Geologic considerations in underground coal mining system design

Geologic characteristics of coal resources which may impact new extraction technologies are identified and described to aid system designers and planners in their task of designing advanced coal extraction systems for the central Appalachian region. These geologic conditions are then organized into a matrix identified as the baseline mine concept. A sample region, eastern Kentucy is analyzed using both the developed baseline mine concept and the traditional geologic investigative approach

Existence and regularity results for viscous Hamilton–Jacobi equations with Caputo time-fractional derivative

We study existence, uniqueness and regularity properties of classical solutions to viscous Hamilton–Jacobi equations with Caputo time-fractional derivative. Our study relies on a combination of a gradient bound for the time-fractional Hamilton–Jacobi equation obtained via nonlinear adjoint method and sharp estimates in Sobolev and Hölder spaces for the corresponding linear problem

Automated test-based learning and verification of performance models for microservices systems

Effective and automated verification techniques able to provide assurances of performance and scalability are highly demanded in the context of microservices systems. In this paper, we introduce a methodology that applies specification-driven load testing to learn the behavior of the target microservices system under multiple deployment configurations. Testing is driven by realistic workload conditions sampled in production. The sampling produces a formal description of the users' behavior through a Discrete Time Markov Chain. This model drives multiple load testing sessions that query the system under test and feed a Bayesian inference process which incrementally refines the initial model to obtain a complete specification from run-time evidence as a Continuous Time Markov Chain. The complete specification is then used to conduct automated verification by using probabilistic model checking and to compute a configuration score that evaluates alternative deployment options. This paper introduces the methodology, its theoretical foundation, and the toolchain we developed to automate it. Our empirical evaluation shows its applicability, benefits, and costs on a representative microservices system benchmark. We show that the methodology detects performance issues, traces them back to system-level requirements, and, thanks to the configuration score, provides engineers with insights on deployment options. The comparison between our approach and a selected state-of-the-art baseline shows that we are able to reduce the cost up to 73% in terms of number of tests. The verification stage requires negligible execution time and memory consumption. We observed that the verification of 360 system-level requirements took ~1 minute by consuming at most 34 KB. The computation of the score involved the verification of ~7k (automatically generated) properties verified in ~72 seconds using at most ~50 KB. (C)& nbsp;2022 The Author(s). Published by Elsevier Inc.& nbsp

Cognitive biases in implementing a performance management system: behavioral strategy for supporting managers’ decision-making processes

Purpose The purpose of this paper is twofold: to provide a clear picture on the cognitive biases affecting managers' decision-making process of implementing a performance management system (PMS), and to identify managerial practices, measures and the key challenges to manage the cognitive biases in the corporate strategy. Design/methodology/approach Semi-structured interviews, based on theoretical milestones of performance management and cognitive psychology, gathered from 104 experienced professionals' evaluations on the likelihood and impact of managers' cognitive biases in PMS implementation, potential solutions as well as drivers and connected criticalities. Findings Recurring cognitive biases, together with considerable impacts, emerged in the first, and most strategic, phases of the PMS implementation. The authors developed a roadmap to support corporate transition to integrate behavioral strategy into the PMS implementation aiming to achieve economically and efficiently sound performance. Research limitations/implications From the view of proper behavioral strategy affirmation in performance management literature, in a small way, the authors contribute to a desirable taxonomy of cognitive biases so differentiated decision-making scenarios may be built to compare results and draw new observations. Behavioral studies could transversally connect the cognitive biases of performance management to actors' sociodemographic features and personality types. Practitioners may check biases affecting their organizations by means of the questionnaire and, consequently, adopt the framework illustrated to reduce them. Originality/value Performance management literature has constantly investigated positive and negative behavioral factors related to the PMS. This study, instead, makes a theoretical and methodological contribution to the PMS implementation as a decision-making process. The authors propose a theoretical framework that integrates cognitive psychology insights and applies measures to reduce biases

Online Model-Based Testing under Uncertainty

Modern software systems are required to operate in a highly uncertain and changing environment. They have to control the satisfaction of their requirements at run-time, and possibly adapt and cope with situations that have not been completely addressed at design-time. Software engineering methods and techniques are, more than ever, forced to deal with change and uncertainty (lack of knowledge) explicitly. For tackling the challenge posed by uncertainty in delivering more reliable systems, this paper proposes a novel online Model-based Testing technique that complements classic test case generation based on pseudo-random sampling strategies with an uncertainty-aware sampling strategy. To deal with system uncertainty during testing, the proposed strategy builds on an Inverse Uncertainty Quantification approach that is related to the discrepancy between the measured data at run-time (while the system executes) and a Markov Decision Process model describing the behavior of the system under test. To this purpose, a conformance game approach is adopted in which tests feed a Bayesian inference calibrator that continuously learns from test data to tune the system model and the system itself. A comparative evaluation between the proposed uncertainty-aware sampling policy and classical pseudo-random sampling policies is also presented using the Tele Assistance System running example, showing the differences in achieved accuracy and efficiency

Stabilization of controlled diffusions via Zubov's method

We consider a controlled stochastic system which is exponentially stabilizable in probability near an attractor. Our aim is to characterize the set of points which can be driven by a suitable control to the attractor with either positive probability or with probability one. This will be done by associating to the stochastic system a suitable control problem and the corresponding Zubov equation. We then show that this approach can be used as a basis for numerical computations of these sets

Homogenization of weakly coupled systems of Hamilton--Jacobi equations with fast switching rates

We consider homogenization for weakly coupled systems of Hamilton--Jacobi equations with fast switching rates. The fast switching rate terms force the solutions converge to the same limit, which is a solution of the effective equation. We discover the appearance of the initial layers, which appear naturally when we consider the systems with different initial data and analyze them rigorously. In particular, we obtain matched asymptotic solutions of the systems and rate of convergence. We also investigate properties of the effective Hamiltonian of weakly coupled systems and show some examples which do not appear in the context of single equations.Comment: final version, to appear in Arch. Ration. Mech. Ana

Coupled radon, methane and nitrate sensors for large-scale assessment of groundwater discharge and non-point source pollution to coastal waters

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Environmental Radioactivity 101 (2010): 553-563, doi:10.1016/j.jenvrad.2009.12.004.We constructed a survey system of radon/methane/nitrate/salinity to find sites of submarine groundwater discharge (SGD) and groundwater nitrate input. We deployed the system in Waquoit Bay and Boston Harbor, MA where we derived SGD rates using a mass balance of radon with methane serving as a fine resolution qualitative indicator of groundwater. In Waquoit Bay we identified several locations of enhanced groundwater discharge, out of which two (Childs and Quashnet Rivers) were studied in more detail. The Childs River was characterized by high nitrate input via groundwater discharge, while the Quashnet River SGD was notable but not a significant source of nitrate. Our radon survey of Boston Harbor revealed several sites with significant SGD, out of these Inner Harbor and parts of Dorchester Bay and Quincy Bay had groundwater fluxes accompanied by significant water column nitrogen concentrations. The survey system has proven effective in revealing areas of SGD and non-point source pollution.R. Camilli acknowledges the National Ocean Partnership Program (NOPP) for supporting the development of the TETHYS mass spectrometer through research grant #OCE-0537173. H. Dulaiova, M. A. Charette and R. Camilli acknowledge funding support from the WHOI Coastal Institute and MIT Sea Grant College Program under NOAA grant number NA06OAR4170019, project number 5710002173. H. Dulaiova was funded by the WHOI Academic Program’s postdoctoral scholarship

Field emission from two-dimensional GeAs

GeAs is a layered material of the IV–V groups that is attracting growing attention for possible applications in electronic and optoelectronic devices. In this study, exfoliated multilayer GeAs nanoflakes are structurally characterized and used as the channel of back-gate field-effect transistors. It is shown that their gate-modulated p-type conduction is decreased by exposure to light or electron beam. Moreover, the observation of a field emission (FE) current demonstrates the suitability of GeAs nanoflakes as cold cathodes for electron emission and opens up new perspective applications of two-dimensional GeAs in vacuum electronics. FE occurs with a turn-on field of ~80 Vum-1 and attains a current density higher than 10 Acm-2, following the general Fowler–Nordheim model with high reproducibility

Synthesis of hydrophilic carbon nanotube sponge via post-growth thermal treatment

Clean water is vital for healthy ecosystems, for human life and, in a broader sense, it is directly linked to our socio-economic development. Nevertheless, climate change, pollution and increasing world population will likely make clean water scarcer in the near future. Consequently, it becomes imperative to develop novel materials and more efficient ways of treating waste and contaminated water. Carbon nanotube (CNT) sponges, for example, are excellent in removing oleophilic contaminants; however, due to their super-hydrophobic nature, they are not as efficient when it comes to absorbing water-soluble substances. Here, by means of a scalable method consisting of simply treating CNT sponges at mild temperatures in air, we attach oxygen-containing functional groups to the CNT surface. The functionalized sponge becomes hydrophilic while preserving its micro- and macro-structure and can therefore be used to successfully remove toxic contaminants, such as pesticides, that are dissolved in water. This discovery expands the current range of applications of CNT sponges to those fields in which a hydrophilic character of the sponge is more suitable