bluuebunny/crossref_metadata_2025_split huggingface dataset:
How side‐chain hydrophilicity modulates morphology and charge transport in mixed conducting polymers
Authors: Khot Aditi , Savoie Brett M. 
Published: December 2021

AbstractOrganic mixed ionic‐electronic conductors (OMIECs) are a developing class of organic electronic materials distinguished by their dual modes of conduction. The side‐chains of OMIEC polymers are responsible for forming a percolating electrolyte phase that mediates doping and ionic conduction. Despite this critical role, design rules for OMIEC side‐chains are still nascent and their effects on OMIEC morphology and charge transport have yet to be systematically studied. Here we perform the first dedicated coarse‐grained molecular dynamics study of OMIECs where the side‐chain identity and distribution are systematically varied using a random copolymer architecture. The simulations recapitulate the nonlinear progression of the morphology from an interfacially gated electrolyte when large fractions of hydrophobic side‐chains are incorporated, to an electrolyte swelled morphology after crossing a threshold of approximately 40% polar side‐chains. Kinetic Monte Carlo simulations were used to characterize the charge transport behaviors in these systems, revealing two interesting maxima in the mobility at 40% and 100% polar side‐chain fractions, respectively. With respect to maximizing the charge mobility and conductivity, these simulations suggest that a uniform hydrophilic side‐chain distribution is optimal and that there are few advantages to using mixed side‐chains in a random copolymer architecture. These results also suggest several alternative side‐chain engineering strategies for optimizing OMIEC performance.
Public corruption and the allocation of government contracts
Authors: Wei Zuobao , Zhu Yicheng 
Published: December 2021

AbstractPublic corruption in the government procurement process is rampant and its cost is huge, even among developed countries. Some scholars estimate that about 20%–30% of the values of government projects are lost due to public corruption. In this paper, we examine how public corruption impacts the allocation of U.S. federal contracts. Using the U.S. Department of Justice corruption convictions data and the federal contract data from 2000 to 2018, we find that firms located in more corrupt states receive more federal contract dollars, more important contracts in terms of their contributions to firm revenues, and contracts with higher visibility among federal contractors. We construct an influence/favoritism index that takes into account defense contracts, cost‐plus contracts, and multi‐year contracts, and document that the index is positively related to corruption levels. These results hold after we conduct several robustness tests, including 2SLS regressions, propensity‐score matching analysis, and using alternative corruption measures. Our empirical findings are consistent with the hypothesis that corruption plays an important role in how federal contracts are allocated.
The impact of irrational behaviors in the optional prisoner's dilemma with game‐environment feedback
Authors: Stella Leonardo , Bauso Dario 
Published: December 2021

AbstractIn the optional prisoner's dilemma (OPD), players can choose to cooperate and defect as usual, but can also abstain as a third possible strategy. This strategy models the players' participation in the game and is a relevant aspect in many settings, for example, social networks or opinion dynamics, for example, during an election abstention is an option. In this article, we provide a formulation of the OPD where we consider irrational behaviors in the population, inspired by prospect theory. Prospect theory has gained increasing popularity in recent times because it can capture aspects such as reference dependence or loss aversion, which are common in human behavior. This element is original in our formulation of the game and is incorporated in our framework through pairwise comparison dynamics. Recently, the impact of the environment has been studied in the form of feedback on the population dynamics. Another element of novelty in our work is the extension of the game‐environment feedback to the OPD in two forms of dynamics, the replicator and the pairwise comparison. The contribution of this article is threefold. First, we propose a modeling framework where prospect theory is used to capture irrational behaviors in an evolutionary game with game‐environment feedback. Second, we carry out the stability analysis of the system equilibria and discuss the oscillating behaviors arising from the game‐environment feedback. Finally, we extend our previous results to the OPD and we discuss the main differences between the model resulting from the replicator dynamics and the one resulting from the pairwise comparison dynamics.
Evolution of supply chain finance: A comprehensive review and proposed research directions with network clustering analysis
Authors: Parida Ratri , Dash Manoj Kumar , Kumar Anil , Zavadskas Edmundas Kazimieras , Luthra Sunil , Mulat‐weldemeskel Eyob 
Published: December 2021

AbstractOver the last decade, supply chain finance (SCF) has gained popularity and increasing attention among academicians and stakeholders in the context of financial flows in the supply chain. However, some research gaps still exist that need to be explored to improve the sustainability of supply chains. Specifically, there is a critical research need to look at the conceptual background of SCF and its potential applicability in various phases of supply chains. Therefore, this article aims to bridge this gap by conducting a comprehensive State‐of‐the‐Art literature review based on 367 papers published from 2006 to 2020. Furthermore, this article is one of the first attempts to present current and past studies in the domain of SCF in a holistic manner. The analysis highlights the most influential authors, keywords, organisations, leading publications and clusters in existing research areas. This article also sets out a proposed research framework based on the triangulation approach perspective, that is, financial perspective, buyer perspective and supply chain‐oriented perspective. The most important and unique contribution of the article is the identification of new and emerging research areas where the application of SCF is still in the nascent stage. These findings can guide stakeholders at every stage of the value chain to appropriately use techniques that model policies to better inform investment and operational decisions in line with Sustainable Development Goals.
Puffing Up Hollow Carbon Nanofibers with High‐Energy Metal‐Organic Frameworks for Capacitive‐Dominated Potassium‐Ion Storage
Authors: Wu Yingxiao , Cheng Jinqian , Liang Zibin , Tang Yanqun , Qiu Tianjie , Gao Song , Zhong Ruiqin , Zou Ruqiang 
Published: December 2021

AbstractNitrogen‐doped carbon materials with abundant defects and strong potassium adsorption ability have been recognized as potential anodes for potassium ion batteries (PIBs). However, the limited content and uncontrolled type of nitrogen‐doped sites hinder the further performance improvement of PIBs. Herein, this work proposes nitrogen phosphorous co‐doped hollow carbon nanofibers (PNCNFs) derived from high‐energy metal‐organic frameworks (MOFs) with an ultra‐high nitrogen content of 19.52 wt% and a high portion of more reactive pyridinic N sites. Furthermore, the highly open architecture exploded by released gases from high‐energy MOFs provides sufficient edge sites to settle the N atoms and further form pyridinic N sites induced by phosphorous dopants. The resulting PNCNFs achieve excellent potassium ion storage performance with high reversible capacity (466.2 mAh g−1), superb rate capability (244.4 mAh g−1 at 8 A g−1), and excellent cycling performance (294.6 mAh g−1 after 3250 cycles). The density functional theory calculation reveals that the N/P defects enhance the potassium adsorption ability and improve the conductivity.
Fabrication of Large‐Area Uniform Nanometer‐Thick Functional Layers and Their Stacks for Flexible Quantum Dot Light‐Emitting Diodes
Authors: Zhang Weimin , Du Jinhong , Wei Qinwei , Zhang Dingdong , Pei Songfeng , Tong Bo , Liu Zhibo , Liang Yan , Cheng Hui‐Ming , Ren Wencai 
Published: December 2021

AbstractLarge‐area fabrication and stacking of various nanometer‐thick functional layers from solutions is essentially important for the construction of flexible thin‐film optoelectronic devices, but very challenging. The existing fabrication methods suffer from either non‐uniformity caused by the coffee‐ring effect or serious solution waste (excess of 90% for spin coating), and are hard to scale up and create stacks. Here, it is shown that centrifugal casting is a universal, scalable, and efficient method to fabricate uniform nanometer‐thick films and their stacks of various materials. The coffee‐ring effect is effectively suppressed, the solution utilization ratio is higher than ≈61%, and the films/stacks show a smooth surface/high‐quality interface. Using this method, flexible quantum dot light‐emitting diode displays with uniform luminance in a large lighting area of ≈115 cm2 that have not been achieved even on rigid substrates by the existing methods, are realized. This efficient and low‐cost solution processing method paves a way for large‐area fabrication of various flexible thin‐film optoelectronic devices.
The Effects of Topic Familiarity on Text Quality, Complexity, Accuracy, and Fluency: A Conceptual Replication
Authors: Kessler Matt , Ma Wenyue , Solheim Ian 
Published: December 2021

AbstractUnderstanding the effects of second language writers’ topic familiarity on their subsequent writing performance has been a subject of great interest to researchers in second language acquisition and TESOL. However, prior studies have tended to suffer from multiple methodological limitations (e.g., no interrater reliability, limited or no control over important variables), which may call into question their results. In this conceptual replication, we highlight some of these issues by revisiting an influential study by He and Shi (2012), which explored the effects of ESL students’ topic familiarity on their writing performance as assessed by measures of text quality and complexity, accuracy, and fluency (CAF). Apart from highlighting the differences in our studies’ designs (e.g., the CAF measures and statistical analyses used), we highlight the similarities and differences in our findings. Finally, we close with a call for increased replication research in this area.
Toward a common language for quality issues in forensic science
Authors: Heavey Anna L. , Turbett Gavin R. , Houck Max M. , Lewis Simon W. 
Published: December 2021

AbstractThe call for transparency with regards to the disclosure of errors in forensic science has permeated dialogues within the field, the legal arena and the public domain. While the challenges of meeting this need have been debated, no consistently accepted approach to recording or disclosing critical issues in forensic science has been developed. Part of the solution may lie in the quality system structures already present in forensic service providers world‐wide. However, while quality systems themselves may be standardized, the language used within them is not. We argue that the development of a common language for quality issues in forensic science may provide the key to unlocking this crucial information to support collaboration, continuous improvement, and the fundamental understanding of “error” in forensic science.This article is categorized under:
Forensic Chemistry and Trace Evidence > Trace Evidence
Jurisprudence and Regulatory Oversight > Communication Across Science and Law
Jurisprudence and Regulatory Oversight > Interdisciplinary Collaboration
Forensic Chemistry and Trace Evidence > Presentation and Evaluation of Forensic Science Output
Nearest‐neighbor sparse Cholesky matrices in spatial statistics
Authors: Datta Abhirup 
Published: December 2021

AbstractGaussian process (GP) is a staple in the toolkit of a spatial statistician. Well‐documented computing roadblocks in the analysis of large geospatial datasets using GPs have now largely been mitigated via several recent statistical innovations. Nearest neighbor Gaussian process (NNGP) has emerged as one of the leading candidates for such massive‐scale geospatial analysis owing to their empirical success. This article reviews the connection of NNGP to sparse Cholesky factors of the spatial precision (inverse‐covariance) matrix. Focus of the review is on these sparse Cholesky matrices which are versatile and have recently found many diverse applications beyond the primary usage of NNGP for fast parameter estimation and prediction in the spatial (generalized) linear models. In particular, we discuss applications of sparse NNGP Cholesky matrices to address multifaceted computational issues in spatial bootstrapping, simulation of large‐scale realizations of Gaussian random fields, and extensions to nonparametric mean function estimation of a GP using random forests. We also review a sparse‐Cholesky‐based model for areal (geographically aggregated) data that addresses long‐established interpretability issues of existing areal models. Finally, we highlight some yet‐to‐be‐addressed issues of such sparse Cholesky approximations that warrant further research.This article is categorized under:Algorithms and Computational Methods > AlgorithmsAlgorithms and Computational Methods > Numerical Methods
From Royal Canal to Neglected Canal? Changing Use and Management of a Traditional Canal Irrigation System in Peri-Urban Kathmandu Valley
Authors: Shrestha Anushiya , Roth Dik , Yakami Saroj 
Published: December 2021

AbstractIn this chapter we discuss the changing uses and management of a traditional canal irrigation system against the background of processes of urbanization in Kathmandu Valley in Nepal. Until urbanization of Kathmandu Valley took off in the 1980s, the management of stream-fed canal irrigation systems had been a priority of both state agencies and the population that depended on agriculture-based livelihoods. The name rajkulo (royal canal) given to these systems expresses the historical interests of (royal) state actors in canal maintenance and management. Fed by a stream called Mahadev Khola in Dadhikot, a peri-urban village in Kathmandu Valley, Mahadev Khola Rajkulo is such a traditional canal irrigation system. Using an in-depth case study of this system, we analyse the interlinkages of demographic, socio-environmental, economic and local political dynamics with the changing canal uses and management. More specifically, we discuss how and why various actors became associated with, or dissociated from, canal use and management in recent times, and what these processes mean for water access, rights and security. We reflect on the implications of these changes for canal management and canal-related conflicts, against the background of national urban policies that formally aim to conserve agricultural land in Kathmandu Valley, but stimulate urban expansion in practice.
Public Lives, Private Water: Female Ready-Made Garment Factory Workers in Peri-Urban Bangladesh
Authors: Joshi Deepa , Haque Sadika , Nahar Kamrun , Tania Shahinur , Singh Jasber , Wallace Tina 
Published: December 2021

AbstractIn Dhaka city and its fringe peri-urban sprawls water for domestic use is an increasingly contested commodity. The location of our research, Gazipur district, bordering the growing city of Dhaka, is the heartland of Bangladesh’s Ready Made Garments (RMG) industry, which has spread unplanned in former wetlands and agrarian belts. However, unlike Dhaka, the almost fully industrialized peri-urban areas bordering the city, like many other such areas globally, function in an institutional vacuum. There are no formal institutional arrangements for water supply or sanitation. In the absence of regulations for mining groundwater for industrial use and weakly enforced norms for effluent discharge, the expansion of the RMG industry and other industries has had a disproportionate environmental impact. In this complex and challenging context, we apply a political economy lens to draw attention to the paradoxical situation of the increasingly “public” lives of poor Bangladeshi women working in large numbers in the RMG industry in situations of increasingly “private” and appropriated water sources in this institutionally liminal peri-urban space. Our findings show that poorly paid work for women in Bangladesh’s RMG industry does not translate to women’s empowerment because, among others, a persisting masculinity and the lack of reliable, appropriate and affordable WASH services make women’s domestic water work responsibilities obligatory and onerous.
Principles and State of the Art of Agent-Based Migration Modelling
Authors: Hinsch Martin , Bijak Jakub 
Published: December 2021

AbstractMigration as an individual behaviour as well as a macro-level phenomenon happens as part of hugely complex social systems. Understanding migration and its consequences therefore necessitates adopting a careful analytical approach using appropriate tools, such as agent-based models. Still, any model can only be specific to the question it attempts to answer. This chapter provides a general discussion of the key tenets related to modelling complex systems, followed by a review of the current state of the art in the simulation modelling of migration. The subsequent focus of the discussion on the key principles for modelling migration processes, and the context in which they occur, allows for identifying the main knowledge gaps in the existing approaches and for providing practical advice for modellers. In this chapter, we also introduce a model of migration route formation, which is subsequently used as a running example throughout this book.
Building a Knowledge Base for the Model
Authors: Nurse Sarah , Bijak Jakub 
Published: December 2021

AbstractIn this chapter, after summarising the key conceptual challenges related to the measurement of asylum migration, we briefly outline the history of recent migration flows from Syria to Europe. This case study is intended to guide the development of a model of migration route formation, used throughout this book as an illustration of the proposed model-based research process. Subsequently, for the case study, we offer an overview of the available data types, making a distinction between the sources related to the migration processes, as well as to the context within which migration occurs. We then propose a framework for assessing different aspects of data, based on a review of similar approaches suggested in the literature, and this framework is subsequently applied to a selection of available data sources. The chapter concludes with specific recommendations for using the different forms of data in formal modelling, including in the uncertainty assessment.
Introduction to Bayesian Statistical Inference
Authors: Karagiannis Georgios P. 
Published: December 2021

AbstractWe present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian paradigm. We present the conjugate priors; a computational convenient way to quantify prior information for tractable Bayesian statistical analysis. We present tools for parametric and predictive inference, and particularly the design of point estimators, credible sets, and hypothesis tests. These concepts are presented in running examples. Supplementary material is available from GitHub.
Introduction to the Theory of Imprecise Probability
Authors: Quaeghebeur Erik 
Published: December 2021

AbstractThe theory of imprecise probability is a generalization of classical ‘precise’ probability theory that allows modeling imprecision and indecision. This is a practical advantage in situations where a unique precise uncertainty model cannot be justified. This arises, for example, when there is a relatively small amount of data available to learn the uncertainty model or when the model’s structure cannot be defined uniquely. The tools the theory provides make it possible to draw conclusions and make decisions that correctly reflect the limited information or knowledge available for the uncertainty modeling task. This extra expressivity however often implies a higher computational burden. The goal of this chapter is to primarily give you the necessary knowledge to be able to read literature that makes use of the theory of imprecise probability. A secondary goal is to provide the insight needed to use imprecise probabilities in your own research. To achieve the goals, we present the essential concepts and techniques from the theory, as well as give a less in-depth overview of the various specific uncertainty models used. Throughout, examples are used to make things concrete. We build on the assumed basic knowledge of classical probability theory.
Simulation Methods for the Analysis of Complex Systems
Authors: George-Williams Hindolo , Santhosh T. V. , Patelli Edoardo 
Published: December 2021

AbstractEveryday systems like communication, transportation, energy and industrial systems are an indispensable part of our daily lives. Several methods have been developed for their reliability assessment—while analytical methods are computationally more efficient and often yield exact solutions, they are unable to account for the structural and functional complexities of these systems. These complexities often require the analyst to make unrealistic assumptions, sometimes at the expense of accuracy. Simulation-based methods, on the other hand, can account for these realistic operational attributes but are computationally intensive and usually system-specific. This chapter introduces two novel simulation methods: load flow simulation and survival signature simulation which together address the limitations of the existing analytical and simulation methods for the reliability analysis of large systems.
Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment
Authors: Bi Sifeng , Beer Michael 
Published: December 2021

AbstractThis chapter presents the technique route of model updating in the presence of imprecise probabilities. The emphasis is put on the inevitable uncertainties, in both numerical simulations and experimental measurements, leading the updating methodology to be significantly extended from deterministic sense to stochastic sense. This extension requires that the model parameters are not regarded as unknown-but-fixed values, but random variables with uncertain distributions, i.e. the imprecise probabilities. The final objective of stochastic model updating is no longer a single model prediction with maximal fidelity to a single experiment, but rather the calibrated distribution coefficients allowing the model predictions to fit with the experimental measurements in a probabilistic point of view. The involvement of uncertainty within a Bayesian updating framework is achieved by developing a novel uncertainty quantification metric, i.e. the Bhattacharyya distance, instead of the typical Euclidian distance. The overall approach is demonstrated by solving the model updating sub-problem of the NASA uncertainty quantification challenge. The demonstration provides a clear comparison between performances of the Euclidian distance and the Bhattacharyya distance, and thus promotes a better understanding of the principle of stochastic model updating, as no longer to determine the unknown-but-fixed parameters, but rather to reduce the uncertainty bounds of the model prediction and meanwhile to guarantee the existing experimental data to be still enveloped within the updated uncertainty space.