Damage evolution in polymer due to exposure to high-pressure hydrogen gas

The use of hydrogen as a fuel is increasing exponentially, and the most economical way to store and transport hydrogen for fuel use is as a high-pressure gas. Polymers are widely used for hydrogen distribution and storage systems because they are chemically inert towards hydrogen. However, when exposed to high-pressure hydrogen, some hydrogen diffuses through polymers and occupies the preexisting cavities inside the material. Upon depressurization, the hydrogen trapped inside polymer cavities can cause blistering or cracking by expanding these cavities. A continuum mechanics–based deformation model was deployed to predict the stress distribution and damage propagation while the polymer undergoes depressurization after high-pressure hydrogen exposure. The effects of cavity size, cavity location, and pressure inside the cavity on damage initiation and evolution inside the polymer were studied. The stress and damage evolution in the presence of multiple cavities was also studied, because interaction among cavities alters the damage and stress field. It was found that all these factors significantly change the stress state in the polymer, resulting in different paths for damage propagation. The effect of adding carbon black filler particles and plasticizer on the damage was also studied. It was found that damage tolerance of the polymer increases drastically with the addition of carbon black fillers, but decreases with the addition of the plasticizer.     

Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the required task. Feature Selection based on the use of statistical and correlation matrices for multivariate analysis. For Random Search and Grid Search models, extensive comparison findings are produced utilizing retrieval, F1 score, and precision measurements. The models are evaluated using the metrics and kappa statistics that illustrate the three models’ comparability. The study effort focuses on optimizing function selection, tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification. Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.     

Enhancing biometric recognition with spatio-temporal reasoning in smart environments

We discuss smart environments that identify and track their occupants using unobtrusive recognition modalities such as face, gait, and voice. In order to alleviate the inherent limitations of recognition, we propose spatio-temporal reasoning techniques based upon an analysis of the occupant tracks. The key idea underlying our approach is to determine the identity of a person based upon information from a track of events rather than a single event. We abstract a smart environment by a probabilistic state transition system in which each state records a set of individuals who are present in various zones of the smart environment. An event abstracts a recognition step, and the transition function defines the mapping between states upon the occurrence of an event. We express two forms of spatio-temporal reasoning in the form of transition functions: a track-based transition function and an error-correcting transition function. We also define the concepts of ‘precision’ and ‘recall’ to quantify the performance of the smart environment and provide experimental results to clarify the performance improvements from spatio-temporal reasoning. Our conclusion is that the state transition system is an effective abstraction of a smart environment and the application of spatial-temporal reasoning enhances the overall performance of a biometric recognition system.     

Fault tolerance in wireless sensor network using hand-off and dynamic power adjustment approach

Wireless Sensor Network (WSN) is deployed to monitor physical conditions in various places such as geographical regions, agriculture lands, office buildings, industrial plants and battlefields. WSNs are prone to different types of failures due to various environmental hazards like interference and internal failures (such as battery failure, processor failure, transceiver failure, etc). In such a situation, the sensed data cannot be transmitted correctly to the data center and the very purpose of deploying WSNs is not effective. Since it is difficult to monitor the network continuously through a manual operator, the nodes in WSN need to be capable of overcoming the failures and transmit the sensed data in proper order to the data center. Sensor network should be designed such that it should be able to identify the faulty nodes, try to rectify the fault and be able to transmit the sensed data to data center under faulty condition of a network and thereby make the network fault-free and thus enhance the fault tolerant capability.In this paper, we propose a novel idea of an Active node based Fault Tolerance using Battery power and Interference model (AFTBI) in WSN to identify the faulty nodes using battery power model and interference model. Fault tolerance against low battery power is designed through hand-off mechanism where in the faulty node selects the neighboring node having highest power and transfers all the services that are to be performed by the faulty node to the selected neighboring node. Fault tolerance against interference is provided by dynamic power level adjustment mechanism by allocating the time slot to all the neighboring nodes. If a particular node wishes to transmit the sensed data, it enters active status and transmits the packet with maximum power; otherwise it enters into sleep status having minimum power that is sufficient to receive hello messages and to maintain the connectivity. The performance evaluation is tested through simulation for packet delivery ratio, control overhead, memory overhead and fault recovery delay. We compared our results with Fault Detection in Wireless Sensor Networks (FDWSNs) for various performance measures and found that AFTBI outperforms compared to the results of FDWSN.     

Molecular Characterization of α- and β-Thalassaemia among Malay Patients

Both α- and β-thalassaemia syndromes are public health problems in the multi-ethnic population of Malaysia. To molecularly characterise the α- and β-thalassaemia deletions and mutations among Malays from Penang, Gap-PCR and multiplexed amplification refractory mutation systems were used to study 13 α-thalassaemia determinants and 20 β-thalassaemia mutations in 28 and 40 unrelated Malays, respectively. Four α-thalassaemia deletions and mutations were demonstrated. −−^SEA deletion and α^CSα accounted for more than 70% of the α-thalassaemia alleles. Out of the 20 β-thalassaemia alleles studied, nine different β-thalassaemia mutations were identified of which β^E accounted for more than 40%. We concluded that the highest prevalence of (α- and β-thalassaemia alleles in the Malays from Penang are −−^SEA deletion and β^E mutation, respectively.     

Simulation of hydrogen distribution in an Indian Nuclear Reactor Containment

The management of hydrogen in a Nuclear Reactor Containment after LOCA (Loss Of Coolant Accident) is of practical importance to preserve the structural integrity of the containment. This paper presents the results of systematic work carried out using the commercial Computational Fluid Dynamics (CFD) software FLUENT to assess the concentration distribution of hydrogen in a typical Indian Nuclear Reactor Containment. In order to obtain an accurate estimate of hydrogen concentration distribution, a suitable model for turbulence closure is required to be selected. Using guidelines from the previous studies reported in the literature and a comparative simulation study using simple benchmark problems, the most suitable turbulence model for hydrogen mixing prediction was identified. Subsequently, unstructured meshes were generated to represent the containment of a typical Indian Nuclear Reactor. Analyses were carried out to quantify the hydrogen distribution for three cases. These were (1) Uniform injection of hydrogen for a given period of time at room temperature, (2) Time varying injection as has been computed from an accident analysis code, (3) Time varying injection (as used in case (2)) at a high temperature. A parametric exercise was also carried out in case (1) where the effect of various inlet orientations and locations on hydrogen distribution was studied. The results indicate that the process of hydrogen dispersal is buoyancy dominated. Further for typical injection rates encountered following LOCA, the dispersal is quite poor and most hydrogen is confined to the fuelling machine vault.