1 2 Quality Control in the GMA Welding ProcessThe study of qual

1.2. Quality Control in the GMA Welding ProcessThe study of quality control in the welding processes has been a subject of great interest for many researchers. The task of evaluating weld quality is not trivial, even for the experienced inspector. This is particularly true when it comes to specifying in quantitative terms what attributes of the weld affect its quality and to what extent. Different types of discontinuities have been categorized for this purpose, such as cracks, porosities, undercuts, microfissures, etc. [16]. Generally, good quality GMA welds are uniform and contain little or no artifacts on the bead surface. Furthermore, the bead width is relatively uniform along the length of the bead [17].

To achieve a standard weld quality it is fundamental to maintain continuity of welding stability and this happens when the mass and heat flow of the end of a consumable electrode to the fusion pool through the arc maintains uniformity in the transference; possible discontinuities and/or upheavals in the transference could originate weld disturbances. The stability of the short circuit gas metal arc welding process is directly related to weld pool oscillations [18]. Optimal process stability corresponds to maximum short-circuit rate, minimum standard deviation of the short-circuit rate, a minimum mass transferred per short circuit and minimum spatter loss [19�C22]. In the present work, the welding stability was evaluated using the sound pressure through the acoustic ignition frequency (AIF) and sound pressure level (SPL) signatures.


?Measurement and Experimental MethodVirtual instrumentation software [23], data acquisition card [24], welding power source [25] and the setup as shown in the Figure 1(a) were used for the acquisition of data based on arc voltage, welding current and arc sound pressure. These parameters were sampled at 20 kHz. The sound pressure was measured using the analogical output decibelimeter [26] which use a microphone 4189 with�C26 dB �� 1.5 dB, 50 mV/Pa sensitivity. The microphone Carfilzomib was located at approximately 150 mm from the weld pool. The welds were deposited on AISI 1020 (30 mm �� 200 �� 650 mm) steel plates us
Nowadays Batimastat the increasing technology of airborne sensors with their capabilities for capturing images, including those on board the new generations of Unmanned Aerial Vehicles, demands solutions for different image-based applications. Natural spectral signature classification is one of such applications because of the high image spatial resolution.

From the study, authors suggest that tuning physical layer operat

From the study, authors suggest that tuning physical layer operational parameters may increase the read rate up to a 33%. In [18] it is shown how the performance in EPC-C1G2 varies widely for different readers.Some works focus on analyzing the identification process of passive RFID systems. A relevant study is addressed by Vogt in [19], where the author characterizes the identification process of ISO-18000-6C standard [2] as a Markov chain, assuming a static scenario. The author found that the results matched an experimental evaluation using the old I-Code RFID system [20]. However, in [19] the author assumes that those tags already identified in previous frames keep on competing.

This is not the case currently, since most FSA derived protocols, including EPC-C1G2, force identified tags to withdraw from the identification process.

In [4] we study the identification performance in static scenarios, but also considering the dynamic frame-length procedure of EPC-C1G2, which is not widely implemen
Optical fibers are ideal for environmental sensing applications because of their ability to transmit optical signals to and from the sensing region without the use of free-space optics. By accessing the evanescent field, the fiber itself can be the sensing element and long interaction lengths can be achieved [1]. Microstructured optical fibers (MOFs) are particularly suited to such applications as the air spaces inside the fiber form natural cavities for locating the material to be detected.

These types of fibers have a significant advantage over conventional core-clad fibers, in that they can be fabricated from a single material, so issues involving thermal and chemical compatibility between different glasses Entinostat can be avoided [2].By tailoring both the MOF material and the geometry, the light-matter overlap can be increased to values much larger than with conventional fibers. Through varied structure geometries such as photonic band-gap fibers (PBGF) [3,4] or suspended nanowires [5�C8], the overlap between the guided light and the analyte located within the holes of the fiber can be increased significantly over that which can be obtained using multi-mode bare fibers or D-shaped fibers [9].

However, due to the relatively limited bandwidth of most PBGFs [7], the excitation and emission wavelengths must be relatively close to enable detection with the fiber. Here AV-951 we employ the suspended nanowire design [10] that provides the high evanescent overlap of a standard nanowire [11] with the large interaction length and robust handling comparable to conventional fibers.

deration of changes in node num ber, edge number and network dens

deration of changes in node num ber, edge number and network density. The signed scale free R2 plot analysis suggests that this selection has a good scale free topology fit, as the R2 value of 0. 85 indicates that the topology of the HLB response network is quite similar to most biological networks. The resulting citrus gene coexpression network contains 3,507 nodes with 56,287 edges. We next determined the robustness of our network across each dataset using the cross validation approach. We randomly left out one dataset and reconstructed the gene co expression networks using the remaining three datasets. The resulting four networks were then compared to the network based on all four datasets in terms of net work connectivity rank of each gene according to the sug gestion described elsewhere.

There were strong, highly significant connectivity Dacomitinib correlations between the network based on all four data sets and the ones reconstructed from any combination of the three datasets. This suggests a high degree of preserva tion of gene co expression patterns across the networks based on different datasets. We then analyzed in detail the characteristics of the HLB response network. First, the frequency distribution of edges for each node was determined. As shown in Figure 2, the network contains 860 Probesets that are orphan nodes, 400 Probesets that have only one interaction, and the ma jority of the nodes that have at listed in Additional file 7. The p values of the overrepre sented GO terms were listed in Additional file 5. We also performed a GO enrichment analysis for the hub genes.

We arbitrarily divided the 2,247 hubs into two categories, minor hubs and major hubs and their overre presented GO terms were summarized in Additional file 8. The major hubs have 13 overrepresented GO terms, carbohydrate metabolic process, primary meta bolic process, metabolic process, secondary metabolic process, lipid metabolic process, cellular amino acid and derivative metabolic process, cellular process, localization, transport, establishment of localization, regulation of ana least three interactions and, by following Geisler Lee et al. are called hubs in this paper. Among the 2,247 Probesets, the majority have 3 100 edges, and the remaining 345 Probesets have 101 300 interactions, while only 1% have more than 300 interac tions.

Overall, the mean number of interactions for each Probeset is 16, with the maximum of interactions being 369. Cit. 4987. 1. S1 s at represents a gene most closely related to Arabidopsis SYP71 encoding a plant syntaxin which functions as a plasma membrane associated protein transporter. Second, we performed a GO enrichment analysis for the Probesets in the HLB response network. Among 30,173 Probesets, 22,775 have the Arabidopsis gene ID as their closest orthologs or homologs. Therefore, these Probesets were assigned GO terms based on the most recent Arabidopsis GO assignment. The remaining Probesets were given three general GO terms, biological p

In the last decades, radar technology has experienced a change

In the last decades, radar technology has experienced a change in its focus. Whereas in the beginning only the detection and tracking of targets was necessary, with the advance of technology the need to obtain higher spatial resolution has emerged. Consequently, radars have evolved into more flexible devices with the ability to generate high resolution imagery for mapping purposes or target identification [3]. Radars are the most suitable sensors for a rapid and reliable recognition of targets as they can operate in scenarios where visibility is very poor, such as bad weather conditions, smoky and dusty environments, etc. Their ability to resolve targets at a long range as well as their operation under any weather conditions makes them differ from other sensors like thermal or optical ones [2].

Target recognition using radar sensors can be divided into two techniques: cooperative and non-cooperative [1]. Cooperative techniques, known as identification friend or foe (IFF), require the communication between target and radar, while non-cooperative techniques, so-called non-cooperative target identification (NCTI), do not establish any communication with them but rely on the comparison of the measured targets with a reference database. This database is usually populated with actual target measurements obtained in scheduled measurement campaigns [4]; however, it implies the collection of information from a great number of flying targets in different aspect angles and configurations and even so, the main problem lies in the fact that not all existing aircrafts may be measured.

For this reason, other methods have been deployed to populate the database. These methods include measurements in anechoic chamber and electromagnetic simulations [5]. The latter is of great interest due to its low cost and the simplicity of obtaining a vast number of CAD aircraft models for electromagnetic simulations.In Cilengitide this paper a target recognition methodology based on high resolution radar imagery is presented. Algorithms related to high resolution radar image creation and the problems found are introduced, as well as a target recognition methodology based on image cross correlation. High resolution radar image generation and target recognition processes are complex and time consuming. The goal of a NCTI system is the reliable recognition of targets in real time; therefore, studies on the computational burden of the whole process are of great interest.

These studies will make it easy to identify the computationally critical points of the system in order to previously choose an implementation platform that could perform these operations efficiently. Accordingly, the computational burden of the proposed system is revised distinguishing the complexity of image generation from the complexity of target recognition.

Each element works in a constant temperature difference (CTD) mod

Each element works in a constant temperature difference (CTD) mode. The readouts of the four sensing elements are used to deduce the flow parameters of the 2-D flow (i.e., flow speed and direction angle) using a neural network data fusion technique. Compared with previous technologies, the sensor has merits of simple structure, low cost, easy fabrication and low power consumption.Figure 1.Prototype of hot-film flow vector sensor.2.?Operation Principle and Design of the Sensor SystemThe sensor uses thermal elements serving as both Joule heater and temperature sensor so that it has a relative simple structure and low-cost fabrication.2.1. Sensing PrinciplesThe working principle of the sensor is based on the heat transfer of the heating element in a flow field [11], which forms a temperature distribution above the thermal element.

Under a constant bias power and zero flow speed, the thermal element achieves a steady-state temperature, which means the heat transfer system reaches equilibrium. When an external flow passes through the sensor, the temperature field will be deflected in the direction of the flow that results in the temperature differences among the elements according to their locations of upstream or downstream as shown in Figure 2. Temperature differences among the four elements can be detected and used to figure out the magnitude and direction of the flow.Figure 2.Temperature distribution above the surface of thermal elements.2.2. Sensing Design and SimulationFor sensing the 2-D flow in the directional range of 360��, both heating and sensing structures need to follow some requirements.

Firstly, the heating structure needs to have central symmetry so as to form a centrosymmetric temperature distribution above the sensor, specifically a circular symmetry is an optimal option for covering 360�� in all directions. Secondly, the temperature sensing structure needs to be divided into several isolated sections to detect the GSK-3 flow-induced temperature differences. For integrating the heating and sensing elements into one element, we consider the use of a round shape and divide it equally into several sections. The number of divided sections gives the number of heating/sensing elements, which also determines the number of conditioning circuits needed to operate the heating and temperature sensing.

For simplifying the operation and saving energy, the number of heating/sensing elements needs to be minimized. After overall considerations, we divide the round shape into four equal sections as shown in Figure 4, each of which is a quadrant consisting of a roundabout wire, as shown in Figures 1 and and33.Figure 3.Sensor design.Figure 4.Results of simulation under a flow with different flow directions.The sensitive area of the sensor needs to be as small as possible so as to capably detect the local flow at one point.

Being regularly active substantially improves outcome and progres

Being regularly active substantially improves outcome and progression of most chronic degenerative diseases [4]. Additionally, an active life style contributes to social participation and quality of life, as expressed in the context of the ��International Classification of Functioning, Disability, and Health�� [5]. In order to understand how PA in daily life is associated with health and functioning in older persons, such behaviour needs to be studied in free-living conditions.Contrary to young active adults, older persons perform most PA as part of every-day life activities related to work, house-holding and leisure time where energy cost is much lower than exercise, such as running. Thus, for many purposes, it is of more relevance to study aspects of PA in older persons, such as postural allocation and type of activity, than the energy expenditure associated with PA.

Against this background, the World Health Organisation considers that PA can be measured by its four main components, which can be abbreviated as FITT: Frequency of the activity (e.g., number of walking periods), Intensity of the activity (e.g., walking speed); Time or the duration of the bout of activity (e.g., duration of walking episodes), and the Type of activity (e.g., lying, sitting, standing, walking) [6]. Where the FITT components apply to the population at large it is reasonable to expect that the weight of the individual FITT components will vary largely for different sub-population. Hence, the daily life performance of mobility related activities (such as standing or walking) can be considered as a key construct of PA in older people.

However, since PA patterns differ so much between different populations, studies addressing PA should carefully define the key construct(s) which correspond to the specific topic and population under study.The formal definition of PA is ��any bodily movement produced by skeletal muscles that results in energy expenditure�� [7]. This definition specifically focuses on the amount and volume of PA and the energy expenditure associated with PA, and thus, a large portion of PA literature has focused on the effect of physical exercise and GSK-3 on energy consumption, mostly from the perspective of health. However, besides activity related energy expenditure, PA is of interest in terms of body posture and movement behaviour.

Assessment of PA has traditionally been done by use of questionnaires, mostly focusing on leisure time levels of PA and on energy expenditure. Questionnaires have known limitations with respects to reliability and their relationship with actual behaviour [8,9], and they do not have the potential to assess all aspects of PA [10], especially in older persons [11]. Objective, performance-based laboratory tests will neither represent the usual performance of the tested individual [12].

One simple approach to the fulfillment of this task is direct dat

One simple approach to the fulfillment of this task is direct data transmission. In this case, each node in the network directly sends sensing data to the base station. However, if the base station is remote from the sensor node, the node will soon die due to excessive energy consumption for delivering data. To solve this problem, some algorithms aimed at saving energy have been proposed [3-7].Heinzelman et al. [3] proposed an alternative clustering-based algorithm, called LEACH (Low-Energy Adaptive Clustering Hierarchy). It assumes that there exists a unique base station outside the sensor network and all the sensor nodes can communicate with this base station directly. In order to save energy, LEACH only chooses a fraction p of all sensor nodes to serve as cluster heads, where p is a design parameter that must be determined before deployment.

The rest sensor nodes join the proper clusters according to the signal strength from cluster heads. In order to share the energy load, its operation is divided into rounds which can guarantee the cluster head rotate in each round. In each round, after cluster formation phase, the cluster heads aggregate the data received from their cluster members and send the aggregated data to the base station by single hop communication, so it can sharply reduce the data needed to be transmitted to the base station.S. Lindsey et al. proposed an algorithm related to LEACH, called PEGASIS [4]. These authors noticed that for a node, within a range of some distance, the energy consumed for receiving or sending circuits is higher than that consumed for amplifying circuits.

In order to reduce the energy consumption of sensor nodes, Brefeldin_A PEGASIS uses the GREED algorithm to form all the sensor nodes in the system into a chain. According to its simulation results, the performance of PEGASIS is better than LEACH, especially when the distance between sensor network and sink node is far large.In [5], to deal with the heterogenous energy circumstance, the node with the higher energy should have the larger probability to become the cluster head. In this paper, each node must have an estimate of the total energy of all nodes in the network to compute the probability of its becoming a cluster head. As a result, each node will not be able to make a decision to become a cluster head if only its local information is known.

In this case, the scalability of this protocol will be influenced.Sh. Lee et al. proposed a new clustering algorithm CODA [6] in order to relieve the inbalance of energy depletion caused by different distances from the sink. CODA divides the whole network into a few groups based on node’s distance to the base station and the routing strategy. Each group has its own number of clusters and member nodes. CODA differentiates the number of clusters in terms of the distance to the base station.

An array type of sensor displaying independent specificity for mu

An array type of sensor displaying independent specificity for multiple targets can be an attractive platform. Fourth, portability and ease-of-use are important for on-site monitoring. In addition, automation can be a significant factor of consideration for a long-term environmental monitoring.The function of a pathogenic biosensor is to transduce receptor recognition towards the target pathogen into a detectable signal. Pathogenic sensing relies on either immunosensing or nucleic acid detection. Immunosensors are based on the interaction between antigens presented on the target cells and antibodies immobilized on surfaces. The resulting conjugates have been detected via various sensing methods, including fluorescence [5], electrical or electrochemical impedance [5,6], cantilever [7,8], quartz crystalline microbalance (QCM) [2,7], surface plasmon resonance (SPR) [5,7], and magnetoresistivity [9].

Nucleic acid-based sensors detect DNA or RNA originating from target cells. Because cells contain a low copy number of nucleic acids, the sensor generally requires a step of amplifying target nucleic acids using polymerase chain reaction (PCR) or reverse transcriptase PCR (RT-PCR). In addition, there are several intricate strategies for amplifying signals that report the hybridization between probe and target DNA. Using nanoparticles [10] and enzyme labels [11], redox probes [12-14], and intercalators [15] are among those strategies. The target DNA or RNA will also be detected using various physical sensing methods.

In general, the ultimate performance of a pathogen sensor relies on the high efficiency of biochemical reactions, high concentration of target analytes, and sensitive detection or transduction methods.Recent advances in micro- and nano-fabrication technologies have provided unique advantages for developing pathogen sensors in several respects. The sensor probe created with similar or smaller dimensions of a bacterial cell could provide high sensitivity and a low detection limit. Nanoparticles, nanotubes, nanowires, and nanomechanical devices are representative examples used as functional probes for detecting Batimastat pathogens. In addition, microfabrication technology has made it possible to integrate multiple processes in sequence for one-step sensing or in parallel for high throughput screening.

In this review, we will highlight a group of pathogen sensors developed in the last several years that have taken advantage of advanced micro- and nano-technology. This paper will focus on the principles, features, and advantages of new sensing technologies. We will also describe how the technology could enhance the sensor sensitivity and detection limit.2.?Recent Sensing Strategies for Pathogen Detection Based on MicrofluidicsOne of the main outcomes of microfabrication technology is the creation of microfluidic devices, so called labs-on-a-chip.

However, it is the biochemical and biosensor applications that ar

However, it is the biochemical and biosensor applications that are attracting piezoresistive cantilevers most. They have been used as environmental sensor [16], biosensor [17], biochemical sensor [18], in DNA sequencing [19], biomolecular force sensor [20] and immunosensor [21]. Nevertheless, the sensitivity and resolution of piezoresistive detection is generally an order of magnitude less than optical method due to low piezoresistive coefficients and the large noise. Piezoresistor cantilevers are vulnerable to thermal effects such as thermal deflection because of temperature increase by Joule heating. Thus, characterisation of Joule heating in piezoresistive microcantilevers is necessary to improve their accuracy. Recently, Chui et al.

[22] proposed a highly effective method of reducing thermal sensitivity in piezoresistive sensors by taking advantage of the dependence of the piezoresistive coefficient of silicon on crystallographic orientation.Piezoresistive microcantilevers were traditionally fabricated from single crystalline silicon substrate with the piezoresistor element created by selectively doping the substrate with a suitable dopant. However, later studies found that for MEMS piezoresistors, polysilicon offers a number of advantages over single-crystalline silicon, including the ability to be deposited on a wide range of substrates [10]. The polycrystalline silicon also exhibits piezoresistivity, but the gauge factor is much smaller than that of single crystalline.

Thus, to improve the sensitivity and resolution of piezoresistive microcantilevers, efforts have been made to use soft material cantilever or use single crystalline Carfilzomib silicon as piezoresistor to achieve high piezoresistive coefficients [23]. To this use, application of silicon dioxide as substrate and single crystalline silicon as piezoresistor was proposed. However, silicon dioxide microcantilevers fabricated from surface micromachining technology can integrate only polysilicon piezoresistors, which suffer from low piezoresistive coefficients and high noise [21]. In recent days, SOI wafers have been used to fabricate silicon dioxide microcantilevers with etched single crystalline silicon piezoresistors to improve the sensitivity and the resolution [24].

The low Young��s modulus of silicon dioxide combined with the high piezoresistive coefficients of single crystalline silicon piezoresistor presents an ideal Cilengitide solution to improve the sensitivity of piezoresistivity microcantilevers. However, silicon dioxide cantilevers have a major drawback in form of Joule heating produced by the piezoresistor encapsulated inside.

4; 0 01 M), to remove the extravidin peroxidase solution in exces

4; 0.01 M), to remove the extravidin peroxidase solution in excess.3.2. Anti-lactoferrin immobilization on Immobilon membraneThe Immobilon Ny+ Membrane was cut into approximately 1 cm2 surface area disks and 100 ��L of a 1.0 mg/mL anti-lactoferrin was directly deposited on the membrane surface. The membrane was then dried at room temperature for about 24 h and selleck stored at 4�� C.3.3. Immunosensor assemblyThe transducer Inhibitors,Modulators,Libraries consisted of an amperometric electrode for H2O2 determination, with a Pt anode and an Ag/AgCl/Cl? Inhibitors,Modulators,Libraries cathode, provided with a plastic cap filled Inhibitors,Modulators,Libraries with 0.1 M KCl solution and screwed onto the body of the electrode, at the lower end of which a dialysis membrane was positioned. The Immobilon membrane with the immobilized anti-lactoferrin overlapped the dialysis membrane.

Finally, a nylon net overlapped the latter membrane. The two membranes and the net were secured by a rubber O-ring to the plastic cap of the electrode as shown in Figure 2.Figure 2.Amperometric immunosensor for lactoferrin determination using hydrogen peroxide Inhibitors,Modulators,Libraries electrode as transducer.3.4. Determination of lactoferrin by immunosensorCompetition procedure: competition between lactoferrin biotin-avidin-peroxidase conjugated and non conjugated lactoferrin, both free in solution, for anti-lactoferrin immobilized in membrane. To this end, the Immobilon membrane, on which the anti-lactoferrin was immobilized, was fixed to the head of the amperometric electrode for hydrogen peroxide as described in Section 3.3. Before measurement, the immunosensor was dipped into a Tris-HCl buffer solution, (pH 8.0; 0.

1 M), containing 0.05 % Tween?-20 by weight and 2.5% BSA by weight (bovine albumin was used to minimize non specific absorption on the membrane). The lactoferrin sample to be determined was added in 5 mL of Tris-HCl buffer solution (pH 8.0; 0.1 M) contained in the measurement cell, together with a fixed supply of lactoferrin biotin-avidin-peroxidase Cilengitide conjugated, i.e. 20 ��L (2.0 mg/mL) of conjugated lactoferrin. The peroxidase-conjugated lactoferrin was allowed to compete with the non-conjugated lactoferrin, both free in solution, in binding with the anti-lactoferrin immobilized on the Immobilon membrane. After washing with the same buffer solution to remove all the unbound lactoferrin, the specific substrate of the enzyme, i.e.

20 ��L of H2O2 selleckchem Tofacitinib solution 1% v/v, was added to the renewed buffer solution in which the immunosensor was dipped, under stirring. The measured signal (as nA) of the transducer correlated directly with the lactoferrin concentration to be measured. In this case, the higher the concentration of non conjugated lactoferrin free in solution, the stronger the signal produced by the hydrogen peroxide. Indeed, the lower the conjugated lactoferrin bound to the antibody immobilized on Immobilon membrane, the lower the H2O2 consumed in the enzymatic reaction, and therefore the higher the signal of the H2O2 oxidized at the amperometric electrode.