Role of Co in formation of Ni-Ti clusters in maraging stainless steel

The effect of Co addition on the formation of Ni-Ti clusters in maraging stainless steel was studied by three dimensional atom probe (3DAP) and first-principles calculation. The cluster analysis based on the maximum separation approach showed an increase in size but a decrease in density of Ni-Ti clusters with increasing the Co content. The first-principles calculation indicated weaker Co-Ni (Co-Ti) interactions than Co-Ti (Fe-Ti) interactions, which should be the essential reason for the change of distribution characteristics of Ni-Ti clusters in bcc Fe caused by Co addition.     

Evaporated Thin Films of CdS and CdTe: Optimization for Photovoltaic Applications

Thin films of cadmium sulphide and cadmium telluride have been prepared by thermal evaporation under various conditions of deposition. These films have been characterized optically. electrically and for structure determination. The results of these characterizations along with the initial results of all thin film CdS/CdTe solar cells are presented in this paper     

A review on fault detection and diagnosis techniques: basics and beyond

Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.

     

Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization

A mobile ad hoc network (MANET) is dynamic in nature and is composed of wirelessly connected nodes that perform hop-by-hop routing without the help of any fixed infrastructure. One of the important requirements of a MANET is the efficiency of energy, which increases the lifetime of the network. Several techniques have been proposed by researchers to achieve this goal and one of them is clustering in MANETs that can help in providing an energy-efficient solution. Clustering involves the selection of cluster-heads (CHs) for each cluster and fewer CHs result in greater energy efficiency as these nodes drain more power than noncluster-heads. In the literature, several techniques are available for clustering by using optimization and evolutionary techniques that provide a single solution at a time. In this paper, we propose a multi-objective solution by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in an ad hoc network as well as energy dissipation in nodes in order to provide an energy-efficient solution and reduce the network traffic. In the proposed solution, inter-cluster and intra-cluster traffic is managed by the cluster-heads. The proposed algorithm takes into consideration the degree of nodes, transmission power, and battery power consumption of the mobile nodes. The main advantage of this method is that it provides a set of solutions at a time. These solutions are achieved through optimal Pareto front. We compare the results of the proposed approach with two other well-known clustering techniques; WCA and CLPSO-based clustering by using different performance metrics. We perform extensive simulations to show that the proposed approach is an effective approach for clustering in mobile ad hoc networks environment and performs better than the other two approaches.     

Cumin Prevents 17β-Estradiol-Associated Breast Cancer in ACI Rats

Breast cancer (BC) is a leading cause of cancer deaths in women in less developed countries and the second leading cause of cancer death in women in the U.S. In this study, we report the inhibition of E2-mediated mammary tumorigenesis by Cuminum cyminum (cumin) administered via the diet as cumin powder, as well as dried ethanolic extract. Groups of female ACI rats were given either an AIN-93M diet or a diet supplemented with cumin powder (5% and 7.5%, w/w) or dried ethanolic cumin extract (1%, w/w), and then challenged with subcutaneous E2 silastic implants (1.2 cm; 9 mg). The first appearance of a palpable mammary tumor was significantly delayed by both the cumin powder and extract. At the end of the study, the tumor incidence was 96% in the control group, whereas only 55% and 45% animals had palpable tumors in the cumin powder and extract groups, respectively. Significant reductions in tumor volume (660 ± 122 vs. 138 ± 49 and 75 ± 46 mm³) and tumor multiplicity (4.21 ± 0.43 vs. 1.16 ± 0.26 and 0.9 ± 0.29 tumors/animal) were also observed by the cumin powder and cumin extract groups, respectively. The cumin powder diet intervention dose- and time-dependently offset E2-related pituitary growth, and reduced the levels of circulating prolactin and the levels of PCNA in the mammary tissues. Mechanistically, the cumin powder diet resulted in a significant reversal of E2-associated modulation in ERα, CYP1A1 and CYP1B1. Further, the cumin powder diet reversed the expression levels of miRNAs (miR-182, miR-375, miR-127 and miR-206) that were highly modulated by E2 treatment. We analyzed the composition of the extract by GC/MS and established cymene and cuminaldehyde as major components, and further detected no signs of gross or systemic toxicity. Thus, cumin bioactives can significantly delay and prevent E2-mediated mammary tumorigenesis in a safe and effective manner, and warrant continued efforts to develop these clinically translatable spice bioactives as chemopreventives and therapeutics against BC.     

Ambient‐cured polyesteramide‐based anticorrosive coatings from linseed oil—A sustainable resource

Ambient‐cured polyesteramide (APEA) coating resin synthesized from dihydroxy fatty amide obtained from linseed oil, a sustainable resource, and poly(styrene‐co‐maleic anhydride), a bifunctional acid component, was found to exhibit improved physicomechanical and anticorrosive properties. The structural elucidation of APEA resin has been carried out by FTIR, ¹H‐NMR, and ¹³C‐NMR spectroscopies. The physicomechanical and chemical resistance properties were investigated by standard methods. The corrosion resistance performance was evaluated in acid, alkali, and organic solvent. The thermal behavior was studied by TGA technique. A comparative study of these properties of APEA with reported baked polyesteramide (PEA) coatings was carried out. A remarkable improvement in the drying property of APEA was observed. The APEA coatings also showed improved physicomechanical and anticorrosive properties as compared to the baked PEA coatings. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 1818–1824, 2005     

An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.