Scenario Planning Research Articles

Recent Cereal Phenological Variations under Mediterranean Conditions

This study analyzes the temporal patterns of rainfed cereal phenology extracted from the GIMMS NDVI3g dataset in the main cereal-growing regions under a Mediterranean climate in Spain, Portugal, France and Italy during the period 1982–2022. The series before and after the beginning of the 21st century were analyzed separately. Phenological parameters were extracted using the modified dynamic threshold method, and their trends were analyzed. Correlation analyses were performed to study the relationships among these parameters and to analyze the influence of hydroclimatic variables on the start (SOS) and end (EOS) of the growing season. Results showed a temporal reversal in phenological trends between both study periods, coinciding with the global warming hiatus. In the first period (1982–2002), SOS and EOS advanced (−7.5 and −3.1 days, respectively), and the length of growing season (LOS) increased. However, during the second stage (2003–2022), SOS and EOS were delayed (7.5 and 1.7 days, respectively), and LOS decreased. Similar dynamics were observed for the influence of the hydroclimatic variables on SOS and EOS, stronger in the first period and weaker in the second. This study provides valuable information on the phenological dynamics of rainfed cereals that may be useful for their management and planning in climate change scenarios.

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.

Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.

A hierarchical attention network integrating multi-scale relationship for drug response prediction

Anticancer drug response prediction with deep learning technology has become the foundation of precision medicine. It is essential for anticancer drug response prediction to incorporate multi-scale relationships within feature items and biomedical entities. Therefore, we propose MultiDRP that develops the hierarchical attention networks integrating multi-scale relationship for drug response prediction. MultiDRP can fuse both internal correlation of feature items and external relationship of biomedical entities by hierarchically integrating graph attention and self-attention networks to improve the anticancer drug response prediction. A variety of results showed that MultiDRP generated the great representation by integrating multi-scale relationships, and achieved higher performance compared to existing methods on various prediction scenarios. The results of network proximity, gene ontology biological process (GOBP) enrichment, and drug pathway association analysis show that MultiDRP can accurately screen the sensitive and resistant drugs for cancer cell lines. In vitro experiments, eight novel drugs predicted by MultiDRP exhibited high sensitivity to lung cancer cell line NCI-H23, seven of which showed IC50 values of less than 10 nM. These results further suggest that MultiDRP can serve as a powerful tool for anticancer drug response prediction. The source data and code are available at https://github.com/pengsl-lab/MultiDRP.git.

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

Geomatics-enabled Interdisciplinary Approach Based on Geospatial Data Processing for Hydrogeological Risk-analysis

Hydrogeological risks that are associated with rivers have emerged as a significant concern worldwide, impacting both natural ecosystems and human settlements. This contribution presents an interdisciplinary project that leverages many technologies for data-acquisition and modeling to comprehensively analyze and manage risks in riverine environments. The project integrates geomatics, geological, and hydrological techniques to provide a holistic understanding of river dynamics and their associated hazards. As a central component of this project, geomatics plays a pivotal role in instrumental field surveying through the deployment of photogrammetry and LiDAR instruments. Remote-sensing data from satellite imagery further enriches the project’s temporal analysis capabilities. By analyzing this data over time, researchers can monitor changes in river patterns, land use, and climate-related variables; this helps identify trends and potential triggers for hydrological events. To manage and integrate the vast amount of geospatial information that is generated, a geodatabase within a geographic information system (GIS) has been established. It enables efficient data storage, retrieval, and analysis, fostering collaboration among multidisciplinary researcher teams. This system offers tools for risk-assessment, modeling, and scenario planning; these allow for proactive measures for mitigating hydrological risks.

Beyond Static Obstacles: Integrating Kalman Filter with Reinforcement Learning for Drone Navigation

Autonomous drones offer immense potential in dynamic environments, but their navigation systems often struggle with moving obstacles. This paper presents a novel approach for drone trajectory planning in such scenarios, combining the Interactive Multiple Model (IMM) Kalman filter with Proximal Policy Optimization (PPO) reinforcement learning (RL). The IMM Kalman filter addresses state estimation challenges by modeling the potential motion patterns of moving objects. This enables accurate prediction of future object positions, even in uncertain environments. The PPO reinforcement learning algorithm then leverages these predictions to optimize the drone’s real-time trajectory. Additionally, the capability of PPO to work with continuous action spaces makes it ideal for the smooth control adjustments required for safe navigation. Our simulation results demonstrate the effectiveness of this combined approach. The drone successfully navigates complex dynamic environments, achieving collision avoidance and goal-oriented behavior. This work highlights the potential of integrating advanced state estimation and reinforcement learning techniques to enhance autonomous drone capabilities in unpredictable settings.

Wind Power Integration and Challenges in Low Wind Zones. A Study Case: Albania

High wind performance systems are influenced by many factors such as site wind resources and configuration, technical wind turbine features and many financial conditions. Scenario planning and modelling activities often focus on restricted parameters and numbers to justify wind power plant performance. To better understand possible pathways to scaling up the distributed wind market in Albania, a deep and multidimensional calculations based on Monte Carlo analysis, using RETScreen and wind JEDI model, to assess socio-economic impact as a function of turbine output power, operating and maintenance cost and many other financial inputs by testing different WT (i.e., VESTAS, GAMESA, W2E and NORDEX) with rated power from 3.45 MW up to 4.5MW applied on LCOE, NPV, SPP, equity payback, B-C, after-tax IRR on equity and effects of GHG credits extended at a sensitivity range of ±35% is scientifically performed. From the simulation results LCOE reaches a minimal value of €43.48/MWh, if the debt rate is 99 % and a debt interest rate of 5.0%, a TotCapEx of €828/MW (-35 % less expenditures) indexed as the best scenario. For the base case scenario LCOE results €62.79/MWh, when applying a debt rate of 80% and a TotCapEx of (€1274/MW), while in the worst-case scenario LCOE impart a maximal value of €87.63/MWh if a TotCapEx of €1720/MW (+35 % more expenditures) and a share of 52 % debt rate is applied. Local annual economic impact (m€) during construction period and operating period evaluated in the wind JEDI model result around m€ 89.92 and m€ 23.54, respectively. As a conclusion, wind power plants (WPP), installed in low wind zones (Albania and many other EU countries) would be of interest if an electricity export rate of 110€/MWh, and a GHG credit rate of €50/tCO2 were accepted.

Simulating Future Exposure to Coastal Urban Flooding Using a Neural Network–Markov Model

Urbanization and climate change are two major challenges of the 21st century, and the effects of climate change, combined with the urbanization of coastal areas, increase the frequency of coastal flooding and the area exposed to it, resulting in increased risk of flooding and larger numbers of people and properties being vulnerable. An urban growth modeling system was used to simulate future growth scenarios along the coast of the Vendée region in western France, and the potential exposure to flooding with each scenario was evaluated. The model used was an Artificial Neural Network combined with a Markov Chain, using data obtained by the remote sensing and geographic information system techniques to predict three future urban growth scenarios: business as usual, environmental protection, and strategic urban planning. High-risk flood areas and future sea level projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change were then used to assess future flood risk under each growth scenario in the study area. According to the results, the different growth scenarios are associated with different development patterns, and the strategic urban planning scenario significantly reduces the risk of flooding compared to the other two scenarios. However, the rise in sea level considerably expands the areas vulnerable to flooding. Finally, the methodology adopted can be used to prepare for the impact of climate change and develop strategies to mitigate the risk of flooding in the future.

Accurate data-driven sliding mode parking control for autonomous ground vehicles with efficient trajectory planning in dynamic industrial scenarios

Accurate data-driven sliding mode parking control for autonomous ground vehicles with efficient trajectory planning in dynamic industrial scenarios

“We don’t have a lot of trees, but by God, do we have a lot of fish”: imagining postcolonial futures for the Nunatsiavut fishing industry

Telling stories can be an empowering exercise, providing important insights into the values and priorities of the storytellers. This article shares stories told during a participatory scenario planning process among Inuit, an Indigenous People of northern Canada, Greenland, and Alaska, USA. This research takes place in Nunatsiavut, a land claim area in Labrador, Canada, to explore how visions provide insights into postcolonial futures for the fishing industry. Beginning in 2019, a group of fisheries stakeholders and managers came together to create a visioning process that would help them to develop consensus around priorities for the industry. Facilitated by university researchers, Inuit in the commercial fishing industry participated in an iterative data-collection process that involved interviews and a workshop. This article shares what was found during the scenario-planning process and position stories of the future within the context of Indigenous sovereignty.

Participatory mapping in exploratory scenario planning: Necessity or luxury?

Exploratory scenarios based on participatory approaches proved helpful in dealing with uncertainty and complexity in urban and transport systems. Such scenarios usually create coherent and manageable narratives that capture broad patterns and trends at global scales. However, significant difficulties exist in bridging exploratory scenario narratives with spatial and local realities, which is crucial for guiding policymaking in urban, regional, and mobility planning. This paper explores whether and how exploratory scenario narratives and participatory mapping processes can establish a reciprocal relationship, wherein narrative-based maps contribute to spatially shaping scenario narratives. It uses an experimental approach involving three participatory workshops with experts and local stakeholders specifically tailored to the mapping of land use and transportation-related scenario narratives for the year 2050 in the Henares Corridor (Madrid, Spain). The spatial transformations mapped during workshops have indicated that participatory mapping processes have an impact on scenario narratives in different ways, enabling the identification of three categories of mapping episodes with different meanings: (i) complementing narratives, (ii) expanding narratives, and (iii) modifying narratives. The paper closes by examining the implications of the obtained findings for policymaking, including a reflection on the research’s limitations and potential for application in real-world planning processes.

Integrated Approach of Historical Landscape Characterisation Techniques and Remote Sensing for the Definition of Predictive Models and Scenario Analysis in the Planning of Archaeological Areas

This study explores the synergistic integration of remote sensing (RS) and Historical Landscape Characterisation (HLC) methodology as an innovative, multi-scalar and holistic approach to enhance archaeological planning. The goal is to maximize the effectiveness of the investigations, optimizing data collection and improving the contextual understanding of the sites. In fact, these methodologies can significantly contribute to the documentation, conservation, planning and valorisation of archaeological areas. By integrating RS data with features detected by HLC, a complete picture is obtained that facilitates a deeper understanding of the landscape and historical dynamics. This article will explain the combined approach of RS and HLC, presenting some methodologies key to improving the precision and effectiveness of archaeological planning. This integration facilitates the sustainable preservation of archaeological resources and contributes to the conscious management of cultural heritage in the context of contemporary development. The paper demonstrates, through a case study, how the application of the two methodologies (RS and HLC) in an integrated form can provide an exhaustive interpretation of the territory in which the archaeological area is located, which can represent an exhaustive knowledge base on which to set up effective processes for the strategic territorial planning of archaeological areas.

Assessing Residential Building Energy Efficiency Using Evolutionary Dendritic Neural Regression

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain.

Neural digital twins: reconstructing complex medical environments for spatial planning in virtual reality

PurposeSpecialized robotic and surgical tools are increasing the complexity of operating rooms (ORs), requiring elaborate preparation especially when techniques or devices are to be used for the first time. Spatial planning can improve efficiency and identify procedural obstacles ahead of time, but real ORs offer little availability to optimize space utilization. Methods for creating reconstructions of physical setups, i.e., digital twins, are needed to enable immersive spatial planning of such complex environments in virtual reality.MethodsWe present a neural rendering-based method to create immersive digital twins of complex medical environments and devices from casual video capture that enables spatial planning of surgical scenarios. To evaluate our approach we recreate two operating rooms and ten objects through neural reconstruction, then conduct a user study with 21 graduate students carrying out planning tasks in the resulting virtual environment. We analyze task load, presence, perceived utility, plus exploration and interaction behavior compared to low visual complexity versions of the same environments.ResultsResults show significantly increased perceived utility and presence using the neural reconstruction-based environments, combined with higher perceived workload and exploratory behavior. There’s no significant difference in interactivity.ConclusionWe explore the feasibility of using modern reconstruction techniques to create digital twins of complex medical environments and objects. Without requiring expert knowledge or specialized hardware, users can create, explore and interact with objects in virtual environments. Results indicate benefits like high perceived utility while being technically approachable, which may indicate promise of this approach for spatial planning and beyond.

Breaking out of conventions: How scenario planners can increase their reflexivity regarding societal imaginaries

Futures imagined in scenario processes reflect both stakeholder perspectives and broader societal imaginaries: collectively-held, institutionally-stabilized visions of the future. The presence of imaginaries has mostly remained implicit in studies of scenario planning, especially in development contexts. We argue that scenario planning will benefit from reflexivity regarding imaginaries. Here, reflexivity refers to an awareness regarding different perspectives, assumptions, values, and—oft-hidden—politics at play. We developed a framework of relevant imaginaries and assessed how and to what extent these are expressed in scenario narratives, through analyzing seven scenario sets focused on agriculture, food security and climate change in the Global South. Our results show that neoliberal and sustainable development imaginaries are dominant in these scenarios. Imaginaries from the Global South are scarcely represented—arguably because of that, we observe few regional perspectives on potential challenges in these scenario sets. We conclude that the scenario sets offer effective critique on neoliberal mechanisms and global development dynamics, but do not provide significant room for transformational alternatives from the Global South. We argue that opening up explorative scenario planning to more pluralistic conceptions of the future can greatly enhance its reflexivity, and a representative mix of imaginaries allows for scenario planning that leads to more transformational policies.

Synergies of future-oriented research, sustainable tourism and adaptive governance: a dialogue with Dr Stefan Hartman, Head of department at the European Tourism Futures Institute

ABSTRACT This edited transcript documents a dialogue between Rodney Westerlaken, editor-in-chief of Research in Hospitality Management, and Dr. Stefan Hartman, Head of Department at the European Tourism Futures Institute (ETFI) at NHL Stenden University of Applied Sciences. The conversation explores multifaceted dimensions of the tourism and hospitality sectors, encompassing the changing landscape of destination management, the influence of futureoriented research and the urgent imperatives arising from contemporary global challenges, such as over-tourism and sustainable development. The dialogue offers valuable insights into the future trajectory of tourism and hospitality research, emphasising the pivotal role of adaptive governance, stakeholder collaboration and scenario planning in shaping sustainable tourism practices. Furthermore, it underscores the significance of balancing economic interests with societal and environmental concerns in the evolving tourism landscape. In this insightful conversation, Dr Hartman’s extensive urban and regional planning background, coupled with his current role at the European Tourism Futures Institute, provides a unique perspective on the future of tourism, leisure and hospitality. Dr Hartman’s work focuses on adaptive tourism areas and sustainable destination development, offering valuable insights into the complex interplay between policy, industry and community in shaping tourism destinations.

Scenario Planning for Employee Engagement Development

This research examined scenario planning for development of employee engagement in PT XYZ. Employees who, did not see any career path opportunities in the company, and dissatisfied with working environment regarding rewards and recognition for work performance tended to experience low engagement. The purpose of this research was divided into 4 (three): 1) to identify and analyze the existing employee engagement quality and factors 2) to understand employee needs and expectations in developing employee engagement, and 3) to create scenarios planning for employee engagement development in the company. The research used a qualitative-quantitative method, aligned with interview and survey results, focused on the method of scenario planning stage consisting of Tracking, Analyzing, Imaging, Deciding, and Acting (TAIDA). There were 4 (four) scenario planning that could develop employee engagement and effect organizational performance outcomes. Based on those four-scenario planning matrix, employees in the company fell into the “We Can” scenario. In this scenario, employees were at the level of moderate engagement and the company was currently facing challenges. The “We Can” scenario planning can be implemented by focusing on the performance appraisal and feedback from leaders to gain recognition, appreciation, and employee and career development to enhance the level of engagement. The acting step will help to create the short-term planning (in a year) according to the trends that need improvement that can be implemented by the company. If this “We Can” scenario planning can be implemented, employees can be more productive and committed to achieving organizational goals.

Between episodes of strategy: Sociomateriality, sensemaking, and dysfunction in a scenario planning process

Scenario planning processes, which occur over multiple episodes, facilitate the development of alternative stories of the future in order to challenge current thinking and help organizations create more robust strategies. But what happens between those episodes and how does it affect the process? Utilizing a sociomaterial perspective, we examine sensemaking within and between multiple scenario planning workshops of a multi-organization strategy process. We observe both subtle changes and more blatant interventions in the production of critical materials in the spaces between workshops. Sociomateriality and sensemaking become entangled as the inputs to, and outputs from, workshops are subject to increased influence from ‘normal’ work structures as episodic and continuous sensemaking converge gradually over time.

Using the low-tech concept to create scenarios: an analysis of its potential to design for sustainable urban future

AbstractDesigners must be equipped with methods to contribute to sustainability transitions. Scenario planning arose as approach to integrate future uncertainties while the low-tech concept promotes technological discernment. This paper looks at how low-tech fuelled scenario planning. Analysing institutional and archetypical scenarios shows a partial integration through high and low-tech extremes. However, more nuanced considerations are lacking. This paper shows that low-tech can bring an interesting dimension to future scenarios and thus contributes to method development for sustainable design.

Aspect-Aware Graph Attention Network for Heterogeneous Information Networks.

Graph Convolutional Networks (GCNs) derive inspiration from recent advances in computer vision, by stacking layers of first-order filters followed by a nonlinear activation function to learn entity or graph embeddings. Although GCNs have been shown to boost the performance of many network analysis tasks, they still face tremendous challenges in learning from Heterogeneous Information Networks (HINs), where relations play a decisive role in knowledge reasoning. What's more, there are multiaspect representations of entities in HINs, and a filter learned in one aspect do not necessarily apply to another. We address these challenges by proposing the Aspect-Aware Graph Attention Network (AGAT), a model that extends GCNs with alternative learnable filters to incorporate entity and relational information. Instead of focusing on learning the general entity embeddings, AGAT learns the adaptive entity embeddings based on prediction scenario. Experiments of link prediction and semi-supervised classification verify the effectiveness of our algorithm.