An Application of Possibilistic Moments of Nonlinear Type of Fuzzy Numbers in Supply Chain Management

The supply chain (SC) network is prone to disturbance due to various uncertainties associated with their subsystems. The COVID-19 outbreak has exposed the global vulnerability of the supply chain network. The current pandemic has severely affected almost every SC network because its members are situated at the international level. One of the reasons for SC network failure is the deterministic assumptions of different parameters. A realistic SC network model requires the use of the uncertain value of the parameters, which can be further captured by fuzzy numbers. This paper discusses the possibilistic moment of several nonlinear types of fuzzy numbers that are important for SC network modeling. We give closed-form possibilistic moments’ expression for various types of fuzzy numbers that are very similar to the moment’s properties in probability theory and stochastic process. We then illustrate the application of proposed fuzzy numbers by solving an inventory model. This paper also provides results related to the EPQ inventory model in a fuzzy possibilistic setup.

A Structural Connectivity Disruption One Decade before the Typical Age for Dementia: A Study in Healthy Subjects with Family History of Alzheimer’s Disease

The concept of the brain has shifted to a complex system where different subnetworks support the human cognitive functions. Neurodegenerative diseases would affect the interactions among these subnetworks and, the evolution of impairment and the subnetworks involved would be unique for each neurodegenerative disease. In this study, we seek for structural connectivity traits associated with the family history of Alzheimer’s disease, i.e., early signs of subnetworks impairment due to Alzheimer’s disease.3. The sample in this study consisted of 123 first-degree Alzheimer’s disease relatives and 61 non-relatives. For each subject, structural connectomes were obtained using classical diffusion tensor imaging measures and different resolutions of cortical parcellation. For the whole sample, independent structural-connectome-traits were obtained under the framework of connICA. Finally, we tested the association of the structural-connectome-traits with different factors of relevance for Alzheimer’s disease by means of a multiple linear regression. The analysis revealed a structural-connectome-trait obtained from fractional anisotropy associated with the family history of Alzheimer’s disease. The structural-connectome-trait presents a reduced fractional anisotropy pattern in first-degree relatives in the tracts connecting posterior areas and temporal areas. The family history of Alzheimer’s disease structural-connectome-trait presents a posterior–posterior and posterior-temporal pattern, supplying new evidences to the cascading network failure model.

Shifting Perceptions: Lessons from an exploratory study of the Inside Criminal Justice Program

This is an exploratory study of the efficacy of Inside Criminal Justice (ICJ), an eight-week long course for incarcerated students and public prosecutors that fosters respectful and open discussions about the criminal-legal system in order to rethink public safety. ICJ’s impact falls within applied contact theory, intergroup relations, and interventions aimed at reducing bias. The ICJ course was founded in 2018 as a joint initiative between three key institutions in the Tri-State Area (New York, New Jersey, Connecticut): the District Attorney’s Office, the Department of Corrections and Community Supervision (DOCCS), and Columbia University. ICJ brings current prosecutors inside correctional facilities to learn about and discuss issues of criminal justice alongside incarcerated students while developing joint policy proposals centered on improving the justice system. This program is the first of its kind in the area. Through semi-structured interviews of students and prosecutors from across the seven cohorts and qualitative data analysis, this dissertation considers the impact of ICJ on three domains: pre-existing beliefs related to the justice system, changes to concrete behaviors, and network building/transformation. It presents evidence of a reciprocal humanizing experience for its participants that helps shift perceptions, as suggested by Allport's (1954) contact hypothesis. It then examines the translation of that shift to individual and larger-scale changes resulting from ICJ involvement, and explores the distinct forms of credibility ICJ networks give to its participants as well as the ways network failure contribute to justice involvement. There is limited literature about programs that bring these types of criminal justice actors together in an educational setting. In addition to informing a larger, more systematic program evaluation, this exploratory study identifies key themes for future exploration, and presents evidence about the program’s efficacy.

A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults

The domain adaptation problem in transfer learning has received extensive attention in recent years. The existing transfer model for solving domain alignment always assumes that the label space is completely shared between domains. However, this assumption is untrue in the actual industry and limits the application scope of the transfer model. Therefore, a universal domain method is proposed, which not only effectively reduces the problem of network failure caused by unknown fault types in the target domain but also breaks the premise of sharing the label space. The proposed framework takes into account the discrepancy of the fault features shown by different fault types and forms the feature center for fault diagnosis by extracting the features of samples of each fault type. Three optimization functions are added to solve the negative transfer problem when the model solves samples of unknown fault types. This study verifies the performance advantages of the framework for variable speed through experiments of multiple datasets. It can be seen from the experimental results that the proposed method has better fault diagnosis performance than related transfer methods for solving unknown mechanical faults.

A Rapid Transition from Subversion to Git: Time, Space, Branching, Merging, Offline commits & Offline builds and Repository aspects

Background: Software development is being transitioned from centralized to decentralized version control systems. This transition is driven by the limited features of centralized version control systems in terms of branching, merging, time, space, offline commits & builds, and repository aspects. Transition from Subversion, a centralized version control system, to Git, a decentralized version control system, has been focused in a limited way. Objective: In this work, the transition process from Subversion Version Control System (VCS) to Git VCS has been investigated in terms of time, space, branching, merging, and repository aspects from the software developer point of view working individually or in a large team over a large and complex software having a legacy of many decades. Experimentation was conducted in SRLC Software Research Lab, Chicago, USA. Methods: Various scripts have been developed and executed for version control using Git and performed over a few legacy software. Results: The results show that branching in Git and Subversion has a difference of about 39 times, i.e., branching operation of Git is about 39 times faster than Subversion. Merging in the case of Git is trivial and automatic, while Subversion needs a manual process of merging, which is error-prone. Using an example of Mozilla with FSFS backend, it is observed that disk space can be saved up to 30 times in Git compared to Subversion. By taking a typical example of a large-sized project, it is observed that Git requires almost half of the revisions compared to Subversion; furthermore, with FSFS backend, a project having ten years of history with 240,000 commits needs 240 directories in case of Subversion, while Git requires only 2 directories. Using offline commits and offline builds of Git, it is observed that in Git, whitespace changes, in contrast to significant business logic changes, can be staged in one commit only. These are not possible in Subversion, which requires a complicated system of diffing to temporary files. It is also observed that Git provides an offline commit facility, i.e., in case if, for some reason, the remote repository is unavailable due to disaster or network failure, then developers can still offline commit their code and execute the offline build. Conclusion: However, no previous study was found that focused on how the choice actually affects software developers and this formed the motivation for the present work. In this work, a list of how the choice between Git and Subversion affects software developers is worked out. Although software developers in many aspects are unaffected by the choice, few interesting findings were achieved. One of the most interesting findings of the proposed work is that software developers seem to publish their code to the main repository more often in Git than in Subversion. It is also found that the majority of the software developers perform at least two commits per push, which means that Git repositories will contain a lot more saved points in history than Subversion repositories.

A Nonlinear Model For The Optimal Capacity Placement In Self-Healing ATM Networks Based On Link Restoration Strategy

Network Survivability is a critical issue in telecommunications network due to increasing dependence of the society on communication systems. Fast restoration from a network failure is an important challenge that deserves attention. This thesis addresses an optimal link capacity design problem for survivable asynchronous transfer mode (ATM) network based on the link restoration strategy. Given a projected traffic demands and the network topology, capacity and flow assignment are jointly optimized to yield the optimal capacity placement. The problem is formulated as large-scale nonlinear programming and is solved using a specific type of Lagrange method (so called Separable Augmented Lagrangian Algorithm or SALA for short). Several networks with diverse topological characteristics are used in the experiments to validate our proposed novel model, using capacity installation cost, routing cost, total network cost, used capacity and required CPU time, as performance metrics. Link restoration strategy is compared against global reconfiguration strategy using these performance metrics.

A Nonlinear Model For The Optimal Capacity Placement In Self-Healing ATM Networks Based On Link Restoration Strategy

Network Survivability is a critical issue in telecommunications network due to increasing dependence of the society on communication systems. Fast restoration from a network failure is an important challenge that deserves attention. This thesis addresses an optimal link capacity design problem for survivable asynchronous transfer mode (ATM) network based on the link restoration strategy. Given a projected traffic demands and the network topology, capacity and flow assignment are jointly optimized to yield the optimal capacity placement. The problem is formulated as large-scale nonlinear programming and is solved using a specific type of Lagrange method (so called Separable Augmented Lagrangian Algorithm or SALA for short). Several networks with diverse topological characteristics are used in the experiments to validate our proposed novel model, using capacity installation cost, routing cost, total network cost, used capacity and required CPU time, as performance metrics. Link restoration strategy is compared against global reconfiguration strategy using these performance metrics.