A new vapor pressure equation for pure substances

A new simple equation for prediction of vapor pressure of pure substances is proposed. The equation which has the Clausius-Clapeyron (C-C) equation form consists of three parameters: critical temperature, critical pressure and normal boiling point. Experimental data for organic and inorganic substances have been used to calculate equation parameters in the boiling point ranges from 169.45 to 457.55 K and critical temperature ranges from 282.75 to 699.15 K. Comparison of proposed equation estimation results with experimental data shows that the new equation has minor average error. The new equation is also 68 percent more accurate than the C-C equation     

Optimal sparse solution for fluorescent diffuse optical tomography: theory and phantom experimental results

We present a method to accurately localize small fluorescent objects within the tissue using fluorescent diffuse optical tomography (FDOT). The proposed method exploits the localized or sparse nature of the fluorophores in the tissue as a priori information to considerably improve the accuracy of the reconstruction of fluorophore distribution. This is accomplished by minimizing a cost function that includes the L1 norm of the fluorophore distribution vector. Experimental results for a milk-based phantom using a fiber-based cw FDOT system demonstrate the capability of this method in accurately localizing small fluorescent objects deep in the phantom.     

Imidazolium-based Alkylsulfate Ionic Liquids and Removal of Sulfur Content From Model of Gasoline

Two ionic liquids, butyl-methyl-imidazolium octylsulfate, [BMIM][OCSO₄], and ethyl-methyl-imidazolium ethylsulfate, [EMIM][EtSO₄], were demonstrated to be effective for the removal of aromatic sulfur compounds such as benzothiophene and thiophene from model of gasoline. Organic compounds with higher aromatic π-electron density were favorably adsorbed by using ionic liquids. It was shown that the extractive ability of the alkylsulfate IL_s was dominated by the structure and size of cation and anion. The cation and anion structure and size of ionic liquids are important factors affecting the absorption capacity for aromatic compounds. It is found that [BMIM][OCSO₄] has the best effect on the selective removal of sulfur compounds from gasoline. Sulfur extractive selectivity for specified IL followed the order BT>T>3-MT>2-MT.     

A new 1.4-GHz soil moisture sensor

Moisture content determination in agriculture and civil engineering is a common process which needs special sensors with high accuracy, durability and compatibility with the measurement environment. A new 1.4-GHz soil moisture sensor using microstrip transmission line is presented. The proposed sensor consists of two separate parts: (1) A sensor head, which is a microstrip transmission line to be placed in the soil, and (2) an electronic transceiver which sends a sinusoidal wave into the sensor head at one end of the transmission line, and receives the traveled wave from the other end. Transmitter is basically a Colpitts oscillator, and the receiver is a phase detector that measures the phase shift due to velocity variation caused by the moisture content of soil. At a certain frequency, the velocity of a microwave traveling through a media depends on the permittivity of that media. The proposed sensor is implemented and tested on one sample of typical soil. The main advantages of the proposed sensor are its high accuracy, quickness of measurement, its low cost and ease of implementation. Since the sensor has low power consumption, it can be recommended for low-power applications such as wireless sensor network.     

Clinical implications of skeletal muscle blood-oxygenation-level-dependent (BOLD) MRI

Blood-oxygenation-level-dependent (BOLD) contrast in magnetic resonance (MR) imaging of skeletal muscle mainly depends on changes of oxygen saturation in the microcirculation. In recent years, an increasing number of studies have evaluated the clinical relevance of skeletal muscle BOLD MR imaging in vascular diseases, such as peripheral arterial occlusive disease, diabetes mellitus, and chronic compartment syndrome. BOLD imaging combines the advantages of MR imaging, i.e., high spatial resolution, no exposure to ionizing radiation, with functional information of local microvascular perfusion. Due to intrinsic contrast provoked via changes in hemoglobin oxygen saturation, it is a safe and easy applicable procedure on standard whole-body MR devices. Therefore, BOLD MR imaging of skeletal muscle is a potential new diagnostic tool in the clinical evaluation of vascular, inflammatory, and muscular pathologies. Our review focuses on the current evidence concerning the use of BOLD MR imaging of skeletal muscle under pathological conditions and highlights ways for future clinical and scientific applications.     

Dispersant-assisted low frequency electrophoretically deposited TiO2 nanoparticles in non-aqueous suspensions for gas sensing applications

The effect of dispersant on deposition mechanism of TiO₂ nanoparticles at 1 Hz under non-uniform AC fields was investigated. It was found that by adding Dolapix to suspension, deposition pattern is drastically changed enabling particles to enter the gap leaving the electrodes intact. Using low frequency AC electrophoretic deposition technique in the presence of dispersant, we succeeded in fabricating gas sensor in less than 2 min. Gas sensing measurements were performed in the temperature range of 450–550 °C. The results explained that the sensor has good stability in time and repeatability performance toward high response. The maximum sensitivity of about 180 for the TiO₂ nanoparticles sensor is observed with 47 ppm NO₂ gas and the response and recovery times is about 60–150 s. The optimum temperature of the gas sensor was obtained in 450 °C where sensor showed a linear trend up to 50 ppm of NO₂ gas. This sensing behavior in un-doped TiO₂ as NO₂ sensor can be mainly ascribed to the porous structure of the sensing film and its good contacts to the substrate and electrode assembly.     

Transient model of oxygen-starved proton exchange membrane fuel cell for predicting voltages and hydrogen emissions

Transfer (crossover) leaks initiated by the chemical deterioration of the PEM and the resulting performance degradation has been previously identified as one the primary life-limiting factors in fuel cells. The leaks result in reduced oxygen levels in affected cells, where a secondary factor intimately related to this is high hydrogen emissions in the cathode exhaust when some cells operate in fully-oxygen-starved conditions. This paper builds on previous work that developed a unified fuel cell model that predicts cell voltage behavior under driving (normal) and driven (oxygen-starved) conditions, where this latest analysis now explicitly includes hydrogen pumping and emissions release when operating under oxygen-depleted conditions. In addition to considering diffusion effects and electrochemical effects, the model tracks the evolution of hydrogen in the cell cathode when no oxygen remains to generate water. The voltage response of the model under normal (non-starved) conditions is first validated for steady-state and transient (current step-change) conditions against previously published experiments, and then the model is used to simulate the cell voltage and stack hydrogen emissions behavior measured from three different commercially available fuel cell stacks. In the first fuel cell stack, a 9-cell commercial short stack, only one cell was fully oxygen-starved. Excellent agreement is seen between the measured and simulated hydrogen release concentrations (where air injection was used downstream of the stack to ensure adequate oxygen levels for measurement with a catalytic hydrogen sensor and to condense water vapor in the exhaust), where the role of hydrogen pumping is seen to contribute significantly to the release behavior. The first fuel cell stack is then used transiently in comparison with testing performed where the hydrogen injection level in the cell is changed quickly, where the model gives good agreement with the measured emission response and cell voltage behavior. Further comparisons with test data from a second and third 10-cell commercial short stack models operated with stack inlet hydrogen injection show good agreement with measured emissions onset versus current, where the observed threshold of starvation and emissions occurs a few percent sooner in the third model than the simulation, but the overall behavior is well predicted.     

Prestressing wire breakage monitoring using sound event detection

Detecting prestressed wire breakage in concrete bridges is essential for ensuring safety and longevity and preventing catastrophic failures. This study proposes a novel approach for wire breakage detection using Mel-frequency cepstral coefficients (MFCCs) and back-propagation neural network (BPNN). Experimental data from two bridges in Italy were acquired to train and test the models. To overcome the limited availability of real-world training data, data augmentation techniques were employed to increase the data set size, enhancing the capability of the models and preventing over-fitting problems. The proposed method uses MFCCs to extract features from acoustic emission signals produced by wire breakage, which are then classified by the BPNN. The results show that the proposed method can detect and classify sound events effectively, demonstrating the promising potential of BPNN for real-time monitoring and diagnosis of bridges. The significance of this work lies in its contribution to improving bridge safety and preventing catastrophic failures. The combination of MFCCs and BPNN offers a new approach to wire breakage detection, while the use of real-world data and data augmentation techniques are significant contributions to overcoming the limited availability of training data. The proposed method has the potential to be a generalized and robust model for real-time monitoring of bridges, ultimately leading to safer and longer-lasting infrastructure.     

Building agent teams using an explicit teamwork model and learning

Multi-agent collaboration or teamwork and learning are two critical research challenges in a large number of multi-agent applications. These research challenges are highlighted in RoboCup, an international project focused on robotic and synthetic soccer as a common testbed for research in multi-agent systems. This article describes our approach to address these challenges, based on a team of soccer-playing agents built for the simulation league of RoboCup—the most popular of the RoboCup leagues so far.To address the challenge of teamwork, we investigate a novel approach based on the (re)use of a domain-independent, explicit model of teamwork, an explicitly represented hierarchy of team plans and goals, and a team organization hierarchy based on roles and role-relationships. This general approach to teamwork, shown to be applicable in other domains beyond RoboCup, both reduces development time and improves teamwork flexibility. We also demonstrate the application of off-line and on-line learning to improve and specialize agents' individual skills in RoboCup. These capabilities enabled our soccer-playing team, ISIS, to successfully participate in the first international RoboCup soccer tournament (RoboCup'97) held in Nagoya, Japan, in August 1997. ISIS won the third-place prize in over 30 teams that participated in the simulation league.     

On estimation of uncertainties in analog measurements [of powersystems]

A range of measurements is obtained from a substation that spans peak-load and light-load conditions and includes bus voltages, phase angles and line flows. Then the redundancies in measurements are used to formulate several functions relating these measurements to their attending errors. Minimization of these functions yields the required corrective coefficients. Specifically, the instruments, their arrangement and their characteristics for the 69/13.2 kV South Athens substation of the Athens Utilities Board, are described. Then an error analysis is presented showing the various sources of random uncertainties in the analog measurements. Next the redundancies in the measurements are used to establish several functions whose minimizations provide the required corrective coefficients. This is followed by estimation of propagation of random uncertainties and evaluation of the systematic errors