Enrichment of bacteria possessing catechol dioxygenase genes in the rhizosphere of Spirodela polyrrhiza: A mechanism of accelerated biodegradation of phenol

The bacterial community structure in bulk water and in rhizosphere fractions of giant duckweed, Spirodela polyrrhiza, was quantitatively and qualitatively investigated by PCR-based methods using 6 environmental water samples to elucidate the mechanisms underlying selective accumulation of aromatic compound-degrading bacteria in the rhizosphere of S. polyrrhiza. S. polyrrhiza selectively accumulated a diverse range of aromatic compound-degrading bacteria in its rhizosphere, regardless of the origin of water samples, despite no exposure to phenol. The relative abundances of the catechol 1,2-dioxygenase (C12O) gene (C12O DNA) and catechol 2,3-dioxygenase (C23O) gene (C23O DNA) were calculated as the ratios of the copy numbers of these genes to the copy number of 16S rDNA and are referred to as the rhizosphere effect (RE) value. The RE values for C12O DNA and C23O DNA were 1.0 × 10¹–9.3 × 10³ and 1.7 × 10²–1.5 × 10⁴ times as high, respectively, in rhizosphere fractions as in bulk water fractions, and these higher values were associated with a notably higher sequence diversity of C12O DNA and C23O DNA. The RE values during phenol degradation were 3.6 × 10⁰–4.3 × 10² and 2.2 × 10⁰–1.7 × 10², respectively, indicating the ability of S. polyrrhiza to selectively accumulate aromatic compound-degrading bacteria in its rhizosphere during phenol degradation. The bacterial communities in the rhizosphere fractions differed from those in the bulk water fractions, and those in the bulk water fractions were notably affected by the rhizosphere bacterial communities. S. polyrrhiza released more than 100 types of phenolic compound into its rhizosphere as root exudates at the considerably high specific release rate of 1520 mg TOC and 214 mg phenolic compounds/d/g root (wet weight). This ability of S. polyrrhiza might result in the selective recruitment and accumulation of a diverse range of bacteria harboring genes encoding C12O and C23O, and the subsequent accelerated degradation of phenol in the rhizosphere.     

Experimental and numerical studies of wingtip and downwash effects on horizontal tail

Journal of Mechanical Science and Technology - Studying wing downwash, which is caused by the wingtip effect, and its influence on horizontal tail is important for aircraft design. In this work,...     

Particle size distribution and homogenisation efficiency in high-pressure homogenisation of wheat germ oil-water system

Wheat germ oil (WGO) is well-known as a good source of vegetable oil due to its nutrients and health benefits. Emulsification is a process that improves the incorporation of oil into food. High-pressure homogenisation (HPH) is a nonthermal and soft technique with enormous potential in oil-in-water emulsification. This paper focussed on the application of HPH for emulsification of WGO-in-water system. Influences of homogenisation pressure (100–300 bar), oil fraction (10–20% v/v) and lecithin adding (0–0.2% w/v of content) on the homogenisation were evaluated based on distribution of particles diameter and homogenisation efficiency. The increase in operating pressure and lecithin ratio decreased the particle size and increased the emulsion stability, and vice versa for oil fraction. The findings imply that the investigated factors significantly influenced particle size and emulsion system stability. The regression model between mean particle diameter and technical conditions of emulsion was established. With HPH treatment conditions of 300 bar operating pressure, 10% (v/v) oil fraction and 0.2% (w/v) lecithin created an emulsion system with a mean particle size of 3.32 µm, more than 50% of the volume of particles smaller than 1.5 µm of diameter and the homogenisation efficiency of 98.61%. HPH exhibits high efficiency and potential in WGO-in-water emulsification application.     

Experimental and optimization of material synthesis process parameters for improving capacity of lithium‐ion battery

New methods for synthesis of active materials have been developed to improve capacity and cycle life performance of lithium‐ion batteries. Past studies have focused on routes of development of materials and new processes, which might not be economical for large‐scale production. In this regard, this study examines a widely employed carbothermal reduction technology for the synthesis of lithium‐iron phosphate (LiFePO₄/C) and investigates effects of process conditions during this synthesis on final battery performance. An experimental combined genetic programming approach is used to model the effects of crucial process conditions (sintering time, the carbon content, and the sintering temperature) on the discharge capacity of the assembled battery. Experiments are conducted to collect the discharge capacity data based on varying LiFePO₄/C synthesis conditions, and genetic programming is employed to develop a suitable functional relationship between them. The results show that the battery discharge capacity is controlled significantly by adjusting sintering temperature and carbon content, while the effect of sintering time is found to be insignificant. Further, the interaction effect of the sintering time and carbon content is much more obvious than that of the sintering time and the sintering temperature. The findings from the study pave the way for the optimum design of the synthesis process of LiFePO₄/C for a higher battery performance.     

Automatic statistical process control of a CNC turning centre using tool offsets and tool change

This work deals with the implementation of automatic statistical process control (ASPC) on a CNC turning centre using automatic in-cycle gauging with tool offsets and tool change. Owing to the increasing need for a fast and reliable quality control system and in order to cope with the high output rates achievable in manufacturing industry today, a closed-loop ASPC system has been proposed. The technique of automatic in-cycle gauging is introduced in preparation for a detailed examination of the ASPC system which has been procured for this work.     

A fuzzy rule-based generation algorithm in interval type-2 fuzzy logic system for fault prediction in the early phase of software development

Reliability, a measure of software, deals in total number of faults count up to a certain period of time. The present study aims at estimating the total number of software faults during the early phase of software life cycle. Such estimation helps in producing more reliable software as there may be a scope to take necessary corrective actions for improving the reliability within optimum time and cost by the software developers. The proposed interval type-2 fuzzy logic-based model considers reliability-relevant software metric and earlier project data as model inputs. Type-2 fuzzy sets have been used to reduce uncertainties in the vague linguistic values of the software metrics. A rule formation algorithm has been developed to overcome inconsistency in the consequent parts of large number of rules. Twenty-six software project data help to validate the model, and a comparison has been provided to analyse the proposed model’s performance.     

Appearance Flow Completion for Novel View Synthesis

Novel view synthesis from sparse and unstructured input views faces challenges like the difficulty with dense 3D reconstruction and large occlusion. This paper addresses these problems by estimating proper appearance flows from the target to input views to warp and blend the input views. Our method first estimates a sparse set 3D scene points using an off‐the‐shelf 3D reconstruction method and calculates sparse flows from the target to input views. Our method then performs appearance flow completion to estimate the dense flows from the corresponding sparse ones. Specifically, we design a deep fully convolutional neural network that takes sparse flows and input views as input and outputs the dense flows. Furthermore, we estimate the optical flows between input views as references to guide the estimation of dense flows between the target view and input views. Besides the dense flows, our network also estimates the masks to blend multiple warped inputs to render the target view. Experiments on the KITTI benchmark show that our method can generate high quality novel views from sparse and unstructured input views.     

Learning from Longitudinal Face Demonstration—Where Tractable Deep Modeling Meets Inverse Reinforcement Learning

International Journal of Computer Vision - This paper presents a novel subject-dependent deep aging path (SDAP), which inherits the merits of both generative probabilistic modeling and inverse...