Development of a virtual reality laboratory stressor

Virtual Reality - This research report describes the development of a virtual reality (VR) laboratory stressor to study the effects of exposure to stressful events. The aim of the research was to...     

Polyester functionalization for mycobacterial capture

Tuberculosis (TB) is one of the deadliest diseases known to man and ranks among the top ten global causes of fatalities. Children often develop a paucibacillary form of this disease which makes diagnosis arduous even with the aid of sophisticated techniques. In these cases, the concentration of bacteria in their sputum falls below the lowest limit of detection for sputum smear microscopy, the technique predominantly used in developing countries. In this study we aim to test the hypothesis that modifying a polymer, often used in buccal swabs, could assist in creating a capturing and concentrating oral swab for Mycobacterium tuberculosis (Mtb), the pathogenic bacterial species responsible for TB. Such a device will assist in meeting the detection limit and allow for rapid detection of the disease. The polymer used was a micro fibrous form of poly(ethylene terephthalate) (PET) which was surface functionalized with Concanavalin A (Con A), a lectin based adhesin with an affinity for the mannose groups on the Mtb cell wall. The functionalization was mediated with glutaraldehyde as bioconjugate molecule and found to produce a surface uniformly covered with Con A. Affinity studies between the modified fibers and the Mycobacterium bovis bacillus Calmette-Guérin (BCG), as Mtb mimic, were conducted to evaluate the capturing abilities of the substrate. The results indicate that the fibers avidly captured BCG and that the fibrous matrix aided in its function. Dilution studies showed successful capturing of the bacterial species at concentrations characteristic of paucibacillary cases. In this study a commercial polymeric material was successfully surface modified with a biological entity to create a substrate to which Mtb could adhere and accordingly be captured.     

High-throughput drilling of titanium alloys

Experiments of high-throughput drilling of Ti–6Al–4V at 183 m/min cutting speed and 156 mm³/s material removal rate (MRR) using a 4 mm diameter WC–Co spiral point drill were conducted. The tool material and geometry and drilling process parameters, including cutting speed, feed, and fluid supply, were studied to evaluate the effect on drill life, thrust force, torque, energy, and burr formation. The tool wear mechanism, hole surface roughness, and chip light emission and morphology for high-throughput drilling were investigated. Supplying the cutting fluid via through-the-drill holes has proven to be a critical factor for drill life, which can be increased by 10 times compared to that of dry drilling at 183 m/min cutting speed and 0.051 mm/rev feed. Under the same MRR of 156 mm³/s with a doubled feed of 0.102 mm/rev (91 m/min cutting speed), over 200 holes can be drilled. The balance of cutting speed and feed is essential to achieve long drill life and good hole surface roughness. This study demonstrates that, using proper drilling process parameters, spiral point drill geometry, and fine-grained WC–Co tool material, the high-throughput drilling of Ti alloy is technically feasible.     

A fuzzy expert system for the design of machining operations

In this paper, a fuzzy expert system called Smart Assistant to Machinist, or SAM, is introduced. The system consists of four modules: a database, a cutter selection module, a cutting condition design module and a learning module. The database consists of four data files: work material data file, machine tool data file, machining plan data file (which defines desirable material removal rate, surface finish, cutting speed, feed, depth of cut, etc.) and cutter data file. The cutter selection module is developed based on fuzzy logic, in which the cutter selection is conducted in three steps. First, the input information is “fuzzyized” Next, using the fuzzy correlation functions, cutter grades and cutters are selected. Then, the selected grades and cutters are searched against the cutter data file to check the availability. The cutting condition design module is developed based on fuzzy non-linear programming and user interactive graphics. The learning module is developed so that users can fine-tune the fuzzy functions to further improve the performance of the system. The system has two distinct features: (1) it can select cutters and design cutting conditions based on partial and imprecise information, and (2) it selects commercial cutter products used on shop floors. The use of the system is demonstrated using two examples.     

Speeding up the spatial adiabatic passage of matter waves in optical microtraps by optimal control

We numerically investigate the performance of atomic transport in optical microtraps via the so called spatial adiabatic passage technique. Our analysis is carried out by means of optimal control methods, which enable us to determine suitable transport control pulses. We investigate the ultimate limits of the optimal control in speeding up the transport process in a triple well configuration for both a single atomic wave packet and a Bose-Einstein condensate within a regime of experimental parameters achievable with current optical technology.     

Inferring (Biological) Signal Transduction Networks via Transitive Reductions of Directed Graphs

In this paper we consider the p-ary transitive reduction (TR _p ) problem where p>0 is an integer; for p=2 this problem arises in inferring a sparsest possible (biological) signal transduction network consistent with a set of experimental observations with a goal to minimize false positive inferences even if risking false negatives. Special cases of TR _p have been investigated before in different contexts; the best previous results are as follows:

Dynamic Verification of Memory Consistency in Cache-Coherent Multithreaded Computer Architectures

Multithreaded servers with cache-coherent shared memory are the dominant type of machines used to run critical network services and database management systems. To achieve the high availability required for these tasks, it is necessary to incorporate mechanisms for error detection and recovery. Correct operation of the memory system is defined by the memory consistency model. Errors can therefore be detected by checking if the observed memory system behavior deviates from the specified consistency model. Based on recent work, we design a framework for dynamic verification of memory consistency (DVMC). The framework consists of mechanisms to verify three invariants that are proven to guarantee that a specified memory consistency model is obeyed. We describe an implementation of the framework for the SPARCv9 architecture, and we experimentally evaluate its performance using full-system simulation of commercial workloads.     

A large anisotropic damage theory based on an incremental complementary energy equivalence model

This paper presents a large anisotropic damage theory of continuum damage mechanics. It is developed via a new hypothesis of incremental complementary elastic energy equivalence. This hypothesis is more versatile and accurate if compared to the original hypothesis of total complementary energy equivalence. To model the large damage, we assumed that it occurs as a series of incremental small damage. An expression for the damage effect tensor M(D) for large damage is derived. It is shown that when the damage is small, that is, D _i1, the proposed large damage theory reduces to the small damage model of Chow and Wang [1]. To demonstrate this large damage theory, it is applied to model the following cases: (a) uniaxial tension, (b) pure torsion and (c) elastic perfectly-plastic material behavior. In all three cases, the results clearly show that when the damage is small, Chow and Wang's model is recovered. However, for large damage, there are significant differences in predictions. Since this large damage theory is formulated on the basis of the incremental complementary energy, it is applicable to a wider range of problems.     

Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.