LinkCluE: A MATLAB Package for Link-Based Cluster Ensembles

Cluster ensembles have emerged as a powerful meta-learning paradigm that provides improved accuracy and robustness by aggregating several input data clusterings. In particular, link-based similarity methods have recently been introduced with superior performance to the conventional co-association approach. This paper presents a MATLAB package, LinkCluE, that implements the link-based cluster ensemble framework. A variety of functional methods for evaluating clustering results, based on both internal and external criteria, are also provided. Additionally, the underlying algorithms together with the sample uses of the package with interesting real and synthetic datasets are demonstrated herein.

Modelling Immunological Memory

Accurate immunological models offer the possibility of performing highthroughput experiments in silico that can predict, or at least suggest, in vivo phenomena. In this chapter, we compare various models of immunological memory. We first validate an experimental immunological simulator, developed by the authors, by simulating several theories of immunological memory with known results. We then use the same system to evaluate the predicted effects of a theory of immunological memory. The resulting model has not been explored before in artificial immune systems research, and we compare the simulated in silico output with in vivo measurements. Although the theory appears valid, we suggest that there are a common set of reasons why immunological memory models are a useful support tool; not conclusive in themselves

Targeting colorectal cancer with anti-epidermal growth factor receptor antibodies: focus on panitumumab

Panitumumab is a fully humanized monoclonal antibody with a high degree of affinity for the extracellular domain of the epidermal growth factor receptor. Phase II clinical evaluation of this drug, when administered as a single agent, in patients with metastatic colorectal cancer refractory to chemotherapy, demonstrated a modest objective radiographic response rate with acceptable toxicity; the most frequently observed side effect is rash. A randomized phase III study in subjects with chemotherapy-refractory metastatic colorectal cancer documented a progression-free survival advantage in subjects treated with panitumumab plus best supportive care versus best supportive care alone; a difference in survival was not observed, likely due to the high cross over rate. Primary tumor KRAS mutation analysis performed in this study indicated that the benefit was confined to those patients whose tumors did not contain a KRAS mutation. Further studies with panitumumab will be required to develop biomarkers of response and to determine if panitumumab has a role in combination with cytotoxic chemotherapy. This article summarizes the current state-of-the-science knowledge on panitumumab therapy in the treatment of advanced colorectal cancer

Modelling Immunological Memory

Accurate immunological models offer the possibility of performing highthroughput experiments in silico that can predict, or at least suggest, in vivo phenomena. In this chapter, we compare various models of immunological memory. We first validate an experimental immunological simulator, developed by the authors, by simulating several theories of immunological memory with known results. We then use the same system to evaluate the predicted effects of a theory of immunological memory. The resulting model has not been explored before in artificial immune systems research, and we compare the simulated in silico output with in vivo measurements. Although the theory appears valid, we suggest that there are a common set of reasons why immunological memory models are a useful support tool; not conclusive in themselves

A Deep Learning-based Approach for Fault Diagnosis of Roller Element Bearings

Condition monitoring and fault detection of roller element bearings is of vital importance to ensuring safe and reliable operation of rotating machinery systems. Over the past few years, convolutional neural network (CNN) has been recognized as a useful tool for fault detection of roller element bearings. Unlike the traditional fault diagnosis approaches, CNN does not require manually extracting the fault-related features from the raw sensor data and most CNN-based fault diagnosis approaches feed the raw or shallowly pre-processed data as the training/testing inputs to a CNN model, thereby avoiding the need for manual feature extraction. As such, these approaches can be considered as purely data-driven. However, it has been proven that some well-established signal pre-processing techniques such as spectral kurtosis and envelope analysis can effectively clean and pre-process a raw signal to be a better representative of the health condition of a bearing without losing critical diagnostic information. This study proposes a new approach to bearing fault diagnosis, termed the SK-based multi-channel CNN (SCNN), that combines signal pre-processing techniques with a modified 1D CNN. The proposed SCNN approach involves two main steps: in the first step, each raw sensor signal acquired from a bearing is pre-processed to maximize the signal-to-noise ratio without losing critical diagnostic information carried by the signal; and in the second step, all pre-processed signals are fed into a 1D multi-channel CNN that classifies the health condition of the bearing. An experimental case study was carried out to evaluate the performance of the proposed approach. In this case study, a machinery fault simulator was used to validate the performance of SCNN in the presence of faults unrelated to bearings such as shaft misalignment and rotor unbalance

Rotation Mitigation and OCCAMS + Tungsten Flight Termination for Eclipse Balloon Missions

Stack rotation is a nemesis for many ballooning experiments, especially photography when trying to keep a specific target in view such as the Moon’s shadow (or the Sun itself) on eclipse flights. Ascending weather balloons tend to slow or even stop rotating once in the stratosphere and out of most cross winds. However payload stacks can continue to rotate with respect to the balloon right up to burst, especially if attached to the balloon neck by just a single main line. Our passive “rotation mitigation” device attaches directly to the neck of the balloon and runs four parallel lines separated by 6 inches from the balloon neck down to the payload stack, significantly diminishing stack rotation with respect to the balloon, especially at high altitudes. This arrangement complicates the placement of the parachute, but we have successfully deployed parachutes from a hook on the side of the upper-most payload box. This also complicates the placement of a flight-termination line-cutter, be that Montana’s “OCCAMS” razor cutter or something like a Tungsten hot-wire cutter. We have developed a compact payload box to enclose both an OCCAMS razor cutter and a Tungsten hot-wire cutter, both of which can independently release the multiple lines of our rotation mitigation device. We can fire the OCCAMS by XBee commands relayed through our RFD 900 payload, as an alternative to the Iridium text-message system. The Tungsten cutter can be fired by XBee command, by timer, or by autonomous GPS-sensor-based decision making