Stroboscopic back-action evasion in a dense alkali-metal vapor

We explore experimentally quantum non-demolition (QND) measurements of atomic spin in a hot potassium vapor in the presence of spin-exchange relaxation. We demonstrate a new technique for back-action evasion by stroboscopic modulation of the probe light. With this technique we study spin noise as a function of polarization for atoms with spin greater than 1/2 and obtain good agreement with a simple theoretical model. We point that in a system with fast spin-exchange, where the spin relaxation rate is changing with time, it is possible to improve the long-term sensitivity of atomic magnetometry by using QND measurements

Pose consensus based on dual quaternion algebra with application to decentralized formation control of mobile manipulators

This paper presents a solution based on dual quaternion algebra to the general problem of pose (i.e., position and orientation) consensus for systems composed of multiple rigid-bodies. The dual quaternion algebra is used to model the agents' poses and also in the distributed control laws, making the proposed technique easily applicable to time-varying formation control of general robotic systems. The proposed pose consensus protocol has guaranteed convergence when the interaction among the agents is represented by directed graphs with directed spanning trees, which is a more general result when compared to the literature on formation control. In order to illustrate the proposed pose consensus protocol and its extension to the problem of formation control, we present a numerical simulation with a large number of free-flying agents and also an application of cooperative manipulation by using real mobile manipulators

Probabilistic analysis of bladed turbine disks and the effect of mistuning

Probabilistic assessment of the maximum blade response on a mistuned rotor disk is performed using the computer code NESSUS. The uncertainties in natural frequency, excitation frequency, amplitude of excitation and damping are included to obtain the cumulative distribution function (CDF) of blade responses. Advanced mean value first order analysis is used to compute CDF. The sensitivities of different random variables are identified. Effect of the number of blades on a rotor on mistuning is evaluated. It is shown that the uncertainties associated with the forcing function parameters have significant effect on the response distribution of the bladed rotor

Antimicrobial activity of apple cider vinegar against Escherichia coli, Staphylococcus aureus and Candida albicans; downregulating cytokine and microbial protein expression

The global escalation in antibiotic resistance cases means alternative antimicrobials are essential. The aim of this study was to investigate the antimicrobial capacity of apple cider vinegar (ACV) against E. coli, S. aureus and C. albicans. The minimum dilution of ACV required for growth inhibition varied for each microbial species. For C. albicans, a 1/2 ACV had the strongest effect, S. aureus, a 1/25 dilution ACV was required, whereas for E-coli cultures, a 1/50 ACV dilution was required (p < 0.05). Monocyte co-culture with microbes alongside ACV resulted in dose dependent downregulation of inflammatory cytokines (TNFα, IL-6). Results are expressed as percentage decreases in cytokine secretion comparing ACV treated with non-ACV treated monocytes cultured with E-coli (TNFα, 99.2%; IL-6, 98%), S. aureus (TNFα, 90%; IL-6, 83%) and C. albicans (TNFα, 83.3%; IL-6, 90.1%) respectively. Proteomic analyses of microbes demonstrated that ACV impaired cell integrity, organelles and protein expression. ACV treatment resulted in an absence in expression of DNA starvation protein, citrate synthase, isocitrate and malate dehydrogenases in E-coli; chaperone protein DNak and ftsz in S. aureus and pyruvate kinase, 6-phosphogluconate dehydrogenase, fructose bisphosphate were among the enzymes absent in C.albican cultures. The results demonstrate ACV has multiple antimicrobial potential with clinical therapeutic implications

Bitter Taste Receptors for Asthma Therapeutics.

Clinical management of asthma and chronic obstructive pulmonary disease (COPD) has primarily relied on the use of beta 2 adrenergic receptor agonists (bronchodilators) and corticosteroids, and more recently, monoclonal antibody therapies (biologics) targeting specific cytokines and their functions. Although these approaches provide relief from exacerbations, questions remain on their long-term efficacy and safety. Furthermore, current therapeutics do not address progressive airway remodeling (AR), a key pathological feature of severe obstructive lung disease. Strikingly, agonists of the bitter taste receptors (TAS2Rs) deliver robust bronchodilation, curtail allergen-induced inflammatory responses in the airways and regulate airway smooth muscle (ASM) cell proliferation and mitigate features of A

Reverse graded high content (x>0.75) Si1-xGex virtual substrates

Silicon germanium alloy layers can be grown epitaxially on a silicon substrate to provide a means of adjusting the lattice parameter of the crystal. Such a platform, known as a virtual substrate, has a number of potential applications. For instance, it allows for subsequent overgrowth of highly strained layers of silicon, or germanium, that could enable very high speed transistors, similarly it could be used as the starting point of a range a silicon-based optoelectronic devices. In this work, a novel adaptation has been made to a recently proposed reverse grading technique to create high Ge composition SiGe virtual substrates. The proposed structures consist of a relaxed, highly defected, pure Ge underlayer on a Si (001) substrate prior to reverse grading where structures have final compositions of Si0.2Ge0.8. Additionally, two grading schemes are studied, reverse linear grading and reverse terrace grading. All buffers are grown by reduced pressure chemical vapour deposition. The relaxation, defect levels and surface roughness of the fabricated buffers have been quantified whilst varying the grading rate. An ideal grading rate has been found where the quality of the buffer is very high, due to the highly defected Ge underlayer and that the buffer relaxes under tensile strain. Outside of this ideal grading rate three dimensional growth, stacking fault formation and crack generation can occur. Cracking of the buffer has been modelled and some conditions where the buffer is stable have been found. This study experimentally investigates this proposed solution and a crack-stable high quality buffer is fabricated. Comparisons have been drawn with other more popular buffer fabrication techniques and it is found that this technique has very competitive qualities

Power Saving by Using Image Processing

This Paper Proposes Power Saving by using Image Processing. In India major problems of Energy and Power crisis because of it is developing country. We have many ways to save Electricity using Electric and Electronic Gadgets whenever and wherever is need and we can also switching them off, while not in use. But in many places such as large auditoriums and meeting halls, there will be a fan or an Air-conditioner keep running. Due to this, a large amount of electricity is wastage. We can prevent this wastage by using installing IR sensors to detect people. But these methods are quite costlier and required large areas. Therefore, we propose a new method of controlling the power supply by using Image Processing. In this paper, we have to take reference image and if any change in that reference image it will detected and change their status according to that and equipment will be turned on. In this way power wastage is controlled. We can use this system for dual purpose in which a camera is used for detecting people as well as surveillance. The main advantage of this system is a very simple, efficient and cheaper technique to save energy. Second big advantage is we extend this up to application like home automation etc

Local origins impart conserved bone type-related differences in human osteoblast behaviour

Osteogenic behaviour of osteoblasts from trabecular, cortical and subchondral bone were examined to determine any bone type-selective differences in samples from both osteoarthritic (OA) and osteoporotic (OP) patients. Cell growth, differentiation; alkaline phosphatase (TNAP) mRNA and activity, Runt-related transcription factor-2 (RUNX2), SP7-transcription factor (SP7), bone sialoprotein-II (BSP-II), osteocalcin/bone gamma-carboxyglutamate (BGLAP), osteoprotegerin (OPG, TNFRSF11B), receptor activator of nuclear factor-κβ ligand (RANKL, TNFSF11) mRNA levels and proangiogenic vascular endothelial growth factor-A (VEGF-A) mRNA and protein release were assessed in osteoblasts from paired humeral head samples from age-matched, human OA/OP (n = 5/4) patients. Initial outgrowth and increase in cell number were significantly faster (p < 0.01) in subchondral and cortical than trabecular osteoblasts, in OA and OP, and this bone type-related differences were conserved despite consistently faster growth in OA. RUNX2/SP7 levels and TNAP mRNA and protein activity were, however, greater in trabecular than subchondral and cortical osteoblasts in OA and OP. BSP-II levels were significantly greater in trabecular and lowest in cortical osteoblasts in both OA and OP. In contrast, BGLAP levels showed divergent bone type-selective behaviour; highest in osteoblasts from subchondral origins in OA and trabecular origins in OP. We found virtually identical bone type-related differences, however, in TNFRSF11B:TNFSF11 in OA and OP, consistent with greater potential for paracrine effects on osteoclasts in trabecular osteoblasts. Subchondral osteoblasts (OA) exhibited highest VEGF-A mRNA levels and release. Our data indicate that human osteoblasts in trabecular, subchondral and cortical bone have inherent, programmed diversity, with specific bone type-related differences in growth, differentiation and pro-angiogenic potential in vitro

Learning Models of Sequential Decision-Making without Complete State Specification using Bayesian Nonparametric Inference and Active Querying

Learning models of decision-making behavior during sequential tasks is useful across a variety of applications, including human-machine interaction. In this paper, we present an approach to learning such models within Markovian domains based on observing and querying a decision-making agent. In contrast to classical approaches to behavior learning, we do not assume complete knowledge of the state features that impact an agent's decisions. Using tools from Bayesian nonparametric inference and time series of agents decisions, we first provide an inference algorithm to identify the presence of any unmodeled state features that impact decision making, as well as likely candidate models. In order to identify the best model among these candidates, we next provide an active querying approach that resolves model ambiguity by querying the decision maker. Results from our evaluations demonstrate that, using the proposed algorithms, an observer can identify the presence of latent state features, recover their dynamics, and estimate their impact on decisions during sequential tasks