Biosynthesis of γ ‐Fe2 O3 @CuO core–shell nanoclusters using aqueous extract of Sesbania grandiflora Linn fresh leaves,its characterisation,and antimicrobial activity studies against Staphylococcus aureus strains

The present study reports an eco‐friendly and rapid method for the synthesis of core–shell nanoclusters using the modified reverse micelle method. It is a green synthetic method which uses Sesbania grandiflora Linn extract which acts as a reducing and capping agent. It is observed that this method is very fast and convenient and the nanoclusters are formed with 5–10 min of the reaction time without using harsh conditions. The core–shell nanoclusters so prepared were characterised using UV–Vis spectroscopy, scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, and X‐ray photoelectron spectroscopy. Further, their effective antibacterial activity towards the gram‐positive bacteria Staphylococcus aureus was found to be due to their smaller particle size.Inspec keywords: iron compounds, copper compounds, nanoparticles, particle size, nanofabrication, nanomedicine, biomedical materials, core‐shell nanostructures, antibacterial activity, ultraviolet spectra, visible spectra, microorganisms, reduction (chemical), scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, X‐ray photoelectron spectraOther keywords: biosynthesis, γ‐Fe₂ O₃ ‐CuO core‐shell nanoclusters, aqueous extract, Sesbania grandiflora Linn fresh leaves, antimicrobial activity, Staphylococcus aureus strains, eco‐friendly method, modified reverse micelle method, green synthetic method, reducing agent, capping agent, UV‐visible spectroscopy, scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, X‐ray photoelectron spectroscopy, antibacterial activity, gram‐positive bacteria Staphylococcus aureus, particle size, time 5 min to 10 min, Fe₂ O₃ ‐CuO     

Distributed model predictive control of dynamically decoupled systems with coupled cost

This paper considers the distributed model predictive control (DMPC) of systems with interacting subsystems having decoupled dynamics and constraints but coupled costs. An easily-verifiable constraint is introduced to ensure asymptotic stability of the overall system in the absence of disturbance. The constraint introduced has a parameter which allows for the performance of the DMPC system to approach that controlled by a centralized model predictive controller. When the subsystems are linear and additive disturbance is present, the added constraint ensures the state of each subsystem converges to its respective minimal disturbance invariant set. The approach is demonstrated via several numerical examples.     

Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection

We present a novel method for detecting malaria parasites and determining the stage of infection from digital images comprising red blood cells (RBCs). The proposed method is robust under varying conditions of image luminance, contrast and clumping of RBCs. Both strong and weak boundary edges of the RBCs and parasites are detected based on the similarity measure between local image neighborhoods and predefined edge filters. A rule-based algorithm is applied to link edge fragments to form closed contours of the RBCs and parasite regions, as well as to split clumps into constituent cells. A radial basis support vector machine determines the stage of infection from features extracted from each parasite region. The proposed method achieves 97% accuracy in cell segmentation and 86% accuracy in parasite detection when tested on a total of 530 digitally captured images of three species of malaria parasites: Plasmodium falciparum, Plasmodium yoelii and Plasmodium berghei.     

Motor noise generation and prediction for high spinning speed hard disk drive

The acoustic level of a hard disk drive is an important specification. This is especially so for enterprise HDD which is required to operate under a high spinning speed. In order to reduce the acoustics level of a HDD, an in depth understanding behind the mechanisms whereby the noise is being generated in a HDD should be established. This paper will study the acoustics characteristics of a high speed permanent magnet brushless DC motor (PM BLDC) and investigate the physics underlying the generation of noise associated with a motor. An analysis procedure of the PM BLDC motor noise generating mechanisms is proposed. Through finite element analysis and experimental verification, the results indicate that the major noise source for the high speed PM BLDC motor is due to the presence of electromagnetic (EM) torque ripples. Furthermore, it has been observed that the noise radiated by a HDD can be amplified when its structural dynamics are close to the frequency components of the noise source. Finally, a methodology which discusses the steps involved in the prediction of motor radiated noise will be presented.     

A hybrid agent architecture integrating desire,intention and reinforcement learning

This paper presents a hybrid agent architecture that integrates the behaviours of BDI agents, specifically desire and intention, with a neural network based reinforcement learner known as Temporal Difference-Fusion Architecture for Learning and COgNition (TD-FALCON). With the explicit maintenance of goals, the agent performs reinforcement learning with the awareness of its objectives instead of relying on external reinforcement signals. More importantly, the intention module equips the hybrid architecture with deliberative planning capabilities, enabling the agent to purposefully maintain an agenda of actions to perform and reducing the need of constantly sensing the environment. Through reinforcement learning, plans can also be learned and evaluated without the rigidity of user-defined plans as used in traditional BDI systems. For intention and reinforcement learning to work cooperatively, two strategies are presented for combining the intention module and the reactive learning module for decision making in a real time environment. Our case study based on a minefield navigation domain investigates how the desire and intention modules may cooperatively enhance the capability of a pure reinforcement learner. The empirical results show that the hybrid architecture is able to learn plans efficiently and tap both intentional and reactive action execution to yield a robust performance.     

Efficient simulation of hard disk drive operational shock response using model order reduction

The complex structure, coupled mechanical and fluidic energy domains, and inherent nonlinearity of air bearing between slider and disk involved in the hard disk drive (HDD) are normally presented as a large scale problem which will result in very heavy computational costs in terms of intensive computation and time consuming for HDD research communities and industries to carry out the transient dynamic simulation for HDD design verification, performance analysis, and optimization by using the traditional full-order models, such as finite element model (FEM). This paper presents a method of application of model order reduction (MOR) technique to dramatically reduce the computation time for HDD transient shock performance analysis while capturing the behaviors of original problem faithfully. The reduced models are obtained by performing MOR directly to the FEMs through Krylov subspace and Arnoldi algorithm. The transient operational shock response results of the reduced models of a head suspension assembly (HSA) subjected to half-sine shock pulse demonstrate that the reduced models can dramatically reduce total computation by at least three orders and have very good agreement with those simulated from the original large problem by full-order FEM.     

A surface blending approach for displacement features on freeform surfaces

Embedding a number of displacement features into a base surface is common in industrial product design and modeling, where displaced surface regions are blended with the unmodified surface region. The cubic Hermite interpolant is usually adopted for surface blending, in which tangent plane smoothness across the boundary curve is achieved. However, the polynomial degree of the tangent field curve obtained symbolically is considerably higher, and the reduction of the degree of a freeform curve is a non-trivial task. In this work, an approximation surface blending approach is proposed to achieve tangential continuity across the boundary curve. The boundary curve is first offset in the tangent field with the user-specified tolerance, after which it is refined to be compatible with the offset curve for surface blending. Since the boundary curve is offset in a three-dimensional (3D) space, the local self-intersection in the offset curve is addressed in a 2D space by approximately mapping the offset vectors in the respective tangent planes to the parameter space of the base surface. The proposed algorithm is validated using examples, and the normal vector deviation along the boundary curve is investigated.