Conference Special Issue Research Articles

New approaches to improving the student experience at Aberystwyth University libraries: from library surveys to cognitive mapping

PurposeThe findings are based on experience at Aberystwyth Universities libraries using surveys and user experience (UX) activities to identify and understand user requirements.Design/methodology/approachThis paper was initially conceived and developed as a presentation for the LibPMC Conference in July 2023. It is included in this special conference issue for this conference.FindingsThere are challenges to successful integration of UX into user engagement processes across the library and IT services. To improve shared analysis and collaborative ideas generation we developed an interactive data dashboard.Originality/valueThe research is based on practical approaches to improving our understanding of student use and requirements at Aberystwyth University libraries. It explores mechanisms used to collect findings from all areas of UX, including library surveys, focus groups and UX activities to be able to analyse and share all student experience data – to be able to analyse themes and generate ideas and actions for development.

AANZCA2023 conference special issue: introduction

The Māori whakataukī, ‘ka mura, ka muri’ loosely translates as ‘walk backward into the future’ (This whakataukī is derived from a longer version: ‘Kia whakatōmuri te haere whakamua’ which translates to ‘I walk backwards into the future with my eyes fixed on my past’). It foregrounds a Māori perspective on time where the past is in front of us and can be observed and interpreted as we walk backwards into an unseen and uncertain future. This whakataukī was at the core of the 2023 AANZCA conference, ‘Ka mura, ka muri: Bridging communication pasts and futures’, held at Te Herenga Waka – Victoria University of Wellington. In the wake of catastrophes such as the COVID-19 pandemic, the failed Voice referendum, conflict in Ukraine and Palestine, and worsening ecological conditions, we look backward in order to look ahead, despite the uncertainty that lies there.

On Interdisciplinary Embraces in Game Studies

That game studies is an interdisciplinary venture is often stated and yet contested. Indeed, the Nordic DiGRA 2023 Conference was held under the theme: Interdisciplinary Embraces. In this editorial to the conference special issues we delve into the history of game studies as interdisciplinary. Through interviews with some key researchers from the Nordic Region (Annika Waern, Espen Aarseth, Frans Mäyrä, Jesper Juul), we explore some meanings and implications of the interdisciplinarity nature of game studies. We suggest that the success of game studies as a “project” lies in its ability to bring together disparate traditions in interdisciplinary efforts while simultaneously building up a core. Game studies today both is and is not interdisciplinary through a disciplinary gravitational core and an interdisciplinary cloud surrounding it.

Call for Papers: Upcoming Special Conference Issues*

Call for Papers: Upcoming Special Conference Issues*

Introduction for the 12th Aegean Analytical Chemistry Days (AACD 2023) Conference Special Issue in Analytical Letters

Introduction for the 12th Aegean Analytical Chemistry Days (AACD 2023) Conference Special Issue in Analytical Letters

Upcoming Special Conference Issues*

Upcoming Special Conference Issues*

From Practice to Public Health: Broadening Neuropsychology's Reach & Value-An Introduction to the National Academy of Neuropsychology's 2022 Annual Conference Special Issue.

This special issue is centered around presentations from the National Academy of Neuropsychology 2022 Annual Conference. The theme of the conference, "From Practice to Public Health: Broadening Neuropsychology's Reach & Value" is pivotal for the field's future. With an ever-shifting technological landscape and recent changes in clinical practice post-COVID, we are left wondering how neuropsychology will develop. How will we use biomedical and technological advances, such as blood-based Alzheimer's disease biomarkers or passive digital recordings, to improve clinical care and further expand our understanding of disease mechanisms? As neuropsychologists, how can we use our expertise to empirically inform public health policy? The diagnosis and treatment of post-acute sequelae of COVID-19, the identification and characterization of post-pandemic educational setbacks, and the adaptation of new technological and diagnostic advances into clinical practice workflows represent a vital set of new challenges and opportunities poised to disrupt traditional modes of practice. The articles in this special issue convey the role of neuropsychology in addressing these emerging issues and illustrate how and why neuropsychology is well positioned to be at the forefront of clinical practice and scientific advancements.

Introduction to the INEM 2021 conference special issue

Introduction to the INEM 2021 conference special issue

Introduction to the special issue on relational autonomy and collective intentionality

AbstractThis essay introduces the 40th Annual Spindel Conference special issue on relational autonomy and collective intentionality. Autonomy is often discussed in contexts of individual intention formation and moral decision making. When we consider collective intention formation and the decision‐making practices of institutions, a number of questions can be raised: How does the individual autonomy of participants affect the collective process? How do such collective processes modulate the autonomy of the individuals involved? How do institutional decisions and practices affect individual autonomy more generally—in what way do they enhance or undermine autonomy? Is there a significant difference in how we answer these questions if we already think of autonomy as relational rather than in a traditional individualistic way?

Upcoming Special Conference Issues*

Upcoming Special Conference Issues*

Introduction to MIMA-2 conference special issue

Introduction to MIMA-2 conference special issue

Synergy of Economic Policies and Innovation in DrivingMomentum for Sustainable Global Economic Recoveryin the Era of Digital TransformationThe 16 th BMEB International Conference Special Issue

Synergy of Economic Policies and Innovation in DrivingMomentum for Sustainable Global Economic Recoveryin the Era of Digital TransformationThe 16 th BMEB International Conference Special Issue

Guest editorial: The 11th EASN international conference special issue: “emerging technologies & innovative concepts in aviation and space”

Guest editorial: The 11th EASN international conference special issue: “emerging technologies & innovative concepts in aviation and space”

Call for Papers: Upcoming Special Conference Issues

Call for Papers: Upcoming Special Conference Issues

Editorial

In September 1985, the first issue of IEEE Electrical Insulation Magazine (EIM) was published. The founding editor, William H. Bentley Jr., known as Bill to his friends and colleagues, presented this special conference issue making reference to Yin and Yang, graphically displayed on the issue's front cover page.

An Efficient Bi-Objective Optimization Workflow Using the Distributed Quasi-Newton Method and Its Application to Well-Location Optimization

SummaryAlthough it is possible to apply traditional optimization algorithms to determine the Pareto front of a multiobjective optimization problem, the computational cost is extremely high when the objective function evaluation requires solving a complex reservoir simulation problem and optimization cannot benefit from adjoint-based gradients. This paper proposes a novel workflow to solve bi-objective optimization problems using the distributed quasi-Newton (DQN) method, which is a well-parallelized and derivative-free optimization (DFO) method. Numerical tests confirm that the DQN method performs efficiently and robustly.The efficiency of the DQN optimizer stems from a distributed computing mechanism that effectively shares the available information discovered in prior iterations. Rather than performing multiple quasi-Newton optimization tasks in isolation, simulation results are shared among distinct DQN optimization tasks or threads. In this paper, the DQN method is applied to the optimization of a weighted average of two objectives, using different weighting factors for different optimization threads. In each iteration, the DQN optimizer generates an ensemble of search points (or simulation cases) in parallel, and a set of nondominated points is updated accordingly. Different DQN optimization threads, which use the same set of simulation results but different weighting factors in their objective functions, converge to different optima of the weighted average objective function. The nondominated points found in the last iteration form a set of Pareto-optimal solutions. Robustness as well as efficiency of the DQN optimizer originates from reliance on a large, shared set of intermediate search points. On the one hand, this set of searching points is (much) smaller than the combined sets needed if all optimizations with different weighting factors would be executed separately; on the other hand, the size of this set produces a high fault tolerance, which means even if some simulations fail at a given iteration, the DQN method’s distributed-parallel information-sharing protocol is designed and implemented such that the optimization process can still proceed to the next iteration.The proposed DQN optimization method is first validated on synthetic examples with analytical objective functions. Then, it is tested on well-location optimization (WLO) problems by maximizing the oil production and minimizing the water production. Furthermore, the proposed method is benchmarked against a bi-objective implementation of the mesh adaptive direct search (MADS) method, and the numerical results reinforce the auspicious computational attributes of DQN observed for the test problems.To the best of our knowledge, this is the first time that a well-parallelized and derivative-free DQN optimization method has been developed and tested on bi-objective optimization problems. The methodology proposed can help improve efficiency and robustness in solving complicated bi-objective optimization problems by taking advantage of model-based search algorithms with an effective information-sharing mechanism.NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

Aspects of Multiscale Flow Simulation with Potential to Enhance Reservoir Engineering Practice

Summary A multiscale sequential fully implicit (MS SFI) reservoir simulation method implemented in a commercial simulator is applied to a set of reservoir engineering problems to understand its potential. Our assessment highlights workflows where the approach brings substantial performance advantages and insight generation. The understanding gained during commercialization on approximately 40 real-world models is illustrated through simpler but representative data sets, available in the public domain. The main characteristics of the method and key features of the implementation are briefly discussed. The robust fully implicit (FI) simulation method is used as a benchmark. The implementation of the MS SFI method is found to faithfully reproduce FI results for black-oil problems. We provide evidence and analysis of why the MS SFI approach can achieve high levels of performance and fidelity. The method supports the solution of unique problems that would benefit from incorporating multiscale geology and multiscale flow physics. The MS SFI implementation was used to successfully simulate a typical sector model used for field pilots at extremely high “whole core” scale resolution within a practical time frame leveraging high-performance computing (HPC). This could not be achieved with the FI approach. A combination of MS SFI and HPC offers immense potential to simulate geological models using grids to capture mesoscopic or laminar scale geology. The method, by design, demands fewer computing resources than FI, making it far more cost-effective to use for such high-resolution models. We conclude that the MS SFI method has a distinct capability to enhance reservoir engineering practice in the areas of high-resolution simulation-driven workflows in context of subsurface uncertainty quantification, field development planning, and reservoir performance optimization. NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

Scalable Graphics Processing Unit–Based Multiscale Linear Solvers for Reservoir Simulation

Summary In this work, the scalability of two key multiscale solvers for the pressure equation arising from incompressible flow in heterogeneous porous media, namely, the multiscale finite volume (MSFV) solver, and the restriction-smoothed basis multiscale (MsRSB) solver, are investigated on the graphics processing unit (GPU) massively parallel architecture. The robustness and scalability of both solvers are compared against their corresponding carefully optimized implementation on the shared-memory multicore architecture in a structured problem setting. Although several components in MSFV and MsRSB algorithms are directly parallelizable, their scalability on the GPU architecture depends heavily on the underlying algorithmic details and data-structure design of every step, where one needs to ensure favorable control and data flow on the GPU, while extracting enough parallel work for a massively parallel environment. In addition, the type of algorithm chosen for each step greatly influences the overall robustness of the solver. Thus, we extend the work on the parallel multiscale methods of Manea et al. (2016) to map the MSFV and MsRSB special kernels to the massively parallel GPU architecture. The scalability of our optimized parallel MSFV and MsRSB GPU implementations are demonstrated using highly heterogeneous structured 3D problems derived from the SPE10 Benchmark (Christie and Blunt 2001). Those problems range in size from millions to tens of millions of cells. For both solvers, the multicore implementations are benchmarked on a shared-memory multicore architecture consisting of two packages of Intel® Cascade Lake Xeon Gold 6246 central processing unit (CPU), whereas the GPU implementations are benchmarked on a massively parallel architecture consisting of NVIDIA Volta V100 GPUs. We compare the multicore implementations to the GPU implementations for both the setup and solution stages. Finally, we compare the parallel MsRSB scalability to the scalability of MSFV on the multicore (Manea et al. 2016) and GPU architectures. To the best of our knowledge, this is the first parallel implementation and demonstration of these versatile multiscale solvers on the GPU architecture. NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

Efficient Drilling Sequence Optimization Using Heuristic Priority Functions

SummaryDrilling sequence optimization is a common challenge faced in the oil and gas industry, and yet it cannot be solved efficiently by existing optimization methods due to its unique features and constraints. For many fields, the drilling queue is currently designed manually based on engineering heuristics. In this paper, we combined the heuristic priority functions (HPFs) with traditional optimizers to boost the optimization efficiency at a lower computational cost to speed up the decision-making process.The HPFs are constructed to map the individual well properties such as well index and interwell distance to the well priority values. As the name indicates, wells with higher priority values will be drilled earlier in the queue. The HPFs are a comprehensive metric of interwell communication and displacement efficiency. For example, injectors with fast support to producers, or producers with a better chance to drain the unswept region, tend to have high scores. They contain components that weigh the different properties of a well. These components are then optimized during the optimization process to generate the beneficial drilling sequences. Embedded with reservoir engineering heuristics, the priority function (PF) helps the optimizer focus on exploring scenarios with promising outcomes.The proposed HPFs, combined with the genetic algorithm (GA), have been tested through drilling sequence optimization problems for the Brugge Field and Olympus Field. Optimizations that are directly performed on the drilling sequence are used as reference cases. Different continuous/categorical parameterization schemes and various forms of HPFs are also investigated. Our exploration reveals that the HPF including well type, constraints, well index, distance to existing wells, and adjacent oil in place (OIP) yields the best outcome. The proposed approach achieved a better optimization starting point (∼5 to 18% improvement due to more reasonable drilling sequence rather than random guess), a faster convergence rate (results stabilized at 12 vs. 30 iterations), and a lower computational cost [150 to 250 vs. 1,300 runs to achieve the same net present value (NPV)] over the reference methods. Similar performance improvement was also observed in another application to a North Sea–type reservoir. This demonstrated the general applicability of the proposed method.The use of HPFs improves the efficiency and reliability of drilling sequence optimization compared with the traditional methods that directly optimize the sequence. They can be easily embedded in either commercial or research simulators as an independent module. In addition, they are also an automatic process that fits well with iterative optimization algorithms.NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

Strategies to Enhance the Performance of Gaussian Mixture Model Fitting for Uncertainty Quantification

Summary When formulating history matching within the Bayesian framework, we may quantify the uncertainty of model parameters and production forecasts using conditional realizations sampled from the posterior probability density function (PDF). It is quite challenging to sample such a posterior PDF. Some methods [e.g., Markov chain Monte Carlo (MCMC)] are very expensive, whereas other methods are cheaper but may generate biased samples. In this paper, we propose an unconstrained Gaussian mixture model (GMM) fitting method to approximate the posterior PDF and investigate new strategies to further enhance its performance. To reduce the central processing unit (CPU) time of handling bound constraints, we reformulate the GMM fitting formulation such that an unconstrained optimization algorithm can be applied to find the optimal solution of unknown GMM parameters. To obtain a sufficiently accurate GMM approximation with the lowest number of Gaussian components, we generate random initial guesses, remove components with very small or very large mixture weights after each GMM fitting iteration, and prevent their reappearance using a dedicated filter. To prevent overfitting, we add a new Gaussian component only if the quality of the GMM approximation on a (large) set of blind-test data sufficiently improves. The unconstrained GMM fitting method with the new strategies proposed in this paper is validated using nonlinear toy problems and then applied to a synthetic history-matching example. It can construct a GMM approximation of the posterior PDF that is comparable to the MCMC method, and it is significantly more efficient than the constrained GMM fitting formulation (e.g., reducing the CPU time by a factor of 800 to 7,300 for problems we tested), which makes it quite attractive for large-scale history-matching problems. NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.