Bayesian Model Based Tracking with Application to Cell Segmentation and Tracking

The goal of this research is to develop a model-based tracking framework with biomedical imaging applications. This is an interdisciplinary area of research with interests in machine vision, image processing, and biology. This thesis presents methods of image modeling, tracking, and data association applied to problems in multi-cellular image analysis, especially hematopoietic stem cell (HSC) images at the current stage. The focus of this research is on the development of a robust image analysis interface capable of detecting, locating, and tracking individual hematopoietic stem cells (HSCs), which proliferate and differentiate to different blood cell types continuously during their lifetime, and are of substantial interest in gene therapy, cancer, and stem-cell research. Such a system can be potentially employed in the future to track different groups of HSCs extracted from bone marrow and recognize the best candidates based on some biomedical-biological criteria. Selected candidates can further be used for bone marrow transplantation (BMT) which is a medical procedure for the treatment of various incurable diseases such as leukemia, lymphomas, aplastic anemia, immune deficiency disorders, multiple myeloma and some solid tumors. Tracking HSCs over time is a localization-based tracking problem which is one of the most challenging tracking problems to be solved. The proposed cell tracking system consists of three inter-related stages: i) Cell detection/localization, ii) The association of detected cells, iii) Background estimation/subtraction. that will be discussed in detail

A data science approach for quantifying spatio-temporal effects to graft failures in organ transplantation

This is the author accepted manuscript. The final version is available from IEEE via the DOI in tis recordThe transplantation of solid organs is one of the most important accomplishments of modern medicine. Yet, organ shortage is a major public health issue; 8,000 people died while waiting for an organ in 2014. Meanwhile, the allocation system currently implemented can lead to organs being discarded and the medical community still investigates factors that affects early graft failure such as distance and ischemic time. In this paper, we investigate early graft failure under a spatio-temporal perspective using a data science unified approach for all six organs that is based on complementary cumulative analysis of both distance and ischemic time. Interestingly, although distance seems to highly affect some organs (e.g. liver), it appears to have no effect on others (e.g. kidney). Similarly, the results on ischemic time confirm it affects early graft failure with higher influence for some organs such as (e.g. heart) and lower influence for others such as (e.g. kidney). This poses the question whether the allocation policies should be individually designed for each organ in order to account for their particularities as shown in this work

Sustainable supply chain management towards disruption and organizational ambidexterity:A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

The world is moving toward a new connected world in which millions of intelligent processing devices communicate with each other to provide services in transportation, telecommunication, and power grids in the future’s smart cities. Distributed computing is considered one of the efficient platforms for processing and management of massive amounts of data collected by smart devices. This can be implemented by utilizing multi-agent systems (MASs) with multiple autonomous computational entities by memory and computation capabilities and the possibility of message-passing between them. These systems provide a dynamic and self-adaptive platform for managing distributed large-scale systems, such as the Internet-of-Things (IoTs). Despite, the potential applicability of MASs in smart cities, very few practical systems have been deployed using agent-oriented systems. This research surveys the existing techniques presented in the literature that can be utilized for implementing adaptive multi-agent networks in smart cities. The related literature is categorized based on the steps of designing and controlling these adaptive systems. These steps cover the techniques required to define, monitor, plan, and evaluate the performance of an autonomous MAS. At the end, the challenges and barriers for the utilization of these systems in current smart cities, and insights and directions for future research in this domain, are presented