bounds may play critical roles for adsorption of diesel oils. Moreover, the thermodynamically stable crystalline polymorph calcite (compared to vaterite) exhibited excellent recyclability. The isothermal study reflects the comparatively high value of correlation coefficient (R 2 = 0.94) for the Langmuir isotherm compared to those of the Freundlich isotherm (R 2 = 0.82) showed that the adsorption of diesel oil onto the hydrophobic CaCO 3 adsorbent was much better described by the Langmuir isotherm. The kinetics study of second-order rate expression (R 2 = 0.99) more fitted with the experimental data compare to first-order model (R 2 = 0.92). The synthesized calcite exhibited a significant dual oleophilic and hydrophobic nature that can be applicable for oil adsorption/or removal purpose in oil contaminated areas in environment and/or industrial oily wastewater for green, simple, and inexpensive environmental cleanup.

This paper presents a cloud service scheme to predict tool wear. When lacking sufficient historical tool wear data for building a data-driven model, predicting tool life is challenging while under various cutting conditions with different tools and machines. On the basis of a hybrid tool wear model with dynamic neural network, this paper proposes a tool life prediction scheme for predicting tool wear by given cutting conditions and relevant tool wear features which extracted from sensing segment data. Experimental results show that the proposed scheme can assist factory users to predict various tool lifetimes well in the cloud-service environment while with the first tool samples for modeling.