Blood parameter and heart rate response to training in Andalusian horses

The psychopathology and associated disabilities experienced by persons with schizophrenia have only partially responded to conventional pharmacological and psychosocial treatment approaches. Biobehavioral treatment and rehabilitation employs behavioral assessment, social learning principles, skills training, and a focus on the recovery process to amplify the effects of pharmacotherapy. Utilizing the Medline database, we review a selection of English-language studies published from 1970 to 1994 that support the effectiveness of each of the components of biobehavioral therapy, such as case management, psychopharmacology with behavioral assessment, psychoeducation, family involvement, and social skills training. An integrated biobehavioral therapy directed toward early detection and treatment of schizophrenic symptoms, collaboration between consumers and caregivers in managing treatment, family and social skills training, and teaching coping skills and self-help techniques has been documented to improve the course and outcome of schizophrenia, as measured by symptom recurrence, social functioning, and quality of life. A case vignette is presented to illustrate the successful integration of biobehavioral therapies into a treatment system that focuses on consumers' attempts to become increasingly responsible for recovering from illness.     

Low-cost sensor to detect overtaking based on optical flow

The automotive industry invests substantial amounts of money in driver-security and driver-assistance systems. We propose an overtaking detection system based on visual motion cues that combines feature extraction, optical flow, solid-objects segmentation and geometry filtering, working with a low-cost compact architecture based on one focal plane and an on-chip embedded processor. The processing is divided into two stages: firstly analog processing on the focal plane processor dedicated to image conditioning and relevant image-structure selection, and secondly, vehicle tracking and warning-signal generation by optical flow, using a simple digital microcontroller. Our model can detect an approaching vehicle (multiple-lane overtaking scenarios) and warn the driver about the risk of changing lanes. Thanks to the use of tightly coupled analog and digital processors, the system is able to perform this complex task in real time with very constrained computing resources. The proposed method has been validated with a sequence of more than 15,000 frames (90 overtaking maneuvers) and is effective under different traffic situations, as well as weather and illumination conditions.     

An EM-based identification algorithm for a class of hybrid systems with application to power electronics

In this paper we present an identification algorithm for a class of continuous-time hybrid systems. In such systems, both continuous-time and discrete-time dynamics are involved. We apply the expectation-maximisation algorithm to obtain the maximum likelihood estimate of the parameters of a discrete-time model expressed in incremental form. The main advantage of this approach is that the continuous-time parameters can directly be recovered. The technique is particularly well suited to fast-sampling rates. As an application, we focus on a standard identification problem in power electronics. In this field, our proposed algorithm is of importance since accurate modelling of power converters is required in high- performance applications and for fault diagnosis. As an illustrative example, and to verify the performance of our proposed algorithm, we apply our results to a flying capacitor multicell converter.     

Studying Fluid Characteristics Atop Surface Patterned Membranes via Particle Image Velocimetry

Surface patterning is a recent promising approach to promote performance of pressure-driven membranes in water treatment and desalination. Nevertheless, knowledge about foulant deposition mechanisms, especially at early stage of filtration, is still lacking. The applicability of particle imaging velocimetry to study fluid characteristics atop surface patterned thin-film composite membranes was investigated at different operating conditions. This work is an important first step toward reliable understanding of the impacts of topographical membrane surface modification on hydrodynamic conditions and foulant deposition mechanisms.     

Optimized Machine Learning Algorithms for Investigating the Relationship Between Economic Development and Human Capital

Computational Economics - In Economic Development, human capital was previously seen as production factors but gradually evolved into endogenous growth theories. Most of the previous studies have...     

Transferrin-Decorated Niosomes with Integrated InP/ZnS Quantum Dots and Magnetic Iron Oxide Nanoparticles: Dual Targeting and Imaging of Glioma

The development of multifunctional nanoscale systems that can mediate efficient tumor targeting, together with high cellular internalization, is crucial for the diagnosis of glioma. The combination of imaging agents into one platform provides dual imaging and allows further surface modification with targeting ligands for specific glioma detection. Herein, transferrin (Tf)-decorated niosomes with integrated magnetic iron oxide nanoparticles (MIONs) and quantum dots (QDs) were formulated (PEGNIO/QDs/MIONs/Tf) for efficient imaging of glioma, supported by magnetic and active targeting. Transmission electron microscopy confirmed the complete co-encapsulation of MIONs and QDs in the niosomes. Flow cytometry analysis demonstrated enhanced cellular uptake of the niosomal formulation by glioma cells. In vitro imaging studies showed that PEGNIO/QDs/MIONs/Tf produces an obvious negative-contrast enhancement effect on glioma cells by magnetic resonance imaging (MRI) and also improved fluorescence intensity under fluorescence microscopy. This novel platform represents the first niosome-based system which combines magnetic nanoparticles and QDs, and has application potential in dual-targeted imaging of glioma.