New Jersey Institute of Technology

With the development of multi-access edge computing (also called mobile edge computing, MEC), more and more service-based applications are deployed to edge servers in order to ensure desired quality of service (QoS), especially in the scenario of Internet of things (IoT). In edge environment, how to reasonably deploy application services emerges as a challenging problem due to the limited resources, heterogeneous servers and different geographical locations of users. Benefiting from its reusability, a single service can be used by multiple applications. Yet only a few studies about the deployment problem in edge environment consider such property of services. This work considers the redundant deployment of reused services by different applications, so as to achieve high QoS. Due to the importance of cost for providers, it aims to minimize transmission cost and network latency under the constraint of deployment budget. This work firstly builds a redundant service deployment model under heterogeneous edge environment, and defines it as a multi-objective optimization problem under a given budget constraint. Then, service priority is calculated to determine redundancy, and K-medoids clustering algorithm based on request frequency filtering is used to conduct edge server selection. It next proposes a genetic algorithm based on priority to obtain an optimized plan. Finally, this work conducts experiments on real-world datasets to prove the superiority of the proposed method over existing ones.

Petri nets (PNs) are a graphical and mathematical tool to model various event-driven automated systems. Reachability is a fundamental property of PNs. The existence of a non-negative integer solution (NIS) to a state equation is a necessary but not sufficient condition for determining the reachability of PNs, i.e., there may be no legal firing sequence (LFS) corresponding to an NIS of a state equation. Finding an LFS for an NIS of a state equation is an NP-hard problem. However, we determine the reachability of a marking by determining the existence of LFSs rather than finding them. We find that the existence of idling circuits (ICs) or idling-dependent circuits (IDCs) is the root cause that there is an NIS satisfying the state equation but the marking is non-reachable in ordinary PNs. Based on this, a Backward Algorithm is presented to determine the existence of an LFS for an NIS. It reversely finds paths from the destination marking to the initial one and determines whether there is always an intermediate marking with ICs or IDCs. When the state equation has a finite number of NISs, only the polynomial time is needed to analyze the reachability of ordinary PNs. Experimental results verify the effectiveness and efficiency of the approach. This work represents an important advance in the theory and applications of PNs to automated system design.

Intensive competition among supply chains often forces trading partners to collaborate despite their conflict of interests. Supply chain contracts and collaboration theory is well established in the academic literature to align the interests but much less conveyed to students and industry professionals for a practical impact. Although the Beer Game captures the bullwhip effect and the value of information sharing, it ignores the conflict of interests, that is, price and quantity bargaining, among the trading partners. We describe a new online teaching game, the FloraPark simulation (“the flower game” at https://flower.gamespots.net/ ), based on real-life events in the international fresh-cut flower supply chains, for students to learn supply chain collaboration via contracts in a setting of multiple supply chains competing in the same market. Students play trading partners in the flower supply chains and experiment with the push, pull, and advanced purchasing discount contracts by negotiating wholesale prices and quantities to achieve the conflicting objectives of (1) collaboration to beat other supply chains, and (2) bargaining to protect their own interests from their trading partners. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ited.2022.0035 .

In this study, we investigated the thermal decomposition mechanisms of perfluoroalkyl ether carboxylic acids (PFECAs) and short-chain perfluoroalkyl carboxylic acids (PFCAs) that have been manufactured as replacements for phased-out per- and polyfluoroalkyl substances (PFAS). C-C, C-F, C-O, O-H, and C═C bond dissociation energies were calculated at the M06-2X/Def2-TZVP level of theory. The α-C and carboxyl-C bond dissociation energy of PFECAs declines with increasing chain length and the attachment of an electron-withdrawing trifluoromethyl (-CF3) group to the α-C. Experimental and computational results show that the thermal transformation of hexafluoropropylene oxide dimer acid to trifluoroacetic acid (TFA) occurs due to the preferential cleavage of the C-O ether bond close to the carboxyl group. This pathway produces precursors of perfluoropropionic acid (PFPeA) and TFA and is supplemented by a minor pathway (CF3CF2CF2OCFCF3COOH → CF3CF2CF2· + ·OCFCF3COOH) through which perfluorobutanoic acid (PFBA) is formed. The weakest C-C bond in PFPeA and PFBA is the one connecting the α-C and the β-C. The results support (1) the C-C scission in the perfluorinated backbone as an effective PFCA thermal decomposition mechanism and (2) the thermal recombination of radicals through which intermediates are formed. Additionally, we detected a few novel thermal decomposition products of studied PFAS.

Cyclical variations of the solar magnetic fields, and hence the level of solar activity, are among the top interests of space weather research. Surface flows in global-scale, in particular differential rotation and meridional flows, play important roles in the solar dynamo that describes the origin and variation of solar magnetic fields. In principle, differential rotation is the fundamental cause of dipole field formation and emergence, and meridional flows are the surface component of a longitudinal circulation that brings decayed field from low latitudes to polar regions. Such flows are key inputs and constraints of observational and modeling studies of solar cycles. Here, we present two methods, local correlation tracking (LCT) and machine learning-based self-supervised optical flow methods, to measure differential rotation and meridional flows from full-disk magnetograms that probe the photosphere and Hα\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\text{H}\alpha$\end{document} images that probe the chromosphere, respectively. LCT is robust in deriving photospheric flows using magnetograms. However, we found that it failed to trace flows using time-sequence Hα\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\text{H}\alpha $\end{document} data because of the strong dynamics of traceable features. The optical flow methods handle Hα\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\text{H}\alpha $\end{document} data better to measure the chromospheric flow fields. We found that the differential rotation from photospheric and chromospheric measurements shows a strong correlation with a maximum of 2.85μrads−1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$2.85~\upmu \text{rad}\,\text{s}^{-1}$\end{document} at the equator and the accuracy holds until 60∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$60^{\circ }$\end{document} for the MDI and Hα\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\text{H}\alpha$\end{document}, 75∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$75^{\circ }$\end{document} for the HMI dataset. On the other hand, the meridional flow deduced from the chromospheric measurement shows a similar trend as the concurrent photospheric measurement within 60∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$60^{\circ }$\end{document} with a maximum of 20ms−1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$20~\text{m}\,\text{s}^{-1}$\end{document} at 40∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$40^{\circ }$\end{document} in latitude. Furthermore, the measurement uncertainties are discussed.

We present a novel method for simulating groups moving in formation. Recent approaches for simulating group motion operate via forces or velocity-connections. While such approaches are effective for several cases, they do not easily scale to large crowds, irregular formation shapes, and they provide limited fine-grain control over agent and group behaviors. In this paper we propose a novel approach that addresses these difficulties via positional constraints, with a position-based dynamics solver. Our approach allows real-time, interactive simulation of a variety of group numbers, formation shapes, and scenarios of up to thousands of agents.

Introduction: Inverted papilloma (IP) is a sinonasal tumor with a well-known potential for malignant transformation. The role of HPV in its pathogenesis has been controversial. The purpose of this study was to determine the virome associated with IP, with progression to carcinoma-in-situ, and invasive carcinoma. Methods: To determine the HPV-specific types, a metagenomics assay that contains 62,886 probes targeting viral genomes in a microarray format was used. The platform screens DNA and RNA from fixed tissues from 8 control tissue, 16 IP without dysplasia, 5 IP with carcinoma-in-situ, and 13 IP-associated squamous cell carcinoma (IPSCC). Paired with next-generation sequencing, 48 types of HPV with 857 region-specific probes were interrogated against the tumors. Results: The prevalence of HPV16 was 14%, 42%, 70% and 73% in control tissue, IP without dysplasia, IP with CIS, and IPSCC, respectively. The prevalence of HPV18 had a similar progressive increase in prevalence, with 14%, 27%, 67%, and 74%. The assay allowed region-specific analysis which identified the only oncogenic HPV type 18 E6 to be statistically significant when comparing to control tissue. The prevalence of HPV18-E6 was 0% in control tissue, 25% in IP without dysplasia, 60% in IP with CIS, and 77% in IPSCC. Conclusions: There are over 200 HPV types that infect human epithelial cells, of which only a few are known to be high-risk. Our study demonstrated a trend of increasing prevalence of HPV18-E6 that correlated with histologic severity, which is novel and supports a potential role for HPV in the pathogenesis of IP. This article is protected by copyright. All rights reserved.

Using a case study of British trade union organizer Margaret Bondfield, this article begins the task of adding a working class perspective to accounts of women’s participation in Progressive era urban reform and expansion of public policy while also showing how international networks of support among trade union women undergirded American reform efforts. The narrative expands the consideration of intersectionality and positionality in analyzing women’s role in developing public administration.

This paper presents a low-cost, accurate indoor positioning system that integrates image acquisition and processing and data-driven modeling algorithms for robotics research and education. Multiple overhead cameras are used to obtain normalized image coordinates of ArUco markers, and a new procedure is developed to convert them to the camera coordinate frame. Various data-driven models are proposed to establish a mapping relationship between the camera and the world coordinates. One hundred fifty data pairs in the camera and world coordinates are generated by measuring the ArUco marker at different locations and then used to train and test the data-driven models. With the model, the world coordinate values of the ArUco marker and its robot carrier can be determined in real time. Through comparison, it is found that a straightforward polynomial regression outperforms the other methods and achieves a positioning accuracy of about 1.5 cm. Experiments are also carried out to evaluate its feasibility for use in robot control. The developed system (both hardware and algorithms) is shared as an open source and is anticipated to contribute to robotic studies and education in resource-limited environments and underdeveloped regions.

We consider a sheared granular system experiencing intermittent dynamics of stick-slip type via discrete element simulations. The considered setup consists of a two-dimensional system of soft frictional particles sandwiched between solid walls, one of which is exposed to a shearing force. The slip events are detected using stochastic state space models applied to various measures describing the system. The amplitudes of the events spread over more than four decades and present two distinctive peaks, one for the microslips and the other for the slips. We show that the measures describing the forces between the particles provide earlier detection of an upcoming slip event than the measures based solely on the wall movement. By comparing the detection times obtained from the considered measures, we observe that a typical slip event starts with a local change in the force network. However, some local changes do not spread globally over the force network. For the changes that become global, we find that their size strongly influences the further behavior of the system. If the size of a global change is large enough, then it triggers a slip event; if it is not, then a much weaker microslip follows. Quantification of the changes in the force network is made possible by formulating clear and precise measures describing their static and dynamic properties.

We report on transport measurements in monolayer MoS 2 devices, close to the bottom of the conduction band edge. These devices were annealed in situ before electrical measurements. This allows us to obtain good ohmic contacts at low temperatures, and to measure precisely the conductivity and mobility via four-probe measurements. The measured effective mobility up to μ eff = 180 cm ² /Vs is among the largest obtained in CVD-grown MoS 2 monolayer devices. These measurements show that electronic transport is of the insulating type for σ≤ 1.4e ² /h and n ≤ 1.7×10 ¹² cm ⁻² , and a crossover to a metallic regime is observed above those values. In the insulating regime, thermally activated transport dominates at high temperature (T > 100 K). At lower temperatures, conductivity is driven by Efros-Schklovkii variable range hopping in all measured devices, with a universal and constant hopping prefactor, that is a clear indication that hopping is not phonon-mediated. At higher carrier density, and high temperature, the conductivity is well modeled by the Boltzmann equation for a non-interacting Fermi gas, taking into account both phonon and impurity scatterings. Finally, even if this apparent metal-insulator transition can be explained by phonon-related phenomena at high temperature, the possibility of a genuine 2D MIT cannot be ruled out, as we can observe a clear power-law diverging localization length close to the transition, and a one-parameter scaling can be realized.

We present a comprehensive analysis of the physical parameters and relationships of umbral dots (UDs), which assists in our understanding of the physical properties of the Sun. This study is based on a detailed analysis of UDs detected in 12 umbras belonging to 10 different sunspots using high-resolution data recorded by the Goode Solar Telescope at the Big Bear Solar Observatory. We obtained the physical parameters (brightness, diameter, eccentricity, lifetime, dynamic velocity) of each UD and calculated correlation coefficients using linear and nonlinear approaches to reveal the relationships between these parameters. We found that: i) the diameter of UDs are varying between 92.2 km to 246.5 km, the eccentricity varies between 0.02 to 0.65, the lifetime of UDs vary from 0.75 to 120.00 min and the dynamic velocities are varying from 0.01 km/s to 3.80 km/s. ii) The intensity-diameter and diameter-eccentricity show the highest degree of correlation, while the lowest linear correlation was obtained for the diameter-lifetime and the lowest nonlinear correlation was obtained for the eccentricity-lifetime relationships. iii) In general the nonlinear correlation coefficients are higher than the linear correlation without any exception. iv) The linear and nonlinear correlation coefficients are very close to each other in the case of diameter-eccentricity relation. v) While the average diameter, intensity, and eccentricity are related to the umbral area, the average lifetime and dynamic velocity of UDs are not dependent on the umbral area.

In biogeomechanics, which describes the mechanical responses to microbial-rock interactions and its succeeding alterations, there is complexity in the estimation and predictability of biological processes and biologically-altered properties of rocks at a greater scale which inhibits the upscaling of biogeomechanical properties and processes from laboratory-scale. However, the successful application of this emerging field of rock mechanics (biogeomechanics) relies on proper upscaling of treatment process of geomaterials with biological agents from a laboratory scale (core scale) to a larger scale (field scale) which could be achieved by adopting a machine learning technique. This work proposes a state-of-the-art machine learning (ML) approach to predict temporal biogeomechanical properties at a field scale. Four ML techniques of K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF), were adopted to develop our new ML approach for the prediction of biogeomechanical properties. Firstly, experimental tests were conducted to obtain time-lapse biogeomechanical properties [microbially altered Uniaxial Compressive Strength (UCS) and Poisson's ratio (ν\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\nu$$\end{document})] of shale and carbonate formations at core- and bulk-scales, and subsequently, these core-scale experimental data (428 datasets for shale and carbonate) were utilized to predict the field-scale biogeomechanical properties. Further, we compared and analyzed the ML-predicted biogeomechanical properties. There is a high degree of correlation between the bulk-scale biogeomechanical properties obtained from uniaxial compression tests and the ML-predicted field-scale biogeomechanical properties. The most accurate results for carbonate formation are produced by the RF model (UCS: R² = 0.9613; MAE = 6.15 MPa; MPE = 2.62%; VAF = 96.16%; a20-index = 0.9091), whereas for shale formation is the KNN model (UCS: R² = 0.8576; MAE = 5.41 MPa; MPE = 0.65%; VAF = 85.82%; a20-index = 0.9841). This study provides a novel potential for predicting the changes in rock mechanical properties due to biologically-induced processes at multi-scales (micro-, meso-, and mega-scale). Further, this study provides the first insight and a robust predictive tool for evaluating biogeomechanical properties at field scales where there is limited or non-existent data to constrain geomechanical models and the design of target formation.

Background The extensive international research for medications and vaccines for the devastating COVID-19 pandemic requires a standard reference ontology. Among the current COVID-19 ontologies, the Coronavirus Infectious Disease Ontology (CIDO) is the largest one. Furthermore, it keeps growing very frequently. Researchers using CIDO as a reference ontology, need a quick update about the content added in a recent release to know how relevant the new concepts are to their research needs. Although CIDO is only a medium size ontology, it is still a large knowledge base posing a challenge for a user interested in obtaining the “big picture” of content changes between releases. Both a theoretical framework and a proper visualization are required to provide such a “big picture”. Methods The child-of-based layout of the weighted aggregate partial-area taxonomy summarization network (WAT) provides a “big picture” convenient visualization of the content of an ontology. In this paper we address the “big picture” of content changes between two releases of an ontology. We introduce a new DIFF framework named Diff Weighted Aggregate Taxonomy (DWAT) to display the differences between the WATs of two releases of an ontology. We use a layered approach which consists first of a DWAT of major subjects in CIDO, and then drill down a major subject of interest in the top-level DWAT to obtain a DWAT of secondary subjects and even further refined layers. Results A visualization of the Diff Weighted Aggregate Taxonomy is demonstrated on the CIDO ontology. The evolution of CIDO between 2020 and 2022 is demonstrated in two perspectives. Drilling down for a DWAT of secondary subject networks is also demonstrated. We illustrate how the DWAT of CIDO provides insight into its evolution. Conclusions The new Diff Weighted Aggregate Taxonomy enables a layered approach to view the “big picture” of the changes in the content between two releases of an ontology.