734 research outputs found
A Survey of Operations Research and Analytics Literature Related to Anti-Human Trafficking
Human trafficking is a compound social, economic, and human rights issue
occurring in all regions of the world. Understanding and addressing such a
complex crime requires effort from multiple domains and perspectives. As of
this writing, no systematic review exists of the Operations Research and
Analytics literature applied to the domain of human trafficking. The purpose of
this work is to fill this gap through a systematic literature review. Studies
matching our search criteria were found ranging from 2010 to March 2021. These
studies were gathered and analyzed to help answer the following three research
questions: (i) What aspects of human trafficking are being studied by
Operations Research and Analytics researchers? (ii) What Operations Research
and Analytics methods are being applied in the anti-human trafficking domain?
and (iii) What are the existing research gaps associated with (i) and (ii)? By
answering these questions, we illuminate the extent to which these topics have
been addressed in the literature, as well as inform future research
opportunities in applying analytical methods to advance the fight against human
trafficking.Comment: 28 pages, 6 Figures, 2 Table
A meta-architecture analysis for a coevolved system-of-systems
Modern engineered systems are becoming increasingly complex. This is driven in part by an increase in the use of systems-of-systems and network-centric concepts to improve system performance. The growth of systems-of-systems allows stakeholders to achieve improved performance, but also presents new challenges due to increased complexity. These challenges include managing the integration of asynchronously developed systems and assessing SoS performance in uncertain environments.
Many modern systems-of-systems must adapt to operating environment changes to maintain or improve performance. Coevolution is the result of the system and the environment adapting to changes in each other to obtain a performance advantage. The complexity that engineered systems-of-systems exhibit poses challenges to traditional systems engineering approaches. Systems engineers are presented with the problem of understanding how these systems can be designed or adapted given these challenges. Understanding how the environment influences system-of-systems performance allows systems engineers to target the right set of capabilities when adapting the system for improved performance.
This research explores coevolution in a counter-trafficking system-of-systems and develops an approach to demonstrate its impacts. The approach implements a trade study using swing weights to demonstrate the influence of coevolution on stakeholder value, develops a novel future architecture to address degraded capabilities, and demonstrates the impact of the environment on system performance using simulation. The results provide systems engineers with a way to assess the impacts of coevolution on the system-of-systems, identify those capabilities most affected, and explore alternative meta-architectures to improve system-of-systems performance in new environments --Abstract, page iii
Unraveling the intricacies of spatial organization of the ErbB receptors and downstream signaling pathways
Faced with the complexity of diseases such as cancer which has 1012 mutations, altering gene expression, and disrupting regulatory networks, there has been a paradigm shift in the biological sciences and what has emerged is a much more quantitative field of biology. Mathematical modeling can aid in biological discovery with the development of predictive models that provide future direction for experimentalist. In this work, I have contributed to the development of novel computational approaches which explore mechanisms of receptor aggregation and predict the effects of downstream signaling. The coupled spatial non-spatial simulation algorithm, CSNSA is a tool that I took part in developing, which implements a spatial kinetic Monte Carlo for capturing receptor interactions on the cell membrane with Gillespies stochastic simulation algorithm, SSA, for temporal cytosolic interactions. Using this framework we determine that receptor clustering significantly enhances downstream signaling. In the next study the goal was to understand mechanisms of clustering. Cytoskeletal interactions with mobile proteins are known to hinder diffusion. Using a Monte Carlo approach we simulate these interactions, determining at what cytoskeletal distribution and receptor concentration optimal clustering occurs and when it is inhibited. We investigate oligomerization induced trapping to determine mechanisms of clustering, and our results show that the cytoskeletal interactions lead to receptor clustering. After exploring the mechanisms of clustering we determine how receptor aggregation effects downstream signaling. We further proceed by implementing the adaptively coarse grained Monte Carlo, ACGMC to determine if \u27receptor-sharing\u27 occurs when receptors are clustered. In our proposed \u27receptor-sharing\u27 mechanism a cytosolic species binds with a receptor then disassociates and rebinds a neighboring receptor. We tested our hypothesis using a novel computational approach, the ACGMC, an algorithm which enables the spatial temporal evolution of the system in three dimensions by using a coarse graining approach. In this framework we are modeling EGFR reaction-diffusion events on the plasma membrane while capturing the spatial-temporal dynamics of proteins in the cytosol. From this framework we observe \u27receptor-sharing\u27 which may be an important mechanism in the regulation and overall efficiency of signal transduction. In summary, I have helped to develop predictive computational tools that take systems biology in a new direction.\u2
Evidential Reasoning & Analytical Techniques In Criminal Pre-Trial Fact Investigation
This thesis is the work of the author and is concerned with the development of a neo-Wigmorean approach to evidential reasoning in police investigation. The thesis evolved out of dissatisfaction with cardinal aspects of traditional approaches to police investigation, practice and training. Five main weaknesses were identified: Firstly, a lack of a theoretical foundation for police training and practice in the investigation of crime and evidence management; secondly, evidence was treated on the basis of its source rather than it's inherent capacity for generating questions; thirdly, the role of inductive elimination was underused and misunderstood; fourthly, concentration on single, isolated cases rather than on the investigation of multiple cases and, fifthly, the credentials of evidence were often assumed rather than considered, assessed and reasoned within the context of argumentation. Inspiration from three sources were used to develop the work: Firstly, John Henry Wigmore provided new insights into the nature of evidential reasoning and formal methods for the construction of arguments; secondly, developments in biochemistry provided new insights into natural methods of storing and using information; thirdly, the science of complexity provided new insights into the complex nature of collections of data that could be developed into complex systems of information and evidence. This thesis is an application of a general methodology supported by new diagnostic and analytical techniques. The methodology was embodied in a software system called Forensic Led Intelligence System: FLINTS. My standpoint is that of a forensic investigator with an interest in how evidential reasoning can improve the operation we call investigation. New areas of evidential reasoning are in progress and these are discussed including a new application in software designed by the author: MAVERICK. There are three main themes; Firstly, how a broadened conception of evidential reasoning supported by new diagnostic and analytical techniques can improve the investigation and discovery process. Secondly, an explanation of how a greater understanding of the roles and effects of different styles of reasoning can assist the user; and thirdly; a range of concepts and tools are presented for the combination, comparison, construction and presentation of evidence in imaginative ways. Taken together these are intended to provide examples of a new approach to the science of evidential reasoning. Originality will be in four key areas; 1. Extending and developing Wigmorean techniques to police investigation and evidence management. 2. Developing existing approaches in single case analysis and introducing an intellectual model for multi case analysis. 3. Introducing a new model for police training in investigative evidential reasoning. 4. Introducing a new software system to manage evidence in multi case approaches using forensic scientific evidence. FLINTS
Scientific inquiry and the causes of civil war : the feasibility thesis and beyond
This Master's Thesis uses Collier, Hoeffler, and Rohner's (2009) Feasibility Thesis as a field for a thorough inquiry into the question of how we can best study the causes of civil war. We advocate a pluralist research strategy, with set-theoretic (especially fuzzy-set) methods as a key component, as the most promising strategy for the study of the causes of civil war
Security Enhanced Applications for Information Systems
Every day, more users access services and electronically transmit information which is usually disseminated over insecure networks and processed by websites and databases, which lack proper security protection mechanisms and tools. This may have an impact on both the users’ trust as well as the reputation of the system’s stakeholders. Designing and implementing security enhanced systems is of vital importance. Therefore, this book aims to present a number of innovative security enhanced applications. It is titled “Security Enhanced Applications for Information Systems” and includes 11 chapters. This book is a quality guide for teaching purposes as well as for young researchers since it presents leading innovative contributions on security enhanced applications on various Information Systems. It involves cases based on the standalone, network and Cloud environments
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Improving Computer Network Operations Through Automated Interpretation of State
Networked systems today are hyper-scaled entities that provide core functionality for distributed services and applications spanning personal, business, and government use. It is critical to maintain correct operation of these networks to avoid adverse business outcomes. The advent of programmable networks has provided much needed fine-grained network control, enabling providers and operators alike to build some innovative networking architectures and solutions. At the same time, they have given rise to new challenges in network management. These architectures, coupled with a multitude of devices, protocols, virtual overlays on top of physical data-plane etc. make network management a highly challenging task. Existing network management methodologies have not evolved at the same pace as the technologies and architectures. Current network management practices do not provide adequate solutions for highly dynamic, programmable environments. We have a long way to go in developing management methodologies that can meaningfully contribute to networks becoming self-healing entities. The goal of my research is to contribute to the design and development of networks towards transforming them into self-healing entities.
Network management includes a multitude of tasks, not limited to diagnosis and troubleshooting, but also performance engineering and tuning, security analysis etc. This research explores novel methods of utilizing network state to enhance networking capabilities. It is constructed around hypotheses based on careful analysis of practical deficiencies in the field. I try to generate real-world impact with my research by tackling problems that are prevalent in deployed networks, and that bear practical relevance to the current state of networking. The overarching goal of this body of work is to examine various approaches that could help enhance network management paradigms, providing administrators with a better understanding of the underlying state of the network, thus leading to more informed decision-making. The research looks into two distinct areas of network management, troubleshooting and routing, presenting novel approaches to accomplishing certain goals in each of these areas, demonstrating that they can indeed enhance the network management experience
Daftar Ebook Engineering Science Terbitan Springer Tahun 2018
Artikel ini memuat daftar judul ebook bidang ilmu teknik yang diterbitkan oleh Springer pada tahun 2018 yang dimiliki oleh Unand
Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques
Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions
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