Thus, functional GO assignment for Biological Process indicated that 3% of the contigs isotigs were grouped under stress stimuli response, 2% in development processes and an addi tional 4% in other biological and metabolic processes. These categories were of our particular interest consid ering that one of the primal objectives of this transcrip tome study was to provide information leading to the identification of biotic stress responsive genes. From the number of transcripts to which a defense role was assigned, more than half were associated with bacterial infection and jas monic acid regulation, including many JA biosynthetic and JA responsive genes. The overall perspective obtained from the above infor mation is that grain amaranth possesses a diverse arsenal of genes to resist pathogen infection and insect herbivory, the majority of which are reported for the first time in this species.

These include genes potentially involved in oxalate and phytoecdysteroid synthesis, which are believed to be effective defensive weapons in amaranth and other species. The implementation of a relatively robust defense response was somewhat unexpected, at least against insect herbiv ory, considering that the unusually high tolerance to defoliation we have observed in A. hypochondriacus plants, might be expected to exempt this spe cies from an investment in metabolically costly inducible defense responses.

The nature of the pathogen resistant Entinostat genes isolated was also complex, and included a whole gamut of bacterial and fungal elicitor induced and pathogenesis related pro teins, extracellular receptors similar to those involved in elicitor induced defense responses, proteases, transcrip tion factors and enzymes involved in reactive oxy gen species generation detoxification. Also important from our perspective were genes poten tially involved in compensatory photosynthesis, carbohy drate re localization and regulation synthesis of phytohormone levels, possibly related to the increased ramification observed in grain amaranth plants as a response to defoliation caused by insect herbivory and or mechanical damage. Many of the genes identified can be used for studying unrelated processes. For example, the analysis of phytohormone related genes, in combination with those showing homology with flowering genes is being pursued to gain an insight of the genetic mechanisms responsible for the several symptoms produced by phytoplasm infection of grain amaranth in the field, including phyllody. Transcriptome comparison between A. hypochondriacus and A. tuberculatus The publicly available raw transcriptomic 454 pyro sequencing data generated for A.

Moreover, SPR phenomena are also found in noble metal nanoparticles, namely the localized surface plasmon resonance (LSPR).Researchers have combined the advantages of optical fiber sensors and the performance of LSPR to improve the sensitivity [21,22]. Noble metal nanoparticles are decorated on the surface on the sensing fiber, and a localized surface plasmon resonance occurs which is excited by the evanescent wave around the bare core. A novel class of fiber-optic evanescent-wave sensor is constructed on the basis of modification of the unclad portion of an optical fiber with self-assembled gold colloids and the colloidal gold surface is functionalized with biotin, with a detection limit for streptavidin of 9.8 �� 10?11 M [23].

To achieve stable test results, most commercial LSPR sensors use gold for the metal nanoparticles, but for the LSPR effect, silver nanoparticles are better [24]. In this paper, we provide a complete explanation of the sensor fabrication and propose a MEMS microchip in addition to describing the sensing fiber decoration in detail. Meanwhile, previous researchers always talk about the local surface plasma resonance peak wavelength variation due to the concentration of the analyte, but here we mainly focus on the intensity changes caused by the analyte absorbance, which is found to be more sensitive. There are still some unexplained details such as what is the specific difference effect of the modification and how much is the sensitivity really improved for testing the same analyte.

In this work, the evanescent field optical fiber sensor with silver nanoparticle modification has been successfully fabricated and experiments performed with different concentrations of analytes. Comparison of the same sensing fiber without any decoration and modified with silver nanoparticles for the detection of methylene blue solutions has also been presented.2.?TheoryOptical fibers transmit light on the basis of the principle of total internal reflection (TIR) as shown in Figure 1.Figure 1.Schematic representation of the light path in optical fibers.When light propagates from the core to a cladding with low refractive index n2 and the incident angle is larger than the critical angle (��i �� ��c), total reflection occurs. For the evanescent field optical sensor, the cladding is always peeled off and substituted by the absorptive analyte.

Then the refractive index of GSK-3 the cladding region should be described as n2 = n2r + jn2j, where the real part shows the transmission characteristics of the refractive index, and the imaginary part represents the absorption properties. The propagation constant can also be written as �� = ��2r + j��2j. In order to simplify the calculation process, here we consider the propagating wave near the refractive point to be the plane wave.

These sensor networks act as information infrastructures, helping to provide ubiquitous services by using the information from daily life [1�C3].Although many such wireless sensor networks (WSNs) seem to be successfully deployed and have evolved in many aspects, they continue to be networks with constrained resources in terms of limited power, memory, and computational capacities [4,5]. Power efficiency is the main concern in sensor networks; however, the Quality of Service (QoS) requirements also need to be satisfied [6]. In the study by Zhu et al., they mentioned that coverage is one of the measurements of WSN QoS and it is closely related to energy consumption [7]. In addition, nodes have limited communication capabilities, when a source node can only cover the area within its maximum transmission range [8].

Optical fiber, which has been developed for high-speed data transmission, has also been employed as a sensor for remote data monitoring of environmental conditions or physical properties [9�C11]. In optical fiber sensing systems, fiber sensor elements use light propagating along optical fibers to take measurements. For that reason, optical fiber sensors do not need secondary power supplies, although related data communications and measurement equipment may. Additionally, using optical fiber as a transmission medium allows higher speed and larger data communications.An optical fiber sensing system that could utilize the benefits that optical fiber offers to both data communications and sensing would likely resolve many existing issues.

This paper proposes two types of optical fiber sensing systems that use hetero-core spliced (HC) optical fiber sensors. These sensors can be easily manufactured by a simple cutting and fusion splicing process; they have been evaluated positively in previous research for their sensitivity and the high measurement precision [12�C15].In the review by Rathnayaka Carfilzomib and Potdar [16], transport protocols for WSNs are discussed. Due to the numerous requirements and constraints on WSNs, many standard network transport protocols such as User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) are not appropriate.To monitor sensor conditions in the system, an existing internet-standard protocol which works in the application layer of the Open Systems Interconnection (OSI) model is used. The objective of this study is to install multiple sensors into one transmission line, remotely manage them and differentiate the response from each sensor by using the Simple Network Management Protocol (SNMP).In Section 2 of this paper, details of the hetero-core optical fiber sensors, results from previous studies, and remaining issues are described.

E = [Ex Ey Ez]T, the electric field vector, D = [Dx D y D z]T, the electric displacement vector, c, the elastic coefficient matrix, g, the dielectric coefficient matrix, and e, the piezoelectric stress coefficient matrix.Figure 1.Geometry of a piezoelectric bimorph.An ESL model adopting the FSDT is adopted to describe the mechanical displacement. The displacement field of a piezoelectric bimorph based on FSDT takes on the form [17,18]:ux,y,z,t=u��(x,y,t)+z����(x,y,t)��x,y,z,t=v��(x,y,t)+z�¡�(x,y,t)wx,y,z,t=w��(x,y,t)(2)where , , denote the displacements of an arbitrary point on the mid plane z = 0, and ? denote the rotations of a transverse normal about the y and x axes, respectively. In the FSDT, the transverse shear strains are assumed to be constant with respect to the thickness coordinate.

The constant state of transverse shear strains across the thickness is a gross approximation of the true strain field, which is at least quadratic throug
Energy saving is a crucial research topic worldwide [1�C3]. Numerous studies have been conducted in Taiwan related to the energy saving potential of residential buildings under the encouragement of government policy. Their primary achievements include improving household appliances [4], high efficiency motors [5], sensor networks [6], and industrial energy-saving technology development [7]. Compared with the status existing in 2008, these studies aim to increase energy efficiency by 2% per year. Based on the energy usage baseline established in 2005, the government has stated a goal of attempting to reduce energy consumption by 20% per unit area by 2015.

The carbon emissions of Taiwan by 2020 should be lowered to the amount of 2005 by employing breakthrough technologies and policies to encourage high-energy efficient equipment [8].Before implementing energy-saving technologies and employing highly efficient equipment, the first priority Anacetrapib is to investigate energy-saving potentials. A place with high-potential indicates low energy usage efficiency. Investing in improving energy efficiency at a place with high-potential obtains a rapid return on investment and speeds up the adoption of energy-saving technology [9]. Conversely, investing in a place with low-savings potential offers a difficult return, and improvement actions will only waste money.

How to investigate energy-saving potential of buildings effectively and cost-efficiently is important for energy saving works in the future.Several methods for determining the energy-saving potentials of buildings are discussed in published research. In 1999, Carriere et al. [10] developed a simulation method for evaluating the energy-saving potential of buildings. They implemented the DOE-2 simulation software, a widely used and accepted freeware building-energy analysis program, to study the energy-saving potential of large buildings. Federspiel et al.

Light exiting in the fiber can be described using Gaussian beam formalism [14,15]. The waist of the beam is located at the end surface of the fiber, i.e., in the reflective layer L1. The diameter 2W0 of the beam in the waist is equal to the Mode-Field Diameter MFD of the fiber. The coupling loss coefficient ��(x,n) can be defined as:��(x,n)=AR(x,n)AI(1)where AI��amplitude of the beam incident on the interferometer, AR��amplitude of the beam coupled back to the fiber, n��refractive index of the medium in the interferometer, x��distance propagated by the beam in the interferometer. It can be assumed that the coupling loss coefficient �� decreases with x at the same rate as the amplitude of the Gaussian beam propagating in the interferometer, i.e.

:��(x,n)~(1+(xx0)2)?12(2)As a result of multiple reflections in the cavity, a series of beams are coupled back to the fiber. Their amplitudes can be expressed as:A1=r1AIA2=r2(1?r1)2��(2xFP,n)AIA3=r22r1(1?r1)2��(4xFP,n)AI?AM=r2M?1r1M?2(1?r1)2��(2(M?1)xFP,n)AIforM��2(3)where Ai��amplitude of i-th reflected beam, r1, r2��reflection coefficients of L1 and L2 respectively, ����coupling loss coefficient, xFP��length of the Fabry-Perot cavity. Phase difference �� between i-th and i + 1-th beam is:��=4��nxFP��(4)where �ˡ�wavelength.The complex amplitude AR of the sum of the reflected beams is given by:AR=A1+A2e?i��+����..+ANe?iN��(5)where �ġ�phase difference given by equation 4, AN��amplitude of N-th reflected beam.Because of the presence of the coupling loss coefficient �� in Ai, the amplitudes Ai decrease faster than those of the same Fabry-Perot interferometer illuminated by a plane wave.

Consequently, the number of beams effectively contributing to the interference is smaller than that in the plane wave-illuminated interferometer case.3.?Low-Coherence Fiber-Optic Fabry-Perot Anacetrapib SensorA Fabry-Perot interferometer designed for investigation of the refractive index of bioliquids should operate in the reflection mode in order to simplify the setup. The Fabry-Perot interferometer is a multibeam interferometer. However, a biosensor for investigation of the refractive index of liquids should be a low-finesse Fabry-Perot interferometer in order to obtain an interferometer with a transfer function as for two-beam interferometers. The optical-fiber Fabry-Perot interferometer has been made with the use of a conventional single mode optical fiber, simplified construction of which is shown in Figure 1.Its reflective layers have been produced by the boundaries: fiber optic��investigation sample (L1) (air in Figure 1) and investigation sample (air in Figure 1)��mirror (L2). It can be noted that any change of the investigated sample causes a change in the reflection coefficient of the Fabry-Perot mirror [14].

Specifically, the lineshape can be described by a Gaussian function in low-pressure regimes. At high pressure, collisions of the molecules are dominant and the lineshape is described by a Lorentzian profile. At intermediate pressure, a Voigt profile is normally used.3.?Experimental setupFigure 1 shows an schematic of the arrangement used for methane detection experiments. The main difference with a conventional spectroscopic gas sensor is the substitution of the bulky conventional gas cell with a 5.6-m-long HC-PBF. It should be noted that, despite the length of the fibre, the system can be arranged in compact form as the fibre can be coiled up.Figure 1.Experimental setup for methane detection experiments using a HC-PBF as gas cell.

As can be seen in Figure 1, light from a broadband light source, (Agilent 83437A), was launched into a Single Mode fibre (SMF). To avoid reflections, the SMF was angle cleaved and coupled to the HC-PBF using 3-axis positioners. A gap was left between the ends of the fibres to allow the gas access into the core of the HC-PBF. The other end of the HC-PBF was spliced to a SMF pigtail using a similar procedure as described in [17]. The splice attenuation was measured to be 1 dB. The transmitted power through the HC-PBF was measured using an Optical Spectrum Analyzer (OSA), (Agilent 86142A). The HC-PBF, with its open end, was placed inside an airtight chamber, as illustrated in Figure 1. Finally, a pump was used to evacuate the air from the chamber and a pressure gauge monitored the vacuum conditions inside.

The HC-PBF used in the experiments was especially designed and manufactured by the Optoelectronics Research Centre at Southampton. The fibre was fabricated Carfilzomib using a two-step stack-and-draw process
With the advances in micro-electro-mechanical system technologies, embedding system technology and wireless communication with low power consumption, it is now possible to produce micro wireless sensors for sensing, wireless communication and information processing. These inexpensive and power-efficient sensor nodes work together to form a network for monitoring the target region. Through the cooperation of sensor nodes, the WSNs collect and send various kinds of message about the monitored environment (e.g. temperature, humidity, etc.) to the sink node, which processes the information and reports it to the user.

Wireless sensor networks have a wide-range of applications, including military surveillance, disaster prediction, and environment monitoring, and thus have attracted a lot of attention from researchers in the military, industry and academic fields.In wireless sensor networks, the sensor node resources are limited in terms of processing capability, wireless bandwidth, battery power and storage space, which distinguishes wireless sensor networks from traditional ad hoc networks [15].