Exploration Of Scenarios Research Articles

Region-of-Interest Prioritised Sampling for Constrained Autonomous Exploration Systems

Goal oriented autonomous operation of space rovers has been known to increase scientific output of a mission. In this work we present an algorithm, called the RoI Prioritised Sampling (RPS), that prioritises Region-of-Interests (RoIs) in an exploration scenario in order to utilise the limited resources of the imaging instrument on the rover effectively. This prioritisation is based on an estimator that evaluates the change in information content at consecutive spatial scales of the RoIs without calculating the finer scale reconstruction. The estimator, called the Refinement Indicator (RI), is motivated and derived. Multi-scale acquisition approaches, based on classical and multilevel compressed sensing, with respect to the single pixel camera architecture are discussed. The performance of the algorithm is verified on remote sensing images and compared with the state-of-the-art multi-resolution reconstruction algorithms. At the considered sub-sampling rates the RPS is shown to better utilise the system resources for reconstructing the RoIs.

The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario.

Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. Our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.

Unsupervised symbol emergence for supervised autonomy using multi-modal latent Dirichlet allocations

In future Mars exploration scenarios, astronauts orbiting the planet will control robots on the surface with supervised autonomy to construct infrastructure necessary for human habitation. Symbol-based planning enables intuitive supervised teleoperation by presenting relevant action possibilities to the astronaut. While our initial analog experiments aboard the International Space Station (ISS) proved this scenario to be very effective, the complexity of the problem puts high demands on domain models. However, the symbols used in symbolic planning are error-prone as they are often hand-crafted and lack a mapping to actual sensor information. While this may lead to biased action definitions, the lack of feedback is even more critical. To overcome these issues, this paper explores the possibility of learning the mapping between multi-modal sensor information and high-level preconditions and effects of robot actions. To achieve this, we propose to utilize a Multi-modal Latent Dirichlet Allocation (MLDA) for unsupervised symbol emergence. The learned representation is used to identify domain-specific design flaws and assist in supervised autonomy robot operation by predicting action feasibility and assessing the execution outcome. The approach is evaluated in a realistic telerobotics experiment conducted with the humanoid robot Rollin' Justin.

Scenarios for European agricultural policymaking in the era of digitalisation

CONTEXTDigitalisation affects the agri-food sector and its governance. However, what digitalisation of the sector will imply for future agricultural policymaking remains unclear. OBJECTIVEThe objective of the study is to develop and evaluate explorative scenarios of digitalisation in the agri-food sector of Europe that are explicitly relevant to agricultural policy. The study aims to provide guidance for strategic development of agricultural policy to address the potentials, uncertainties and unknowns arising with digitalisation of the sector. METHODSWe combine a Delphi study and a participatory scenario workshop to develop and evaluate plausible explorative scenarios of digitalisation of Europe's agri-food sector. For all scenarios we identify gaps in achieving a range of important European agricultural policy goals, drawing on the Delphi study and desk-based analysis. Subsequently we deduce strategies to address these agricultural policy gaps. RESULTS AND CONCLUSIONSFour scenarios of digitalisation of the agri-food sector were developed for Europe in 2030. They comprise of 1) digitalisation of the sector following current directions at current rates as a baseline scenario, 2) strong digitalisation of a regulatory government, 3) use of autonomous farming technology and 4) digitalised food business. These explorative scenarios entail various gaps in achieving European agricultural policy goals. Our findings suggest that the baseline scenario needs strategies to ramp up technological and institutional infrastructure for digitalisation. The other scenarios need strategies to prevent risks, e.g., of technological failures or undesired social impacts. They also need strategies to cater for special cases and diversity, e.g., of ecosystems and farming practices. Across the scenarios, it seems useful to increase digital competencies of the stakeholders. SIGNIFICANCEThe study is the first that derives implications for policy strategies from explorative scenarios of future digitalisation of agricultural systems that target gaps in achieving agricultural policy goals. The combination of developing and analysing scenarios generated findings that are of significance to policymaking stakeholders and researchers alike, who all need to address the uncertainties arising with future digitalisation of the agri-food sector.

Bottom-up innovations in natural hazard risk management in Austria

Current procedures in risk management request new institutional innovations to effectively respond to observed dynamics. This paper aims to assess and explain institutional innovations in natural hazard risk management by analyzing examples of multi-functional protection schemes, which are distinctive to mainstream solutions. These multi-functional protection schemes combine several different functions (e.g. risk management strategy, sustainable land-use development, individual preparedness and well-being) that are concerned with mitigation systems in order to save space, time and possible arising costs. The paper uses a mixed method approach. The first step includes 14 qualitative standardized semi-structured interviews with key stakeholders connected to proposed transformative strategies in selected four study sites. As a second step, we conducted an explorative scenario analysis with key experts based on potential upscaling of bottom-up innovations to the mainstream natural hazard risk management in Austria. The results highlight that institutional innovations in risk management are dependent on bottom-up initiatives instead of top-down decisions. Consequently, these measures also need a larger risk acceptance among decision makers and the public. Innovations are based on individual interests and certain policy entrepreneurs at local level; however, national authorities act as ‘gatekeepers’ which allows innovation at local level. Based on these variables, three selected policy pathways were developed to show the tipping points necessary for policy transformation in Austria.

Modeling Sustainable Risks Mitigation Strategies Using a Morphological Analysis-Based Approach: A Real Case Study

The supply chain risk management (SCRM) is very critical to strategically support the firms to continuous success. There are, at least, three basic steps in this SCRM process: risk identification, risk evaluation, and risk mitigation (treatment). Whatever happens, the main step is risk mitigation (RM) and mainly sustainable RM. In fact, every risk must be eliminated or controlled as much as possible. The purpose of this paper is to elaborate and evaluate various RM scenarios from an initial risk identification and prioritization solution. The proposed scenario modeling technique is based on morphological analysis (MA) as an explorative scenario tool for RM. MA is used to develop a framework to proactively assess critical risk variables. Firstly, MA is employed to exhaustively create possible RM scenarios and, secondly, to assess the likelihood of each scenario. The proposed approach addresses the need for a basic rubric to help identify and choose RM approaches. A real case study is provided from the food industry to illustrate the application of the proposed approach. To handle all possible MA strategies, a dedicated MORPHOL software package is used. In addition, RM strategies are selected based on sustainability indicators. The case study results prove that MA has a considerable value for SCRM. It shows that firms can adopt multiple robust strategies in the form of a scenario describing all stages of SCRM in an integrated representation.

Evaluation of emissions in traffic reduction and pedestrianization scenarios in Madrid

• We evaluate reduction in CO, NO x and PM emissions in the ‘Madrid Central’ • VISUM, COPERT Street level and R-Line models have been applied. • We consider exploratory traffic reduction and pedestrianization measures. • The decrease in pedestrian exposure to pollutants is assessed. • Combination of pedestrianizations and traffic reductions lead to good results. Cities have implemented a range of strategies to reduce traffic volumes and hence pollution; including low emission zones (LEZ) and pedestrianization. This paper aims to evaluate the degree of reduction in CO, NO × and PM emissions in Madrid’s ‘Centro’ LEZ after the adoption of traffic reduction and pedestrianization actions. We propose a current scenario of traffic volume and pedestrianized streets, and another set of exploratory scenarios. The methodology uses a traffic simulation model, followed by the estimation of the emissions at street level and their dispersion. These results are crossed with pedestrian routes to estimate the reduction in pedestrian exposure to these pollutants. The results show that improvements are generalized in scenarios where traffic is reduced. The results serve to analyse the rate of the decrease in pedestrian exposure to these pollutants and can provide evidence-based geographically-dimensioned information to identify priority actions.

Design and Implementation of Triple Band Half Mode Substrate Integrated Waveguide (HMSIW) Antenna with Compact Size

This study investigated structure strategies and exploratory scenarios for a half mode substrate integrated waveguide (HMSIW) antenna. The proposed antenna consists of three Hilbert cells, which are simulated by using CST programming. The antenna was manufactured with the realities of minor imperfections and high incorporation. The proposed structure offers a suitable substrate integrated waveguide (SIW) with about a decrease in size by half. In addition, Hilbert cells were added to realize the triple-band characteristics with good impedance matching, radiation patterns, and radiation performance. The antenna was fabricated on h = 1 mm thick dielectric substrate with dielectric constant (𝜀𝑟 = 4.3). The Hilbert cells were drilled on the top plane of the antenna substrate and fed using a microstrip transmission line. The proposed antenna is small, with a slot side length of approximately half of the guided wavelength. The three developed Hilbert cell HMSIW antenna resonates at 3.25, 5.94 and 6.5 GHz with a bandwidth of 2.97, 2.25 and 2.29% within a return loss of ‑38.77, ‑35.82 -23.35 dB, respectively. The results showed enhancements in antenna gain of 3.56, 4.97 and 6.43 dBi, with a radiation efficiency of -1.253, -0.493 and -0.586 dB, respectively.

Data to Knowledge-Based Transformation

The objectives of this study is to analyze a large amount of data that often appears to create a knowledge base that can be utilized by firm to enhance their decision support system. The authors used the association rules with rapid miner software, data mining approach, and predictive analysis that contains various data exploration scenarios. The study provides important evidence for adopting data mining methods in the industrial sector and their advantages and disadvantages. Chevron Pacific Indonesia (CPI) has a type of computer maintenance activity. Currently, a numerous errors often occur due to the accuracy in computer maintenance which has a major impact on production results. Therefore, this study focuses on association rules using growth patterns that often appear on variables that have been determined into the algorithm (FP-growth) which results in knowledge with a 100% confidence value and a 97% support value. The value results of this study has support and trust are expected to become knowledge for top management in deciding evergreen IT-business routines.

Investigation of Mission-Driven Inverse Aircraft Design Space Exploration with Machine Learning

The goal of this work was to investigate the feasibility of developing machine learning models for predicting the values of aircraft configuration design variables when provided with time series of mission-informed performance parameters. Regression artificial neural networks, along with their associated training data, have been generated and tested for aircraft design space exploration scenarios. The bounds of the data used to train the models were partially informed by the configuration characteristics of the Boeing 737 Next Generation family. The effects of varying neural network architecture, along with the application of different data filtering schemes, on the models’ predictive accuracy have been examined. The results obtained demonstrated that cascade-forward shallow neural networks not only exhibited excellent generalization across the design space for which the model was calibrated for, but also showcased versatility when tasked with predicting design variable values for a configuration layout relatively different from the ones used for training. Furthermore, these models had favorable metrics in computational wall-clock time required and number of epochs needed for training.

EM3DANI: A Julia package for fully anisotropic 3D forward modeling of electromagnetic data

Forward modeling is vital for 3D inversion and interpretation of electromagnetic (EM) data in anisotropic media, which is one of the major challenges in the field of EM geophysics. However, there are few freely available 3D codes that are capable of modeling EM responses in fully anisotropic media. In addition, most existing 3D EM codes are written in low-level languages (LLs) such as C and Fortran, making them difficult to read, maintain, and extend. Taking advantage of recent progress in computer technology and numerical methods, we have developed an open-source package for forward modeling of frequency-domain EM fields in a fully 3D anisotropic earth (EM3DANI) using the Julia language, a relatively young, high-level programming language with a focus on high performance. Based on a mimetic finite-volume discretization of the governing equations, the modeling algorithm is expressed in an abstract form in terms of matrices/vectors and thus can be easily implemented by using any high-level language commonly used for numerical computing. Existing libraries written in LLs can be easily integrated into a Julia code without the so-called two-language problem; thus, we have exploited several mature third-party packages to deal with computationally intensive parts of the forward modeling, which guarantees high stability and efficiency. We have elaborated the structure of the package, paying special attention to code usability, readability, and extendability, while striving to retain versatility and high performance. The effectiveness of the code is demonstrated through two 1D synthetic examples for magnetotelluric and controlled-source EM (CSEM) problems, respectively. High accuracy and efficiency can be achieved for both 1D examples. Furthermore, we have developed a 3D example mimicking a marine CSEM survey scenario for hydrocarbon exploration. The simulation results indicate that the effect of the anisotropy on forward responses is significant and can be comparable to that of the target reservoir.

Probabilistic Temporal Networks with Ordinary Distributions: Theory, Robustness and Expected Utility

Most existing works in Probabilistic Simple Temporal Networks (PSTNs) base their frameworks on well-defined, parametric probability distributions. Under the operational contexts of both strong and dynamic control, this paper addresses robustness measure of PSTNs, i.e. the execution success probability, where the probability distributions of the contingent durations are ordinary, not necessarily parametric, nor symmetric (e.g. histograms, PERT), as long as these can be discretized. In practice, one would obtain ordinary distributions by considering empirical observations (compiled as histograms), or even hand-drawn by field experts. In this new realm of PSTNs, we study and formally define concepts such as degree of weak/strong/dynamic controllability, robustness under a predefined dispatching protocol, and introduce the concept of PSTN expected execution utility. We also discuss the limitation of existing controllability levels, and propose new levels within dynamic controllability, to better characterize dynamic controllable PSTNs based on based practical complexity considerations. We propose a novel fixed-parameter pseudo-polynomial time computation method to obtain both the success probability and expected utility measures. We apply our computation method to various PSTN datasets, including realistic planetary exploration scenarios in the context of the Mars 2020 rover. Moreover, we propose additional original applications of the method.

The rhythms of rhythm

The low frequency (LF) spectral analysis or ‘rhythm spectrum’ approach to the quantitative analysis and comparison of speech rhythms is extended beyond syllable or word rhythms to ‘rhetorical rhythms’ in read-aloud narratives, in a selection of exploratory scenarios, with the aim of developing a unified theory of speech rhythms. Current methodologies in the field are first discussed, then the choice of data is motivated and themodulation-theoreticrhythm spectrum and rhythm spectrogram approach is applied to the amplitude modulation (AM) and frequency modulation (FM) of speech. New concepts ofrhythm formant,rhythm spectrogramandrhythm formant trajectoryare introduced in theRhythm Formant Theory(RFT) framework with its associated methodologyRhythm Formant Analysis(RFA) in order to capture second order regularities in the temporal variation of rhythms. The interaction of AM and FM rhythm factors is explored, contrasting English with Mandarin Chinese. The LF rhythm spectrogram is introduced in order to recover temporal information about long-term rhythms, and to investigate the configurative function of rhythm. The trajectory of highest magnitude frequencies through the component spectra of the LF spectrogram is extracted and applied in classifying readings in different languages and individual speaking styles using distance-based hierarchical clustering, and the existence of long-term second order ‘rhythms of rhythm’ in long narratives is shown. In the conclusion, pointers are given to the extension of this exploratory RFT rhythm approach for future quantitative confirmatory investigations.

Study of the Alkaloid Polyneuridine as a Drug Candidate for Therapy of Alzheimer's Disease

Alzheimer's disease is characterized by the progressive and irreversible loss of natural cognitive functions in most elderly people. There is currently no cure for this neurodegenerative disorder, but there are therapies available on the market based on substances that inhibit acetylcholinesterase and cognitive symptoms as a way to improve cholinergic hypofunction. Polyneuridine is the main indole alkaloid extracted from the bark and leaves of Aspidosperma polyneuron, a brazilian plant species popularly known as peroba-rosa. The objective of this work is to investigate, through a scientific prospection, the polyneuridine alkaloid, as well as its anticholinesterase property, since it is known that the therapy of this disease is based on cholinesterase inhibitors. To carry out the study, two of the main publication databases of journals such as PubMed and Web of Science were analyzed. To search for scientific production, we inserted the following keywords combined with the terms in english to carry out the search in international databases: “Polyneuridine”, “Polyneuridine AND anticholinesterase properties”, “Polyneuridine AND Alzheimer's disease”. The need for studies on this alkaloid is urgent, especially since Brazil holds the plant species that produces the most polyneuridine, but the plant species Aspidosperma polyneuron is on the red line of extinction due to the unbridled exploitation of its wood. It is concluded that if this exploration scenario continues, Brazil will lose a very important pharmacological genetic resource of its plant flora.

Bayesian Wavelet Regression for Spatial Estimation

We consider the problem of estimating the properties of an oil reservoir, like porosity and sand thickness, in an exploration scenario where only a few wells have been drilled. We use gamma ray records measured directly from the wells as well as seismic traces recorded around the wells. To model the association between the soil properties and the signals, we fit a linear regression model. Additionally we account for the spatial correla tion structure of the observations using a correlation function that depends on the distance between two points. We transform the predictor variable using discrete wavelets and then perform a Bayesian variable selection us ing a Metropolis search. We obtain predictions of the properties over the whole reservoir providing a probabilistic quantification of their uncertainties, thanks to the Bayesian nature of our method. The cross-validated results show that a very high accuracy can be achieved even with a very small number of wavelet coefficients.

Evaluating the impact of urban design scenarios on walking accessibility: the case of the Madrid ‘Centro’ district.

Walking accessibility planning is seen a powerful approach for moving towards sustainable mobility paradigms; however little attention is paid to determining which factors influence this accessibility and why. This paper addresses this gap between the theory and the practice, and evaluates how far variations in walking accessibility are related to four specific walking needs: attractiveness, comfort, safety, and ease-to-walk. Taking the ‘Centro’ district in Madrid (Spain) as a case study, exploratory scenarios are simulated by altering certain urban design factors for each walking need. Walking accessibility levels are calculated and compared across the exploratory scenarios to gain an insight into how each urban design factor affects walking accessibility. The results show a similar spatial pattern of accessibility for the four walking needs, with higher accessibility values in the north than in the south due to the greater density of destinations. Urban factors related to attractiveness and comfort are found to produce the most significant variations in walking accessibility. The paper concludes with a discussion on the practical usefulness of the findings, particularly in terms of prioritising urban design policies that increase walking accessibility levels.

DatAgent

In this demonstration, we present DatAgent , an intelligent data assistant system that allows users to ask queries in natural language, and can respond in natural language as well. Moreover, the system actively guides the user using different types of recommendations and hints, and learns from user actions. We will demonstrate different exploration scenarios that show how the system and the user engage in a human-like interaction inspired by the interaction paradigm of chatbots and virtual assistants.

Repositioning People in Creative Futures: A Method to Create Sound Advice with Exploratory Scenarios

Foresight scenarios are not only useful presentational devices to show that many aspects of the future are open. Scenarios are means for generating advice that helps policymakers initiate actions in the present or near future that will be of long-term significance. Despite the influence that such advice may have on policy decisions, the Foresight literature has paid very little attention to the creation of policy recommendations. Though reports of scenario exercises frequently conclude with lists of recommendations that follow from the study, there is very little explication of the process whereby advice is elicited from the examination of these future scenarios. This paper addresses this gap, examining how the generation of recommendations is related to the development of scenarios within multiple future repositioning workshop settings. It focuses on the fluency and originality of these recommendations, and how this is influenced by repositioning participants in highly transformational scenarios. Repositioning is the process whereby participants are invited to imagine themselves playing roles in hypothetical future contexts, and on that basis to make decisions or devise strategies as if they actually were immersed in these circumstances. The method proposed and the findings of the case study have implications for why and how this future repositioning approach can be incorporated as a ‘key feature’ in the design of Foresight activities. The aim is also to raise awareness of the need for more exploration of Foresight recommendation methodology.

A Comparison Study of Explicit and Implicit 3-D Transient Electromagnetic Forward Modeling Schemes on Multi-Resolution Grid

This study compares the efficiency of 3-D transient electromagnetic forward modeling schemes on the multi-resolution grid for various modeling scenarios. We developed time-domain finite-difference modeling based on the explicit scheme earlier. In this work, we additionally implement 3-D transient electromagnetic forward modeling using the backward Euler implicit scheme. The iterative solver is used for solving the system of equations and requires a proper initial guess that has significant effect on the convergence. The standard approach usually employs the solution of a previous time step as an initial guess, which might be too conservative. Instead, we test various initial guesses based on the linear extrapolation or linear combination of the solutions from several previous steps. We build up the implicit scheme forward modeling on the multi-resolution grid, which allows for the adjustment of the horizontal resolution with depth, hence improving the performance of the forward operator. Synthetic examples show the implicit scheme forward modeling using the linearly combined initial guess estimate on the multi-resolution grid additionally reduces the run time compared to the standard initial guess approach. The result of comparison between the implicit scheme developed here with the previously developed explicit scheme shows that the explicit scheme modeling is more efficient for more conductive background models often found in environmental studies. However, the implicit scheme modeling is more suitable for the simulation with highly resistive background models, usually occurring in mineral exploration scenarios. Thus, the inverse problem can be solved using more efficient forward solution depending on the modeling setup and background resistivity.

Outlooks, explorations and normative scenarios: Approaches to global energy futures compared

This paper compares recent global energy scenarios developed by governments, international bodies, businesses and the scientific community. We divide the scenarios into three broad classes: outlooks which extrapolate current trends and anticipate policy developments; exploratory scenarios which may consider disruptions; and normative scenarios which derive energy system pathways consistent with a long-term goal. Many organisations are starting to blend outlooks, exploratory and normative approaches. The paper covers trends in primary energy demand to 2040, snapshots of the energy mix in 2040, drivers of demand, and the evolution of scenarios projections developed in recent years. We find sharp divergences between outlooks and normative scenarios compatible with the Paris Agreement on climate change. All published outlooks imply that the world is not on an energy pathway compatible with the Paris Agreement. We conclude with an assessment of emerging themes including: scenario benchmarking and group think; adaptation of scenarios to real world developments; and the plausibility of different types of scenarios. We propose that more dialogue between scenario developers from the scientific community and those working in governments and commercial organisations could be beneficial. Research focusing on the organisational processes through which scenarios are developed could usefully extend this work.