High brachial artery bifurcation: a report of 2 cases

High division of the brachial artery was observed in two cadavers, during routine dissection of upper extremities. In the first case, the brachial artery of the right upper extremity was divided into its two terminal branches immediately after passing between the lateral and medial roots of the median nerve and just below the origin of the profunda brachii artery. The lateral branch was the radial artery, located in the space normally occupied by the brachial artery and the medial one was the ulnar artery. In the second case, the brachial artery was divided into its two terminal branches just below the origin of the profunda brachii artery. Accurate knowledge of the relationships and course of these major arterial conduits and particularly of their variational patterns, is of considerable practical importance in the conduct of reperative surgery in the arm, forearm and hand.     

Morphological evolution of edge-hillocks on single-crystal films having anisotropic drift-diffusion under the capillary and electromigration forces

The morphological evolution of hillocks at the unpassivated sidewalls of single-crystal metallic thin film interconnects is investigated via computer simulations using the free-moving boundary value problem. The effect of drift-diffusion anisotropy on the development of surface topographical scenarios is fully explored under the action of electromigration and capillary forces, utilizing numerous combinations of the surface texture, the drift-diffusion anisotropy and the direction of the applied electric field. The simulation studies yield analytical relationships for the velocity of the surface solitary waves and the drift velocity of electromigration-induced internal voids as a function of the applied current densities, which contain intrinsic and structural properties of the single-crystal thin films. The threshold value of the applied current density, above which electromigration-induced internal voids can be formed and may cause the catastrophic failure of interconnects by breaching, also appears explicitly in this relationship.     

Self Assembly of Polymer Structures Induced by Electric Field

A method has been developed for fabricating polymer microstructures based on electric field induced self assembly and pattern formation. A dielectric fluid placed in between two conductive plates experience a force in an applied electric field gradient across the plates, which can induce a diffusive surface instability and self construction of the fluid surface. This process is exploited for the fabrication of self assembled polymer microstructures as well as replicated patterns through the use of pre-patterned plates or electrodes. FEM simulation is used to decide the minimum wavelength and electric gradient distribution of polymer structures. A variety of structures in the micron and nanometer scales including bio-fluidic MEMS, polymer optoelectronic devices can be fabricated using this method.     

The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability

The relationship between protein motions (i.e., dynamics) and enzymatic function has begun to be explored in β-lactamases as a way to advance our understanding of these proteins. In a recent study, we analyzed the dynamic profiles of TEM-1 (a ubiquitous class A β-lactamase) and several ancestrally reconstructed homologues. A chief finding of this work was that rigid residues that were allosterically coupled to the active site appeared to have profound effects on enzyme function, even when separated from the active site by many angstroms. In the present work, our aim was to further explore the implications of protein dynamics on β-lactamase function by altering the dynamic profile of TEM-1 using computational protein design methods. The Rosetta software suite was used to mutate amino acids surrounding either rigid residues that are highly coupled to the active site or to flexible residues with no apparent communication with the active site. Experimental characterization of ten designed proteins indicated that alteration of residues surrounding rigid, highly coupled residues, substantially affected both enzymatic activity and stability; in contrast, native-like activities and stabilities were maintained when flexible, uncoupled residues, were targeted. Our results provide additional insight into the structure-function relationship present in the TEM family of β-lactamases. Furthermore, the integration of computational protein design methods with analyses of protein dynamics represents a general approach that could be used to extend our understanding of the relationship between dynamics and function in other enzyme classes.     

Investigation of bio-ethanol steam reforming over cobalt-based catalysts

The catalytic performance of cobalt catalysts supported on γ-Al₂O₃, TiO₂, ZrO₂ were studied for bio-ethanol steam reforming (BESR) reaction. The supported catalysts (10 wt%Co) were prepared by impregnation and characterized through Thermogravimetric analysis (TGA), H₂ chemisorption, laser Raman Spectroscopy, Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), and temperature-programmed reaction (TPRxn). The metallic cobalt sites were found to correlate with the BESR reaction activity. The reaction and H₂ chemisorption showed that ZrO₂ supported catalyst showed the best dispersion and best catalytic activity. Over the 10% Co/ZrO₂ catalyst, using a H₂O:EtOH:inert molar ratio of 10:1:75 and a GHSV = 5000 h⁻¹, 100% ethanol conversion and a yield of 5.5 mol H₂/mol EtOH were obtained at 550 °C and atmospheric pressure.     

Unsupervised and simultaneous training of multiple object detectors from unlabeled surveillance video

Object detection is an essential component in automated vision-based surveillance systems. In general, object detectors are constructed using training examples obtained from large annotated data sets. The inevitable limitations of typical training data sets make such supervised methods unsuitable for building generic surveillance systems applicable to a wide variety of scenes and camera setups. In our previous work we proposed an unsupervised method for learning and detecting the dominant object class in a general dynamic scene observed by a static camera. In this paper, we investigate the possibilities to expand the applicability of this method to the problem of multiple dominant object classes. We propose an idea on how to approach this expansion, and perform an evaluation of this idea using two representative surveillance video sequences.     

In Situ Electrical Network Activation and Deactivation in Short Carbon Fiber Composites via 3D Printing

Prior studies on carbon-filler based, conductive polymer composites have mainly investigated how conductive filler morphology and concentration can tailor a material's electrical conductivity and overlooks the effects of filler alignment due to the difficulty to control and quickly quantify the filler alignment. Here, direct ink write 3D printing's unique ability is utilized to control carbon fiber alignment with a single process parameter, velocity ratio, to instantaneously activate or deactivate the electrical network in composites. Maximum electrical conductivity is achieved by randomly aligning carbon fibers that enhances the chance of direct fiber-to-fiber contact and, thus, activating the electrical network. However, aligning the fibers by increasing the velocity ratio disrupts the electrical network by minimizing fiber-to-fiber contact that resulted in a drastic decrease in electrical conductivity by as much as five orders of magnitude in both short and long carbon fiber composites. With this study, this study demonstrates that electrically conductive or insulative composites can be fabricated sequentially with a single ink. This novel ability to instantaneously control the electrical conductivity of carbon fiber reinforced composites allow to directly embed conductive pathways into designs to 3D print multifunctional composites that are capable of localized heating and self-sensing.     

Simulation-based workforce assignment in a multi-organizational social network for alliance-based software development

The development of alliance-based software requires the collaboration of many stakeholders. These different stakeholders across multiple organizations form a complex social network. The goal of this paper is to develop a novel modeling framework, which will help task managers devise optimal workforce assignments considering both short-term and long-term aspects of the software development process. The proposed framework is composed of an assignment module and a prediction module. For a given task, the assignment module first selects a candidate workforce mix. Based on the candidate workforce mix, the prediction module then predicts the short-term performance (productivity) as well as the long-term performance (workforce training and robustness of the organization) of the organization. Then, the assignment module selects another candidate mix, and this iteration continues until an optimal workforce mix is found. The prediction module and the assignment module are based on an agent-based simulation method and a multi-objective optimization model, respectively. The proposed modeling framework is illustrated with a software enhancement request process in Kuali, an alliance-based open source software development project involving 12 organizations. The constructed framework is executed with varying parameters to demonstrate its use and benefit in the software enhancement process.