Socioeconomic Patterns of Twitter User Activity

Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their successful collection. In this study, we use data from social media to expose how behavioral patterns in different socioeconomic groups can be used to infer an individual’s income. In particular, we look at the way people explore cities and use topics of conversation online as a means of inferring individual socioeconomic status. Privacy is preserved by using anonymized data, and abstracting human mobility and online conversation topics as aggregated high-dimensional vectors. We show that mobility and hashtag activity are good predictors of income and that the highest and lowest socioeconomic quantiles have the most differentiated behavior across groups.     

Middle-ear circuit model parameters based on a population of human ears

Middle-ear circuit model parameters are selected to produce overall magnitude and phase agreement with pressure to stapes velocity transfer function measurements made on 16 human temporal bones, up to approximately 12 kHz. The circuit model, which was previously used for the cat, represents the tympanic membrane (TM) as a distributed parameter acoustic transmission line, and ossicular chain and cochlea as a network of lumped circuit elements. For some ears the TM transmission line primarily affects the magnitude of the response, while for others it primarily affects the phase. Model responses also compare favorably with velocity ratio data between the umbo and stapes footplate as well as between the umbo and incus, and exhibit similar characteristics to three previous input impedance measurements, including two from living ears. Similarities are also shown between the model magnitude and adjusted pressure to stapes velocity measurements from living ears, suggesting that the model may suitably approximate the behavior of living ears. In addition to fitting individual measurements, a set of parameters is selected to produce agreement with the mean of the 16 measurements up to 10 kHz, to allow the main features of the ensemble to be reproduced from a single parameter set.     

Development of capacitance-based and impedance-based wireless sensors and sensor nodes for structural health monitoring applications

A field demonstration of a new and hybrid wireless sensing network paradigm for structural health monitoring (SHM) is presented. In this paradigm, both power and data interrogation commands are conveyed via a mobile agent that is sent to each sensor node to perform individual interrogations, which can alleviate several limitations of traditional sensing networks. This paper will discuss such prototype systems, which will be used to interrogate capacitive-based and impedance-based sensors for SHM applications. The capacitive-based wireless sensor node is specifically built to collect peak displacement measurements. In addition, a wireless sensor node for collecting electromechanical impedance data has also been developed. Both sensor nodes are specifically designed to accept various power sources and to be wirelessly triggered on an as-needed basis so that they can be used for the hybrid sensing network approach. The capabilities of these miniaturized and portable devices are demonstrated in the laboratory and the field, which was performed at the Alamosa Canyon Bridge in southern New Mexico.     

Investigation of the electrode effects in mixed potential type ammonia exhaust gas sensors

Mixed potential ammonia exhaust gas sensors provide a high capability for applications in harsh environments when appropriate means are conducted to stabilize the electrodes catalytic activity. The discussed sensor utilizes oxygen ion conducting yttria stabilized zirconia and gold electrodes, one of which is covered with an SCR active film to establish ammonia sensitivity and selectivity. The used SCR catalyst is well-known and developed especially for ammonia SCR exhaust gas aftertreatment systems. Electrochemical half-cell measurements vs. a Pt reference indicate that both electrode configurations are electro catalytically active for ammonia oxidation, but the activity is more pronounced at the additional SCR catalyst layer. The results of the half-cell tests including ammonia dependent voltage and oxygen interference agree very well with the performance of the ammonia sensor device. The behavior of the catalyst electrode cover is characterized separately by activity measurements of powders. The sensing mechanism combines electrochemical reactions at the three phase boundary and chemical reactions at the catalyst material.     

Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of São Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model.     

Sorption of methane and carbon dioxide mixtures in Polish hard coals considered in terms of adsorption-absorption model

The measurements of gas mixture sorption of CO₂ + CH₄ (50%:50%) on hard coals were carried out using an original measurement method. The accuracy of the experimental data was controlled by using a gas chromatograph. Experimental sorption isotherms of individual gases were used for the simulation of sorption of their mixtures. A good agreement was obtained between the simulated and experimental data.     

Cross-calibration of participatory sensor networks for environmental noise mapping

Participatory measurements appear as a promising technique for performing noise mapping and monitoring. However, the confidence in the quality of raw data collected through participatory measurements controls the faithfulness of the output noise maps. In this paper, a cross-calibration method is proposed, which aims at both selecting the best candidate sensors and improving the furnished raw data. The method rests upon four steps: (i) an outlier detection, (ii) the crowd sensors-based correction, (iii) a fixed sensors-based correction, and (iv) the L_den estimation. The efficiency of the approach for different characteristics of the network of mobile sensors is evaluated on its ability to reconstruct an artificial reference sound field, which consists of the one-month L_10s evolution, on a twenty streets network. The main conclusions are: (i) the systematic errors of the sensors can be efficiently corrected by a cross-calibration method, and thus do not affect the L_den estimation, (ii) the fixed sensor network helps estimating the average error of the network of mobile sensors, (iii) the dispersion in an individual sensor measurements, which is due for example to the operator, stands for a much more critical concern and should be flagged by a rigorous outlier detection method, as the one proposed in this paper, (iv) although individual measures are improved by the proposed cross-calibration, some errors remain on the L_den estimation because of the shortness of the collected samples, (v) increasing the number of sensors does not improve the L_den estimation as long as individual measurements dispersions remain too large.     

XPS study on the correlation between chemical state and oxygen-sensing properties of an iron oxide thin film

We have studied the correlation between the chemical state and the oxygen-sensing properties of an iron oxide thin film using a setup that allows simultaneous sensor resistance measurements and X-ray photoelectron spectroscopy (XPS) data acquisition. The gas exposures were performed at the highest operating pressure of the XPS spectrometer at a controlled sample temperature which allows direct comparison between the sensor response and the chemical state of the surface. The iron oxide film was modified by a sequence of argon ion sputtering steps and the induced changes in the chemical state, resistance, and sensitivity to oxygen were investigated. The sputtering was found to reduce the iron from the Fe³⁺ to the Fe²⁺ state and to decrease the sensor resistance. The measured sensitivity to oxygen first increased by a factor of two but then collapsed to its original level. The mechanism for oxygen sensing was found to be filling of the oxygen vacancies in the lattice. The effect of the sputtering on the resistance and sensitivity could be explained first with an increase in the density of oxygen vacancies and then, as the iron became more reduced, with an increase in the p-type conductivity.     

Phase-sensitivity measurement of a 10-sensor array with erbium-doped fiber amplifier telemetry

We report what are believed to be the first measurements of the phase sensitivity of a fiber sensor array using multiple low-gain remotely pumped amplifiers with an interferometric sensor inserted. The measured phase sensitivities for individual rungs average 5.7 microrad(rms)/ radicalHz and exhibit no dependence on rung number, in agreement with predictions.     

Empirical analysis of individual popularity and activity on an online music service system

Quantitative understanding of human behaviors supplies basic comprehension of the dynamics of many socio-economic systems. Based on the log data of an online music service system, we investigate the statistical characteristics of individual activity and popularity, and find that the distributions of both of them follow a stretched exponential form which interpolates between exponential and power law distribution. We also study the human dynamics on the online system and find that the distribution of interevent time between two consecutive listenings of music shows the fat tail feature. Besides, with the reduction of user activity the fat tail becomes more and more irregular, indicating different behavior patterns for users with diverse activities. The research results may shed some light on the in-depth understanding of collective behaviors in socio-economic systems.     

Networks behind the morphology and structural design of living systems

Technological advances in imaging techniques and biometric data acquisition have enabled us to apply methods of network science to study the morphology and structural design of organelles, organs, and tissues, as well as the coordinated interactions among them that yield a healthy physiology at the level of whole organisms. We here review research dedicated to these advances, in particular focusing on networks between cells, the topology of multicellular structures, neural interactions, fluid transportation networks, and anatomical networks. The percolation of blood vessels, structural connectivity within the brain, the porous structure of bones, and relations between different anatomical parts of the human body are just some of the examples that we explore in detail. We argue and show that the models, methods, and algorithms developed in the realm of network science are ushering in a new era of network-based inquiry into the morphology and structural design of living systems in the broadest possible terms. We also emphasize that the need and applicability of this research is likely to increase significantly in the years to come due to the rapid progress made in the development of bioartificial substitutes and tissue engineering.     

Measurement of Wavefront Aberration of Human Eye Using Talbot Image of Two-Dimensional Grating

The measurement of human ocular aberration is now frequently performed because of the increase in refractive surgery on the human cornea. The Hartmann-Shack (H-S) wavefront sensor is considered to be the most useful wavefront sensor, and a calculation method for wavefront aberration has been established. New methods of measuring wavefront aberrations of human eyes, using the Talbot image of a two-dimensional grating as a wavefront sensor and local shift of the Talbot image to calculate tilt of the wavefront are shown. The shift of the Talbot image was determined by comparing the phases of fundamental spatial frequency between the grating and the local patch of the Talbot image by Fourier transformation. The actual experiment was performed using a modified commercially available wavefront analyzer. Using these methods, Talbot images were obtained from model eyes and a human eye, and wavefront shapes were successfully reconstructed. Wavefront aberrations can be measured even when the obtained image is degraded by defocusing or scattering.     

Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models

Behavioral and ecological studies would benefit from the ability to automatically identify species from acoustic recordings. The work presented in this article explores the ability of hidden Markov models to distinguish songs from five species of antbirds that share the same territory in a rainforest environment in Mexico. When only clean recordings were used, species recognition was nearly perfect, 99.5%. With noisy recordings, performance was lower but generally exceeding 90%. Besides the quality of the recordings, performance has been found to be heavily influenced by a multitude of factors, such as the size of the training set, the feature extraction method used, and number of states in the Markov model. In general, training with noisier data also improved recognition in test recordings, because of an increased ability to generalize. Considerations for improving performance, including beamforming with sensor arrays and design of preprocessing methods particularly suited for bird songs, are discussed. Combining sensor network technology with effective event detection and species identification algorithms will enable observation of species interactions at a spatial and temporal resolution that is simply impossible with current tools. Analysis of animal behavior through real-time tracking of individuals and recording of large amounts of data with embedded devices in remote locations is thus a realistic goal.     

Performance of YSZ oxygen sensors protected by electrochemical filters

The performance of nernstian Yttria Stabilized Zirconia (YSZ) oxygen sensors can be seriously affected when in contact with some aggressive industrial furnace atmospheres. Longer life time can be obtained for sensors protected with electrochemical filters. The most relevant parameters determining the protected sensor performance are the ratio between the sensor and the filter oxygen electrochemical permeabilities, and the volume of the electrode protection chamber. The ratio between materials electrochemical permeabilities determines the sensor oxygen activity applicability domain and the deviation between the effective (steady state) and desirable (theoretical) sensor reading. The protected chamber volume will influence the time response behavior. Theoretical predictions on sensor performance are compared with experimental data obtained for protected YSZ sensors conceived for moderately oxidizing atmospheres (1 Pa<Po₂<21 kPa). Two different cases are studied including one single phase and one composite (ionic + electronic conductor) electrochemical filter. Paper presented at the 1st Euroconference on Solid State Ionics, Zakynthos, Greece, 11–18 Sept. 1994.     

Spatiotemporal Patterns of Urban Human Mobility

The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples’ visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is sufficient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location. Moreover, we indicate the limitations of the proposed model and discuss open questions in the current state of the art statistical models of human mobility.     

Lateral interactions and nonequilibrium in adsorption and desorption. Part 1. Experimental results for (2 × 2)-(3O + NO)/Ru(0 0 1)

Extending earlier vibrational spectroscopy and thermal desorption measurements on this system, its geometrical structure and its adsorption/desorption kinetics have been investigated in detail. The adsorption and desorption of NO proceed, with little influence on the 3O template, on its (2 × 2) lattice of empty hcp sites. The desorption kinetics, with splitting of the TPD spectra into two peaks, are far from the expected behavior for independent sites. Also, the vibrational band structure shows dispersion beyond dipole-dipole interactions. So, despite the quite large NO-NO distance and their screening by the O atoms, there is clear evidence for static and dynamic lateral interactions which should be extractable from the data. A qualitative analysis suggests that these interactions are due to elastic coupling between the positions and vibrations, respectively, of the O and NO adsorbates. However, quantitative conclusions cannot be drawn directly as the kinetic data cannot be interpreted in a quasiequilibrium approach, as would be the normal procedure, due to the presence of strong nonequilibrium effects. The lack of internal equilibration is presumably caused by slow diffusion. The results are sufficiently complete and detailed to justify the effort of theoretical modeling with the aim to quantitatively describe both the lateral interactions and the nonequilibrium effects.     

On the heating modulation frequency dependence of the photopyroelectric signal in experiments for liquid thermal characterization

A pyroelectric sensor can be seen as a layered system consisted on a pyroelectric material sandwiched between two thin metal layers acting as electrical contacts for measurements of the voltage drop that can be induced by heating. This kind of sensor can be used as a detector of electromagnetic radiation but also for thermal characterization of materials using the photopyroelectric technique. In this work we perform a theoretical analysis based in the so-called thermal wave approach to show that, when this sensor is heated periodically by the absorption of intensity modulated light by one of the metalized surfaces, while the other metal surface is in contact with a liquid sample, the resulting pyroelectric voltage signal amplitude enhances respecting the one resulting from the bare sensor, for certain values of the modulation frequency. This contradicts the intuitively expectation based in the assumption that the sample provides a new channel for heat conduction, thereby decreasing the pyroelectric temperature. We will show that the back and forth propagation and the superposition of thermal waves through the metal coatings must be taken into account in order to explain the observed behavior. The proposed model was experimentally tested for water and glycerin samples, and using a polyvinylidene difluoride (PVDF) polymer film, with Ni–Cu metal electrodes, as a pyroelectric sensor.     

Cold preservation of isolated hepatocytes in UW solution: experimental studies on the respiratory activity at 0 degrees C

To date, little attention has been paid to the role of the gas milieu in preservation solutions and its effect on cell viability. Dissolved O2 in the preservation media may be an important parameter to consider. In this study we polarographically measured the O2 concentration in air-equilibrated UW solution at 0 degrees C, as well as the respiratory activity of isolated hepatocytes cold-preserved in this solution up to 72 hours. To perform measurements at 0 degrees C, it was first necessary to characterize the sensor behavior at low temperatures. We verified that the sensor response is still linear at this temperature but the rate of response is significantly slower. The O2 solubility in UW-air solution at 0 degrees C was determined using a modified physical method and it was 410 microM O2, which, as expected, is lower than the solubility in water at the same temperature (453 microM O2). Isolated hepatocytes cold-stored in UW-air solution retained a measurable respiratory activity during a period of 72 hours. The O2 consumption rate was 0.48 +/- 0.13 nmol/O2/min/10(6) cells, which represents 1% of the control value at 36 degrees C (61.46 +/- 14.61 nmol/O2/min/10(6) cells). The respiratory activity and cell viability were well maintained during the preservation period. At present, preservation conditions need to be improved for cells to remain functionally active. Dissolved O2 may be required for energy re-synthesis but it also leads to an increment in reactive oxygen species. The O2 concentration in the preservation solution should be carefully controlled, reaching a compromise between cell requirement and toxicity.     

Separating internal and external dynamics of complex systems

The observable behavior of a complex system reflects the mechanisms governing the internal interactions between the system's components and the effect of external perturbations. Here we show that by capturing the simultaneous activity of several of the system's components we can separate the internal dynamics from the external fluctuations. The method allows us to systematically determine the origin of fluctuations in various real systems, finding that while the Internet and the computer chip have robust internal dynamics, highway and Web traffic are driven by external demand. As multichannel measurements are becoming the norm in most fields, the method could help uncover the collective dynamics of a wide array of complex systems.     

Training Data Selection and Optimal Sensor Placement for Deep-Learning-Based Sparse Inertial Sensor Human Posture Reconstruction

Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario from the accumulated imbalanced motion dataset with sufficient information. We formulate the data selection into an optimization problem to obtain continuous and IID data segments, which comply with a small reference dataset collected from the target scenario. A two-step heuristic algorithm is proposed to solve the data selection problem. On the other hand, the optimal sensor placement problem is studied to exploit most information from partial observation of human movement. A method for evaluating the motion information amount of any group of wearable inertial sensors based on mutual information is proposed, and a greedy searching method is adopted to obtain the approximate optimal sensor placement of a given sensor number, so that the maximum motion information and minimum redundancy is achieved. Finally, the human posture reconstruction performance is evaluated with different training data and sensor placement selection methods, and experimental results show that the proposed method takes advantages in both posture reconstruction accuracy and model training time. In the 6 sensors configuration, the posture reconstruction errors of our model for walking, running, and playing basketball are 7.25°, 8.84°, and 14.13°, respectively.