Improved labeling of subcortical brain structures in atlas-based segmentation of magnetic resonance images

Precise labeling of subcortical structures plays a key role in functional neurosurgical applications. Labels from an atlas image are propagated to a patient image using atlas-based segmentation. Atlas-based segmentation is highly dependent on the registration framework used to guide the atlas label propagation. This paper focuses on atlas-based segmentation of subcortical brain structures and the effect of different registration methods on the generated subcortical labels. A single-step and three two-step registration methods appearing in the literature based on affine and deformable registration algorithms in the ANTS and FSL algorithms are considered. Experiments are carried out with two atlas databases of IBSR and LPBA40. Six segmentation metrics consisting of Dice overlap, relative volume error, false positive, false negative, surface distance, and spatial extent are used for evaluation. Segmentation results are reported individually and as averages for nine subcortical brain structures. Based on two statistical tests, the results are ranked. In general, among four different registration strategies investigated in this paper, a two-step registration consisting of an initial affine registration followed by a deformable registration applied to subcortical structures provides superior segmentation outcomes. This method can be used to provide an improved labeling of the subcortical brain structures in MRIs for different applications.     

Optimization of the microstructure of porous composite cathodes in solid oxide fuel cells

A comprehensive micromodel to predict the electrochemical performance of porous composite LSM‐YSZ cathodes in solid oxide fuel cells (SOFCs) is developed. The random packing sphere model is used to estimate the cathode microstructural properties required for the micromodel. The micromodel developed takes into account the complex interdependency among the mass transport, electron and ion transports, and the electrochemical reaction, and can be used for optimization of the microstructure of porous LSM‐YSZ composite cathodes. It is shown that the electrochemical performance of these cathodes depends on the microstructural variables of the cathode porosity, thickness, particle size ratio, and size and volume fraction of LSM particles. The effect of these microstructural variables on the cathode total resistance, as the objective function to achieve the optimum microstructure for the cathode, is studied through computer simulation. The results indicated that for a LSM‐YSZ cathode operated at the average temperature of 1073.15 K, bulk oxygen partial pressure of 0.21 atm, and total current density of 5000 Am^?2, and constrained to the minimum value of 1 μm for the size of LSM particles and 0.25 for the cathode porosity, the optimum microstructure is obtained at the particle size ratio of unity, LSM particle size of 1 μm and volume fraction of 0.413, porosity of 0.25, and thickness of 60 μm. The cathode total resistance corresponding to the cathode optimized is estimated to be 0.291 Ω cm². © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Seismic evaluation of a 56‐storey residential reinforced concrete high‐rise building based on nonlinear dynamic time‐history analyses

In recent decades, shear walls and tube structures have been the most appropriate structural forms for the construction of high‐rise concrete buildings. Thus, recent Reinforced Concrete (RC) tall buildings have more complicated structural behaviour than before. Therefore, studying the structural systems and associated behaviour of these types of structures is very important. The main objective of this paper is to study the linear and nonlinear behaviour of one of the tallest RC buildings, a 56‐storey structure, located in a high seismic zone in Iran. In this tower, shear wall systems with irregular openings are utilized under both gravity and lateral loads and may result in some especial issues in the behaviour of structural elements such as shear walls and coupling beams. The analytical methodologies and the results obtained in the evaluation of life‐safety and collapse prevention of the building are also discussed. The weak zones of the structure based on the results are introduced, and a detailed discussion of some important structural aspects of the high‐rise shear wall system with consideration of the concrete time dependency and constructional sequence effects is also included. Copyright © 2010 John Wiley & Sons, Ltd.     

A study on motion of machine tools’ hexapod table on freeform surfaces with circular interpolation

Accuracy is greatly affected by nonlinear motion of hexapods. This need is more obvious when these mechanisms are used in machining environments where precision and surface qualities are of critical importance. In this paper, comprehensive algorithm for hexapod tool path programming is developed. Using C#.Net, this algorithm is developed based on circular motion and rotation of the table which has the capability of checking nonlinear error and keeping it in a controlled limit as well. Improved Tustin algorithm is used for interpolating circular path. To evaluate the accuracy of the developed algorithm on a freeform surface, a turbine blade is scanned, and its CAD model is developed. Taking zigzag strategies, movement on turbine blade surface is approximated with smaller circles using the algorithm presented in this paper. The output accuracy resulted from interpolation algorithm for passing on turbine blade surface is studied in SimMechanics of MATLAB software. Using Total Station camera, motion path of two turbine blades with different radius curves on the hexapod table is experimentally obtained. Finally, it can be stated that the developed algorithm based on circular interpolation has the capabilities of motion on freeform curves.     

A Proteomic Study Suggests Stress Granules as New Potential Actors in Radiation-Induced Bystander Effects

Besides the direct effects of radiations, indirect effects are observed within the surrounding non-irradiated area; irradiated cells relay stress signals in this close proximity, inducing the so-called radiation-induced bystander effect. These signals received by neighboring unirradiated cells induce specific responses similar with those of direct irradiated cells. To understand the cellular response of bystander cells, we performed a 2D gel-based proteomic study of the chondrocytes receiving the conditioned medium of low-dose irradiated chondrosarcoma cells. The conditioned medium was directly analyzed by mass spectrometry in order to identify candidate bystander factors involved in the signal transmission. The proteomic analysis of the bystander chondrocytes highlighted 20 proteins spots that were significantly modified at low dose, implicating several cellular mechanisms, such as oxidative stress responses, cellular motility, and exosomes pathways. In addition, the secretomic analysis revealed that the abundance of 40 proteins in the conditioned medium of 0.1 Gy irradiated chondrosarcoma cells was significantly modified, as compared with the conditioned medium of non-irradiated cells. A large cluster of proteins involved in stress granules and several proteins involved in the cellular response to DNA damage stimuli were increased in the 0.1 Gy condition. Several of these candidates and cellular mechanisms were confirmed by functional analysis, such as 8-oxodG quantification, western blot, and wound-healing migration tests. Taken together, these results shed new lights on the complexity of the radiation-induced bystander effects and the large variety of the cellular and molecular mechanisms involved, including the identification of a new potential actor, namely the stress granules.     

Epoxy/acrylonitrile-butadiene-styrene copolymer/clay ternary nanocomposite as impact toughened epoxy

Epoxy resins have low impact strength and poor resistance to crack propagation, which limit their many end use applications. The main objective of this work is to incorporate both acrylonitrile-butadiene-styrene copolymer (ABS) and organically modified clay (Cloisite 30B) into epoxy matrix with the aim of obtaining improved material with the impact strength higher than neat epoxy, epoxy/clay and epoxy/ABS hybrids without compromising the other desired mechanical properties such as tensile strength and modulus. Impact and tensile properties of binary and ternary systems were investigated. Tensile strength, elongation at break and impact strength were increased significantly with incorporation of only 4 phr ABS to epoxy matrix. For epoxy/clay nanocomposite with 2.5% clay content, tensile modulus and strength, and impact strength were improved compared to neat epoxy. With incorporation of 2.5% clay and 4 phr ABS into epoxy matrix, 133% increase was observed for impact strength. Ternary nanocomposite had impact and tensile strengths greater than values of the binary systems. Morphological properties of epoxy/ABS, epoxy/clay and epoxy/ABS/clay ternary nanocomposite were studied using atomic force microscopy (AFM) phase imaging, scanning electron microscopy (SEM) and wide angle X-ray diffraction (WAXD). New morphologies were achieved for epoxy/ABS and epoxy/ABS/clay hybrid materials. Exfoliated clay structure was obtained for epoxy/clay and epoxy/ABS/clay nanocomposite.     

General method of simulating radiation fields using measured boundary values

A general methodology integrating the Fresnell, Snell, and Beer-Lambert laws for modeling the radiant distribution in a medium is presented. The model considers refraction/reflection through/from the body of the UV lamp and sleeve, as well as the reflections from other sources, such as the reactor body. The measured boundary conditions are applied to realistically simulate the fluence/irradiance rate around the radiant source, in particular, in the zone closest to the radiant source. Different low-pressure UV lamps were tested under different operating conditions using photodiodes and a radiometer. The experimentally measured irradiance rate was in a very good agreement with the simulation results of the presented model.     

Lead time control in multi-class multi-stage assembly systems with finite capacity

In this paper, we model multi-class multi-stage assembly systems with finite capacity as queueing networks. It is assumed that different classes (types) of products are produced by the production system and products’ orders for different classes are received according to independent Poisson processes. Each service station of the queueing network specifies a manufacturing or assembly operation, in that processing times for different types of products are independent and exponentially distributed random variables with service rates, which are controllable, and the queueing discipline is First Come First Served (FCFS). Different types of products may be different in their routing sequences of manufacturing and assembly operations. For modeling multi-class multi-stage assembly systems, we first consider every class separately and convert the queueing network of each class into an appropriate stochastic network. Then, by using the concept of continuous-time Markov processes, a system of differential equations is created to obtain the distribution function of manufacturing lead time for any type of product, which is actually the time between receiving the order and the delivery of finished product. Furthermore, we develop a multi-objective model with three conflicting objectives to optimally control the service rates, and use goal attainment method to solve a discrete-time approximation of the original multi-objective continuous-time problem.     

From zero-shot machine learning to zero-day attack detection

Machine learning (ML) models have proved efficient in classifying data samples into their respective categories. The standard ML evaluation methodology assumes that test data samples are derived from pre-observed classes used in the training phase. However, in applications such as Network Intrusion Detection Systems (NIDSs), obtaining data samples of all attack classes to be observed is challenging. ML-based NIDSs face new attack traffic known as zero-day attacks that are not used in training due to their non-existence at the time. Therefore, this paper proposes a novel zero-shot learning methodology to evaluate the performance of ML-based NIDSs in recognising zero-day attack scenarios. In the attribute learning stage, the learning models map network data features to semantic attributes that distinguish between known attacks and benign behaviour. In the inference stage, the models construct the relationships between known and zero-day attacks to detect them as malicious. A new evaluation metric is defined as Zero-day Detection Rate (Z-DR) to measure the effectiveness of the learning model in detecting unknown attacks. The proposed framework is evaluated using two key ML models and two modern NIDS data sets. The results demonstrate that for certain zero-day attack groups discovered in this paper, ML-based NIDSs are ineffective in detecting them as malicious. Further analysis shows that attacks with a low Z-DR have a significantly distinct feature distribution and a higher Wasserstein Distance range than the other attack classes.