GERT analysis of two-stage attribute sampling plans based on an exponential failure model

The Graphical Evaluation and Review Technique (GERT) has been applied to modelling of a two-stage attribute sampling plan based on an exponential failure model.     

Testing in harsh conditions: Tracking resources on construction sites with machine vision

When tracking resources in large-scale, congested, outdoor construction sites, the cost and time for purchasing, installing and maintaining the position sensors needed to track thousands of materials, and hundreds of equipment and personnel can be significant. To alleviate this problem a novel vision based tracking method that allows each sensor (camera) to monitor the position of multiple entities simultaneously has been proposed. This paper presents the full-scale validation experiments for this method. The validation included testing the method under harsh conditions at a variety of mega-project construction sites. The procedure for collecting data from the sites, the testing procedure, metrics, and results are reported. Full-scale validation demonstrates that the novel vision tracking provides a good solution to track different entities on a large, congested construction site.     

Anti-Inflammatory Therapy for Atherosclerosis: Focusing on Cytokines

Atherosclerosis is a well-known global health problem. Despite the high prevalence of the disease, numerous aspects of pathogenesis remain unclear. Subsequently, there are still no cure or adequate preventive measures available. Atherogenesis is now considered a complex interplay between lipid metabolism alterations, oxidative stress, and inflammation. Inflammation in atherogenesis involves cellular elements of both innate (such as macrophages and monocytes) and adaptive immunity (such as B-cells and T-cells), as well as various cytokines cascades. Because inflammation is, in general, a well-investigated therapeutic target, and strategies for controlling inflammation have been successfully used to combat a number of other diseases, inflammation seems to be the preferred target for the treatment of atherosclerosis as well. In this review, we summarized data on targeting the most studied inflammatory molecular targets, CRP, IL-1β, IL-6, IFN-γ, and TNF-α. Studies in animal models have shown the efficacy of anti-inflammatory therapy, while clinical studies revealed the incompetence of existing data, which blocks the development of an effective atheroprotective drug. However, all data on cytokine targeting give evidence that anti-inflammatory therapy can be a part of a complex treatment.     

Free volume,adhesion, and viscoelastic properties of model nanostructured pressure‐sensitive adhesive based on stoichiometric complex of poly(N‐vinyl pyrrolidone) and poly(ethylene glycol) of disparate chain lengths

Positron annihilation lifetime spectroscopy was used to characterize the size and content of subnanoscopic free volume in a model pressure‐sensitive adhesive based on a stoichiometric hydrogen‐bonded network complex of poly(N‐vinyl pyrrolidone) (PVP) and oligomeric poly(ethylene glycol) (PEG). Adhesive properties were examined with peel and probe tack tests, and mechanical properties were studied with tensile test. Nonequimolar stoichiometry and the structure of PVP–PEG model pressure‐sensitive adhesive blends were found to be determined by the length of PEG short chains. The size and number density of free volume domains in the PVP–PEG blends were determined as functions of blend composition and relative humidity of the surrounding atmosphere, which controls the amount of absorbed water. Correlating the free volume, adhesion behaviors, and tensile properties of the blends, the range of free volume favoring pressure‐sensitive adhesion in examined compositions was established. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Impacts of the periodic wall conditions on the hydromagnetic convective flow of viscoelastic fluid through a vertical channel with Hall current and induced magnetic field

In this study, a mathematical analysis is presented for the hydromagnetic convective flow of an incompressible, chemically reacting, and electrically and thermally conducting viscoelastic fluid through a vertical channel bounded by the porous regime under the action of an applied magnetic field with Hall current and induced magnetic field effects. The left wall of the channel is considered to be nonmagnetic, whereas the right wall of the channel is periodically magnetized. The flow within the channel is induced due to the nonuniform wall temperature and concentration, periodic pressure gradient, and periodic movement of the right wall. The method of separation of variable is used to convert the flow governing coupled partial differential equations into the ordinary differential equations that are solved analytically, and the solution for fluid velocity, induced magnetic field, temperature, and concentration is presented in a closed form. Numerical computation has been performed to demonstrate the impact of various system parameters on the fluid flow behavior. It is observed that oscillations increase the primary flow and primary induced magnetic field. Buoyancy forces have a tendency to lessen the secondary induced magnetic field. Furthermore, it is examined that magnetic diffusivity increases the primary flow, whereas it decreases the secondary flow and primary induced magnetic field.     

An intelligent hierarchical residual attention learning-based conjoined twin neural network for Alzheimer's stage detection and prediction

Alzheimer's disorder (AD) causes permanent impairment in the brain's memory of the cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease in the initial stages is challenging for researchers. Deep learning and machine learning-based techniques can help resolve many issues associated with brain imaging exploration. Brain MR Images (Brain-MRI) are used to detect Alzheimer's in computable research work. To correctly categorize the stages of Alzheimer's disease, discriminative features need to be extracted from the MR images. Recently, many studies have used deep learning methods for the early detection of this disorder. However, overfitting degrades the deep learning method's performance because the dataset's selection images are smaller and imbalanced. Some studies could not reach more discriminative and effectual attention-aware features for Alzheimer's stage classification to increase the model performance. In this paper, we develop a novel hierarchical residual attention learning-inspired multistage conjoined twin network (HRAL-CTNN) to classify the stages of Alzheimer's. We used augmentation approaches to scale insufficient and imbalanced data. The HRAL-CTNN is efficiently overcoming the issues of not obtaining efficient attention-aware and generative features for Alzheimer's stage classification. The proposed model solved the problem of redundant features by extracting attentive discriminant features, and scaling imbalance data by data augmentation, after that training and validation using HRAL-CTNN. The execution of this proposed work has been performed on the ADNI MRI dataset. This work achieved outstanding accuracy of 99.97 $\pm$ 0.01% and F1 score of 99.30 $\pm$ 0.02% for Alzheimer's stage classification. This model proposed by our group outperformed the existing related studies in terms of the model's performance score.