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This study draws on labeling and routine activity theory to examine whether being suspended from school is associated with subsequent gang membership onset. With data from the National Longitudinal Survey of Youth 1997 (NLSY97), we estimated discrete time models that predict gang membership onset from ages 12 to 19. The results revealed that being suspended from school at one wave was associated with an increased hazard on gang membership onset at the next wave. The results also revealed that although being suspended from school during one wave increased the hazard of gang membership onset, youth who are suspended during multiple waves tended to have an even higher hazard of gang membership onset.

A steam generator serves as a power generation equipment that uses the expansive power of steam to generate electricity. The startup process of a steam generator plays an important process in a power plant to adjust its electricity generation in response to changes in load demand. As renewable generation plants increase, the levels of variability in electricity production increase. Fast startups become instrumental as they enable traditional power generation plants to provide the quantity of electricity missing when variable renewable energies cannot satisfy the load demand. The drum boiler is one of the main pieces of equipment involved in the startup process of a steam generator. However, if the startup process is carried out too fast, excessive thermal stresses may occur, thus provoking damage to the components of the drum boiler. This paper proposes a dynamic optimization methodology to synthesize operating procedures that minimize the startup time of the drum boiler while avoiding the excessive formation of thermal stresses. Since valve operations influence the time-varying behavior of the steam, dynamic simulation is needed in order to evaluate the operating procedure. The proposed algorithm is based on two important elements of two metaheuristic algorithms: the acceptance probability of the simulated annealing algorithm and the tabu search memory structures. A case study evaluates the proposed approach by comparing it against results previously published in the literature.

The increasingly tighter interdependency between natural gas and power systems because of the increasing number of natural gas-fired units for electricity production has motivated research on the operational effects associated with this coupling. To evaluate the effects of this interaction, however, the operating status of the integrated gas and electricity networks must first be estimated. Hence, this paper proposes and implements a static state estimator that assesses the operating state of integrated electricity and natural gas networks. The proposed approach is based on the equality-constrained weighted least squares algorithm, which simultaneously upgrades the estimated values of the state variables associated with both energy infrastructures for a unified solution in a single frame of reference. The effectiveness of the proposed approach is demonstrated in a multi-energy system composed of the 20-bus Belgium gas system and a 3-bus electric power system.

The increasing global use of natural gas for electric power generation has motivated the study of existing inter-dependencies between electricity and natural gas transmission networks, which are traditionally analyzed as independent systems. Within this context, a novel approach to the integrated formulation of the natural gas and electric power flow problem is proposed in this paper. This formulation is developed in a single frame of reference that allows an integrated energy flow analysis. Contrary to the current proposals for performing this type of analysis, the generator-governor characteristics, as well as the voltage and frequency dependence of loads, are incorporated into the system of equations of the electric power flow problem. This permits a better representation of the interaction between both energy infrastructures and of their actual operating conditions. The applicability of the proposed approach is demonstrated by analyzing an integrated multi-energy system composed of the IEEE 118-bus electrical system and a 15-node natural gas infrastructure.

Given the imminent threats of climate change, it is urgent to boost the use of clean energy, being wind energy a potential candidate. Nowadays, deployment of wind turbines has become extremely important and long-term estimation of the produced power entails a challenge to achieve good prediction accuracy for site assessment, economic feasibility analysis, farm dispatch, and system operation. We present a method for long-term wind power forecasting using wind turbine properties, statistics, and genetic programming. First, due to the high degree of intermittency of wind speed, we characterize it with Weibull probability distributions and consider wind speed data of time intervals corresponding to prediction horizons of 30, 25, 20, 15 and 10 days ahead. Second, we perform the prediction of a wind speed distribution with genetic programming using the parameters of the Weibull distribution and other relevant meteorological variables. Third, the estimation of wind power is obtained by integrating the forecasted wind velocity distribution into the wind turbine power curve. To demonstrate the feasibility of the proposed method, we present a case study for a location in Mexico with low wind speeds. Estimation results are promising when compared against real data, as shown by MAE and MAPE forecasting metrics.

Nowadays, the global energy system is in a transition phase, in which the integration of renewable energy is among the main requirements for attenuating climate change. Wind power is a major alternative to supply clean energy; hence, its widespread penetration is being pursued in all end-use sectors. In particular, it is currently noteworthy to analyze the feasibility of deploying small-scale wind power technology to provide cleaner and cheaper energy in the residential sector. As a first step, a technical assessment must be carried out to provide crucial information to intensive energy consumers, providers of small-scale wind power technology, electric energy distribution utilities, and any other party, to help them decide whether or not to deploy small-scale wind turbines. With this aim, we propose to perform such an analysis using a suitable probabilistic paradigm to solve complex decision-making problems with uncertainty, namely Bayesian Intelligence, since wind resources and energy demands are intermittent variables, properly characterized by probability distribution functions. Then, the problem of determining the technical feasibility can be formulated as an investigation into whether or not small-scale wind turbine technology can produce enough energy to cover the excess demand of intensive energy residential consumers to get off high-priced tariffs. For this purpose, we introduce a novel model based on probabilistic reasoning to assess the suitability of small-scale wind turbine technology to produce the said energy, taking into consideration the availability of wind resources and the energy pricing structure. To demonstrate the usefulness and performance of the proposed model, we consider a case study of deploying 5 and 10 kW wind turbines and analyze the feasibility of their implementation in Mexico, where the energy pricing structure and scattered wind resource availability pose difficult challenges.

The goal of digital image segmentation is to assign different labels to different objects present in a digital image or volume. A wide variety of sources have been successfully segmented by the traditional fuzzy segmentation algorithm on several different applications. However, the traditional approach of the fuzzy segmentation algorithm usually does not work well when dealing with objects whose materials are characterized by complex textures that cannot be accurately represented by simple fuzzy affinity functions of the traditional fuzzy segmentation algorithm. In this paper, we extend the fuzzy segmentation algorithm to use adaptive textural affinity functions for segmenting these objects in 2D images. These adaptive affinity functions define their optimal appropriate neighborhood size on execution time, by computing texture descriptors surrounding the seed spells, according to the characteristics of the texture being processed. The algorithm then segments the image with the appropriate neighborhood that may be different for each object. We performed experiments on mosaic images that were composed using images from the Brodatz database, and compared our results with the ones produced by a recently published texture segmentation algorithm, showing the applicability of our method.

A common problem that arises in wind generation systems is the dependence on electromechanical sensors to measure and control the propeller speed. Normally, these sensors increase the system complexity, total cost, and are sensitive to environmental constrains. To overcome the aforementioned problems, in this paper, a sensorless technique is proposed to estimate the speed and position of the propeller. The speed and position estimator are composed of a second-degree sliding mode estimator in an integral proportional-structure. This estimator makes it possible to close the speed control loop of the wind generation system in order to extract the largest amount of energy from the wind to be injected into the grid or to an isolated load, in a concept of microgrids or distributed generation. The speed estimator is compared with two estimators to evaluate its behavior. The excellent performance of the proposed sensorless algorithm is demonstrated by simulations carried out in MATLAB/Simulink.

This paper presents a single-phase standalone inverter with an integrated control structure. The integrated control structure has the function of allowing the implementation of a sliding mode control in a single-phase standalone inverter, the inverter is limited to a switching frequency established by the devices of power. To implement the sliding mode control, the inverter analysis is also required in state variables and the equalization of the limited switching frequency with the PWM averaged modulator. The integrated control structure capable to maintain the waveform of the output voltage, amplitude, and frequency, all of this before load variations and non-linear loads. The results of the integrated control structure are compared with a P-Resonant controller. In the case of a constant load, a ripple is observed in the waveform of the output voltage with a maximum magnitude of 4 V and with the P-Resonant controller, a constant ripple of 5 V. Total Harmonic Distortion of voltage (THD v ) is 3.16% when the nominal power is obtained at full load; with the P-Resonant control, 〖 THD v is 8.99%. With non-linear loads, better performance is obtained with the integrated control structure.

The electronic medical record (EMR) presents an opportunity to standardize patient data collection based on quality guidelines and conduct practice-based research. We describe the development of a customized EMR “toolkit” that standardizes patient data collection with hundreds of discrete fields that supports Best Practices for treating patients with memory disorders. The toolkit also supports practice-based research. We describe the design and successful implementation of a customized EMR toolkit to support Best Practices in the care of patients with memory disorders. We discuss applications, including quality improvement projects and current research initiatives, using the toolkit. This toolkit is being shared with other departments of Neurology as part of the Neurology Practice-Based Research Network. Data collection is ongoing, including longitudinal follow-up. This toolkit will generate data that will allow for descriptive and hypothesis driven research as well-quality improvement among patients seen in a memory clinic.

Wind turbine generators are highly desirable to operate autonomously throughout the wind speed range below hurricane conditions. A key requirement to achieve this goal is to be able to control the wind turbine speed using a full-scope feedback control scheme. Currently, wind turbine speed is controlled by modulating the angular position of the rotor blades to catch the required amount of kinetic energy of the wind to produce the desired rotational speed. Typically, conventional PI controllers modulate the blades angular position only for wind speeds in the range from 12 m/s through 25 m/s. This paper introduces a fuzzy speed controller to control autonomously the turbine rotational speed in the whole wind speed range from 0 m/s throughout 30 m/s. After presenting several key concepts about small-scale wind turbines, the design of the fuzzy speed controller based on a TSK fuzzy system is introduced. In this regard, the proposed fuzzy speed controller intelligently extends the scope of control below nominal speed and above trip conditions.

Nowadays, an eco-friendly way to satisfy the high-energy demand is by the exploitation of renewable sources. Wind energy is one of the viable sustainable sources. In particular, small-scale wind turbines are an attractive option for meeting the high demand for domestic energy consumption since exclude the installation problems of large-scale wind farms. However, appropriate wind resource, installation costs, and other factors must be taken into consideration as well. Therefore, a feasibility study for the setting up of this technology is required beforehand. This requires a decision-making problem involving complex conditions and a degree of uncertainty. It turns out that Bayesian Decision Networks are a suitable paradigm to deal with this task. In this work, we present the development of a decision-making method, built with Decision Bayesian Networks, to assess the use of small-scale wind turbines to meet the high-energy demand considering the available wind resource, installation costs, reduction in CO2 emissions and the achieved savings.

This paper presents the design of a disturbance observer used for estimating the electromotive force of a surface mount permanent magnet synchronous machine. The proposed observer combines the linear advantages of a Luenberger observer and the non-linear advantages of a sliding mode observer. In addition, the use of low-pass filters is avoided since the discontinuous function of the observer is replaced by the continuous sigmoid function without affecting its performance. Because the internal electromotive force contains the information on the position and speed of the motor used, its estimation is digitally process in order to obtain the position and speed of the motor. These position and speed estimates are used in a vector control scheme, where the need for the mechanical position/ speed sensor is eliminated. However, the electromotive force is Iinearly dependent on the speed of the motor, making it difficult to detect at zero and low speeds. Therefore, the proposed observer is useful only at medium and high speeds. Various simulations are carried out in MATLAB/ Simulink where the correct performance of the proposed observer is shown. Finally, the results obtained are analyzed and discussed.

Robotics belongs to the megatrends radically changing the logistics industry which has long time been reluctant to the adoption of technological innovation. Facing aggravated competition by ever new and stronger actors in the industry, labor shortage due to demographic shifts, and constant cost pressure, logistics robots are considered as an effective means to tackle those problems. By taking a look at the market of logistics robots, the market condition for cable-driven parallel robots for storage purposes can be studied and a successful introduction into the market of material handling devices initiated. Hence, this paper examines the prevalence of robotics solutions in general and cable-robot applications in particular in the logistics industry.

With nearly 93,500 deaths were attributed to Alzheimer's Disease in 2014 and numbers projected to climb, medical experts have recently placed increasing emphasis in the early detection of Alzheimer's disease and other types of dementia. An open challenge in early detection is developing standardized testing that helps clinicians detect subtle signs of Mild Cognitive Impairment that occur prior to the development of said dementias. In this regard we present our preliminary work on leveraging machine-learning behavioral classification to detect subtle behavioral abnormalities. Our framework consists of detecting behavioral abnormalities through the computerization of an existing paper-based clinical neuropsychological tests as well as the development of novel tests that could only be realized with modern touch tablet technology.

Energy efficiency has attracted the attention of many governments around the world due to the urgent call to reduce investments in energy infrastructure, lower fossil fuel dependency, integrate renewable energies, improve consumer welfare and reduce CO2 emissions. The conservative and smart use of energy is one of the main approaches to improve energy efficiency. However, the management of energy at the national level is a complex decision making problem involving uncertainty and therefore, Bayesian Networks are suitable paradigm to deal with this task. In this work, we present a progress report on the development of a decision making method, based on Bayesian decision networks, for the efficient use of energy as a function of the cost, efficiency and CO2 emissions from the source of energy used.

Lately, great efforts have been made to develop effective hybrid power systems, which consist of a mixture of renewable and conventional power plants, energy storage systems and power consumers. The very dissimilar characteristics of these elements, as well as the ever increasing performance requirements imposed to them, makes the design of control systems for power generation plants a remarkably challenging task. A promising approach to provide effective solutions to this problem is by applying the paradigms of Intelligent Agents and Multi-Agent Systems. In this paper, the definition of an Intelligent Multi-Agent System for Supervision and Control (iMASSC) is proposed to create intelligent power plants for either renewable or conventional power generation units. A Multi-Agent System with a generic structure is used instead of a single specific Intelligent Agent. This approach is more realistic in that it takes into account the complexity of current power plants. Later, the community of intelligent power plants, through autonomous and coherent collaboration, will achieve the objectives of the hybrid power system. Hence, the iMASSC model is expected to provide feasible solutions to the operation of modern intelligent hybrid power systems and smart grids.

The identification of the optic disc (OD) in fudus images is a useful landmark for a large number of ophthalmic diseases. The method proposed herein detects and segments the OD in a rather simple and fast manner, compared with those methods recently published. The procedure involves first: segment the blood vessel, then locate the OD using the highest intensity values found near the vessels. Finally, OD segmentation is performed using a modification of the elliptical Hough transform. The results are evaluated using five different metrics already used in the literature on the subject, and applied to a public database of 1200 fundus images, obtaining 89% in the Dice coefficient and 82% in Jaccard coefficient, and taking a processing time of 4.1 seconds per image. Compared with other published methodologies, the results presented with the current technique, improve speed performance while maintaining similar precision metrics.

Public streams of geo-located social media information have provided researchers with a rich source of information from which to draw patterns of urban-scale human-mobility. However, most of the literature relies on assumptions over the spatial distribution of this data (e.g., by considering only a uniform grid division of space). In this work the authors present a method that does not rely on such assumptions. They followed the social media activity of 24,135 Twitter users from Mexico City over a period of seven months (June 2013 - February 2014). The authors' method clusters user's geo-locations into a 19 zone data-driven division of Mexico City. These results can be interpreted from a graph theory-based perspective, by representing each division as nodes, and the edges between them as the number of people traveling between locations. Graph centrality reveals city's infrastructural key points. Without these gateways the authors can argue that mobility would either be radically transformed or break the city apart.

Clinical neuropsychologists develop comprehensive behavioral profiles on their patients primarily by using paper-and-pencil test stimuli. Despite these tests being significantly cheaper and faster than complex procedures such as MRI scans, multiple drawbacks remain. Constructing these behavioral profiles can take upwards of six hours to fully complete, and the analysis of the sketches from these pencil-and-paper tests is still largely subjective and qualitative. We developed SmartStrokes, a testing suite that implements digital versions of common clinical neuropsychology pencil-and-paper tests, with the purpose of helping to automate and analyze patient sketches using the principles of sketch recognition.

This paper presents the fixed and floating-point implementations for field-programmable gate arrays (FPGAs) of third-order hand tremor predictors using recursive-least square (RLS) and a proposed Kalman adaptation algorithm. The proposed algorithm outperforms RLS in convergence speed and mean square error (MSE). It also shows better numerical convergence than the RLS as the number of bits in fixed-point precision is reduced. Both fixed and floating-point realizations are implemented and the hardware tradeoffs are discussed. A modified binary floating-point format is proposed that takes advantage of the 18-bit hard macro multiplier within the Virtex 5 Architecture in order to gain precision while preserving clock speed. The increased precision overcomes the prior known issues of explosive divergence and the stalling effect associated with the fixed-point implementation of such adaptive algorithms, proving the feasibility of an FPGA based physiological hand tremor predictor. In order to demonstrate the tradeoff between the performance and the hardware complexity, we quantify the penalty paid by the system in terms of MSE due to the use of lower precision arithmetic.

A set of building and displaying programs has been written and applied to serve as tools in the study of the interaction of proteins with DNA and RNA. This study, which has involved the use of a variety of computers, is a collaborative project between the group at RPMI, Buffalo, New York, and NASA-Ames, California. The RPMI system, which is currently running on the Univac 70 computer, is easily adaptable to any computer, with or without sophisticated graphics hardware. Hence we emphasize the availability of an easy to handle and inexpensive means for building, displaying, and manipulating molecules. The RPMI system also includes an automated, nonin–teractive program for fast construction of specified molecular structures. The NASA system is running on an Evans and Sutherland display system which has sophisticated hardware and which is capable of continuously rotating the molecule with constant adjustment of perspective and intensity.

Various methods for detecting optic disc and macula in fundus images have been developed. Our aim is to propose a fairly easy method for detecting both features jointly. This is achieved by first correcting in homogenous luminosity using a polynomial approximation of the background of the images. Secondly, the use of the cross-correlation in the frequency domain between the images and a steerable template which contains both structures. The 38 photographs used in this work belong to a local database of patients suffering diabetic retinopathy along its four severity stages. Our results showed 100% optic disc centers located within the OD area and 90% macula centers located within the MC area.

Software frameworks which support integration and scaling of text analysis algorithms make it possible to build complex, high performance information systems for information extraction, information retrieval, and question answering; IBM's Watson is a prominent example. As the complexity and scaling of information systems become ever greater, it is much more challenging to effectively and efficiently determine which toolkits, algorithms, knowledge bases or other resources should be integrated into an information system in order to achieve a desired or optimal level of performance on a given task. This paper presents a formal representation of the space of possible system configurations, given a set of information processing components and their parameters (configuration space) and discusses algorithmic approaches to determine the optimal configuration within a given configuration space (configuration space exploration or CSE). We introduce the CSE framework, an extension to the UIMA framework which provides a general distributed solution for building and exploring configuration spaces for information systems. The CSE framework was used to implement biomedical information systems in case studies involving over a trillion different configuration combinations of components and parameter values operating on question answering tasks from the TREC Genomics. The framework automatically and efficiently evaluated different system configurations, and identified configurations that achieved better results than prior published results.

In the era of high competition with E-commerce and online shops, brick-and-mortar retail industry seeks new opportunities to enhance shopping experience through engaging technologies. Even though retailers are applying their omnichannel strategies to attract more shoppers through technology-driven solutions including websites, mobile apps, and so forth, we find that these technologies are somewhat basic and do not represent the “disruptive” innovations. Along with these current technologies, retailers should leverage their store physical real estate, and transform it into immersive store environments (ISEs) that allow shoppers to navigate in 3D store aisles through rich media interface ported onto networked devices. Therefore, we propose our own study of what ISE use-cases are most desirable by customers and retailers in such contexts; we describe the implementation of our cloud-based interactive shopping interface for ISE, before discussing the promising results of its deployment in a “real-world” store.

This paper presents the fixed and floating point implementations for field-programmable gate arrays (FPGA) of third-order hand tremor predictors using recursive-least square (RLS) and a proposed Kalman adaptation algorithms. The proposed algorithm outperforms RLS in convergence speed and mean square error (MSE). It also shows better numerical convergence than the RLS as the number of bits in fixed-point precision is reduced. Both fixed and floating point realizations are implemented and the hardware tradeoffs are discussed. A modified binary floating point format is proposed that takes advantage of the 18-bit hard macro multiplier within the Xilinx Virtex 5 Architecture in order to gain precision while preserving speed. The increased precision overcomes the prior known issues of explosive divergence and the stalling effect associated with the fixed point implementation of such adaptive algorithms, proving the feasibility of an FPGA based physiological hand tremor predictor.

The increasing availability of sensing devices has made the possibility of context-aware pervasive computing applications real. However, constructing this software requires extensive knowledge about the devices and specialized programming languages for interacting with them. While the nature of pervasive computing lends users to demand individualized applications, complexities render programming embedded devices unapproachable. In this paper we introduce the SEAP (Sensor Enablement for the Average Programmer) middleware which applies existing technologies developed for web programming to the task of collecting and using sensor data. We show how this approach can be used to create new applications and to update existing web applications to accept sensor data.

In this paper, we describe a demonstration of the SEAP middleware architecture applied to pervasive computing applications. SEAP, or Sensor Enablement for the Average Programmer, is an architectural pattern specifically targeted at junior and hobbyist level programmers. It builds on existing knowledge and technology resources commonly available to this target audience, and provides a friendly environment to create customized applications that interact with the physical world. While we discuss some of the motivation behind our work and give a brief overview of the SEAP architecture, the majority of this paper describes a proposed demonstration of the technology. This interactive demonstration is designed to be accessible to a wide variety of people, and to spur discussions on middleware for endusers of pervasive computing.

Small-size lesions to cerebral cortex induced by the CO2 laser have found applications in neurophysiology for anatomical-functional mapping of the brain functions by areas. The advantage of this new technique in comparison to traditional ones (mechanical, electrical and chemical methods) consists in the possibility of making well-controlled lesions of small and repeatable size to cerebral cortex. In this work, we employed numerical and physical modelling to study the effect of the CO2 laser beam diameter and power density on the ablation rate, size and shape of the laser-induced lesions. Our study showed that the existing linear mathematical model of the laser-induced ablation is sufficiently correct only for the case of relatively shallow lesions. For large penetration depths, the mathematical model of laser-tissue interaction that accounts for the exponential decay of laser power density with penetration depth gave better agreement with the experiment than the existing linear model.

Using a dense motion vector field as the main information the authors develop a region segmentation algorithm in which each region is matched to a four-parameter motion model. Based on Markov random fields the segmentation model detects moving parts of the human body with different apparent displacement such as the hands. The motion vector field has been estimated by a Baaziz pel-recursive method and considered together with others sources of information such as intensity contours, intensity values and non-compensated pixels as inputs of the Markov random field model. The maximum a posteriori criterion (MAP) is used for the optimization of the solution, and performed with a deterministic method: iterated conditional modes (ICM). Results on segmenting and classifying real sequences are shown and, based on a roughly defined directional dictionary, one application is pursuing the use of the segmented regions as commands for a virtual robot. The classification is based on the correlation coefficient (between the trained sequences and others) of wavelet coefficients, of the projected sum of the intensity of the segmentation field (in its binary version).

Traditional protocols as TCP and UDP propose a very restricted vision of the quality of service notion. These limitations that restrict the spreading out of distributed multimedia application led to us to define a new generic transport protocols generation instantiable from the applicative quality of service requirements. However, the introduction of a new transport protocol has to answer the wide scale deployment questions. This paper proposes a networking architecture based on the concept of active networks that makes possible the automatic and transparent deployment of advanced end to end communications services. The proposed approach has been successfully experimented on top of a large scale European Networking Infrastructure designed in the framework of the GCAP European project.

This paper summarizes a pragmatic technical approach to active networks that supports prototyping of multimedia transport protocols. One of the targets of the architecture defined is the extension of the processing capabilities of existing routers trying to keep up the forwarding performance of regular packets. This work presents practical results obtained from the development of a Java execution environment based on the design principles presented and proposes a method for the transparent deployment of multimedia relays over a source-destination path, tested in several experiments.