Transportation Network Resiliency: A Study of Self-Annealing

Transportation networks, as important lifelines linking communities and goods, are indispensable for the smooth functioning of society. These networks are, however, fragile and vulnerable to natural and manmade disasters, which can disrupt their vital functionality. The role of the transportation sector becomes more crucial during disasters due to its role in pre-disaster evacuation as well as post-disaster recovery. The ability of transportation systems to retain performance during and after disasters undergoing little to no loss and their ability to return to the normal state of operation quickly after disasters defines their resilience. Authorities need to understand the degree of resilience within the transportation system under their jurisdiction and plan for improvements. In this research, attempts have been made to deal with resilience in quantitative ways to provide defensible data to decision makers to support investment strategies. Total loss in the network performance can be quantified by dealing with the variation of network performance over time after disasters and the network resilience can be measured by the ability to minimize this loss. It has been shown that robust networks retain better performance after disruptions and recovery works, which follow optimized recovery paths, in spite of constraints of resources and time, help to minimize the total losses and enhance the network resilience. The objective of this research is to create a conceptual framework to quantify resilience and discuss quantitatively the properties determining resilience of transportation networks. The concepts presented are applied to a test network to illustrate the mathematical procedures. Such methods can help decision makers analyze relative improvements in resiliency as a consequence of proposed project alternatives and help to perform benefit-cost analysis for such projects

Use of Case Histories to Enhance Practical Geotechnical Engineering

Mathematical models are constructed to describe the behavior of engineering systems in quantitative terms. During conceptualization stage of modelling several valid assumptions have to be made so as to make the model predict the behavior of the system as accurately as possible. Refinement of mathematical models need feed back from practice. Many practical cases are of interest in updating and enhancing quantitative judgment of geo-technical systems behavior. This paper envisages to present a few interesting cases where the situation forced true synthesis of theory and practice for innovation and advancement of practical geo-technical engineering

Study of clustering algorithms for Gene expression analysis

Data Mining refers to as \the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable pattern in data". Based on the type of knowledge that is mined, data mining can be classi¯ed in to di®erent models such as Clustering, Decision trees, Association rules, and Sequential pattern and time series. In this thesis work, an attempt has been made to study theoretical background and applications of Clustering techniques in data mining with a special emphasis on analysis of Gene Expression under Bioinformatics. Bioinformatics is the study of genetic and other biological information using computer and statistical techniques. DNA microarray technology has now made it possible to simul- taneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. A °ood of data means that many of the challenges in biology are now challenges in computing. A ¯rst step toward addressing this challenging is the use of clustering technique, which is essential in the data mining process to reveal natural structures and identifying interesting patterns in the underlying data. In this thesis work, e®ort has been made to compare between few Clustering algorithms such as: K means, Hierarchical, Self Organization Map(SOM), and Cluster A±nity Search Technique(CAST) with proposed algorithm called CAST+. Strengths and Weaknesses of the above Clustering algorithms are identi¯ed and drawbacks like knowing number of clusters before clustering, and taking a±nity threshold as input from the users are recti¯ed by the proposed algorithm. Results show that Proposed Algorithm is e±cient in comparison with other Clustering algorithms mentioned above. The Clustering algorithms are compared on the basis of few Evaluation Indices such as Homogeneity Vs separation, and Silhouette width

Exceptional Response with Immunotherapy in a Patient with Anaplastic Thyroid Cancer

Chemotherapy with or without radiation is the standard therapy for anaplastic thyroid cancer (ATC), although the response rate is not high and not durable. We describe a 62-year-old male who was diagnosed with ATC and initially treated with a thyroidectomy and lymph node dissection, followed by chemotherapy. Next generation sequencing was then performed to guide therapy and the tumor was found to have BRAF and programmed death-ligand 1 (PD-L1) positivity that was subsequently treated with vemurafenib and nivolumab. This led to substantial regression of tumor nodules. Genomic sequencing-based approaches to identify therapeutic targets has potential for improving outcomes. Currently, the patient continues to be in complete radiographic and clinical remission 20 months after beginning treatment with nivolumab. KEY POINTS: Programmed death-1 (PD-1)/PD-L1 immunotherapy has shown evidence of durable responses in certain malignancies such as melanoma, lung cancer, and renal cell carcinoma.PD-L1 positive tumors promote autoimmunity against the tumor; therefore, PD-1/PD-L1 blockade may be beneficial.Molecular profiling could possibly result in improved targeted therapy for certain malignancies