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"adaptive planning"
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Learning and flexibility for water supply infrastructure planning under groundwater resource uncertainty
Water supply infrastructure planning in groundwater-dependent regions is often challenged by uncertainty in future groundwater resource availability. Many major aquifer systems face long-term water table decline due to unsustainable withdrawals. However, many regions, especially those in the developing world, have a scarcity of groundwater data. This creates large uncertainties in groundwater resource predictions and decisions about whether to develop alternative supply sources. Developing infrastructure too soon can lead to unnecessary and expensive irreversible investments, but waiting too long can threaten water supply reliability. This study develops an adaptive infrastructure planning framework that applies Bayesian learning on groundwater observations to assess opportunities to learn about groundwater availability in the future and adapt infrastructure plans. This approach allows planners in data scarce regions to assess under what conditions a flexible infrastructure planning approach, in which initial plans are made but infrastructure development is deferred, can mitigate the risk of overbuilding infrastructure while maintaining water supply reliability in the face of uncertainty. This framework connects engineering options analysis from infrastructure planning to groundwater resources modeling. We demonstrate a proof-of-concept on a desalination planning case for the city of Riyadh, Saudi Arabia, where poor characterization of a fossil aquifer creates uncertainty in how long current groundwater resources can reliably supply demand. We find that a flexible planning approach reduces the risk of over-building infrastructure compared to a traditional static planning approach by 40% with minimal reliability risk (<1%). This striking result may be explained by the slow-evolving nature of groundwater decline, which provides time for planners to react, in contrast to more sudden risks such as flooding where tradeoffs between cost and reliability risk are heightened. This Bayesian approach shows promise for many civil infrastructure domains by providing a method to quantify learning in environmental modeling and assess the effectiveness of adaptive planning.
Journal Article
Flood resilience
Three different conceptual frameworks of resilience, including engineering, ecological and social–ecological have been presented and framed within the context of flood risk management. Engineering resilience has demonstrated its value in the design and operation of technological systems in general and in flood resilient technologies in particular. Although limited to the technical domain, it has broadened the objectives of flood resilient technologies and provided guidance in improving their effectiveness. Socio-ecological resilience is conceived as a broader system characteristic that involves the interaction between human and natural systems. It acknowledges that these systems change over time and that these interactions are of complex nature and associated with uncertainties. Building (socio-ecological) resilience in flood risk management strategies calls for an adaptive approach with short-term measures and a set of monitoring criteria for keeping track of developments that might require adaptation in the long-term (adaptation pathways) and thus built-in adaptive capacity as opposed to building engineering resilience which involves a static approach with a fixed time horizon a set of robust measures designed for specific future conditions or scenarios. The two case studies, from a developing and a developed country, indicate that the concepts of ecological and socio-ecological resilience provide guidance for building more resilient flood risk management systems resulting in an approach that embraces flood protection, prevention and preparedness. The case studies also reveal that the translation of resilience concepts into practice remains a challenge. One plausible explanation for this is our inability to arrive at a quantification of socio-ecological resilience taking into account the various attributes of the concept.
This article is part of the theme issue ‘Urban flood resilience’.
Journal Article
Development and clinical validation of a robust knowledge‐based planning model for stereotactic body radiotherapy treatment of centrally located lung tumors
Purpose
To develop a robust and adaptable knowledge‐based planning (KBP) model with commercially available RapidPlanTM for early stage, centrally located non‐small‐cell lung tumors (NSCLC) treated with stereotactic body radiotherapy (SBRT) and improve a patient's“simulation to treatment” time.
Methods
The KBP model was trained using 86 clinically treated high‐quality non‐coplanar volumetric modulated arc therapy (n‐VMAT) lung SBRT plans with delivered prescriptions of 50 or 55 Gy in 5 fractions. Another 20 independent clinical n‐VMAT plans were used for validation of the model. KBP and n‐VMAT plans were compared via Radiation Therapy Oncology Group (RTOG)–0813 protocol compliance criteria for conformity (CI), gradient index (GI), maximal dose 2 cm away from the target in any direction (D2cm), dose to organs‐at‐risk (OAR), treatment delivery efficiency, and accuracy. KBP plans were re‐optimized with larger calculation grid size (CGS) of 2.5 mm to assess feasibility of rapid adaptive re‐planning.
Results
Knowledge‐based plans were similar or better than n‐VMAT plans based on a range of target coverage and OAR metrics. Planning target volume (PTV) for validation cases was 30.5 ± 19.1 cc (range 7.0–71.7 cc). KBPs provided an average CI of 1.04 ± 0.04 (0.97–1.11) vs. n‐VMAT plan'saverage CI of 1.01 ± 0.04 (0.97–1.17) (P < 0.05) with slightly improved GI with KBPs (P < 0.05). D2cm was similar between the KBPs and n‐VMAT plans. KBPs provided lower lung V10Gy (P = 0.003), V20Gy (P = 0.007), and mean lung dose (P < 0.001). KBPs had overall better sparing of OAR at the minimal increased of average total monitor units and beam‐on time by 460 (P < 0.05) and 19.2 s, respectively. Quality assurance phantom measurement showed similar treatment delivery accuracy. Utilizing a CGS of 2.5 mm in the final optimization improved planning time (mean, 5 min) with minimal or no cost to the plan quality.
Conclusion
The RTOG‐compliant adaptable RapidPlan model for early stage SBRT treatment of centrally located lung tumors was developed. All plans met RTOG dosimetric requirements in less than 30 min of planning time, potentially offering shorter “simulation to treatment” times. OAR sparing via KBPs may permit tumorcidal dose escalation with minimal penalties. Same day adaptive re‐planning is plausible with a 2.5‐mm CGS optimizer setting.
Journal Article
An Automated knowledge‐based planning routine for stereotactic body radiotherapy of peripheral lung tumors via DCA‐based volumetric modulated arc therapy
Purpose
To develop a knowledge‐based planning (KBP) routine for stereotactic body radiotherapy (SBRT) of peripherally located early‐stage non‐small‐cell lung cancer (NSCLC) tumors via dynamic conformal arc (DCA)‐based volumetric modulated arc therapy (VMAT) using the commercially available RapidPlanTM software. This proposed technique potentially improves plan quality, reduces complexity, and minimizes interplay effect and small‐field dosimetry errors associated with treatment delivery.
Methods
KBP model was developed and validated using 70 clinically treated high quality non‐coplanar VMAT lung SBRT plans for training and 20 independent plans for validation. All patients were treated with 54 Gy in three treatments. Additionally, a novel k‐DCA planning routine was deployed to create plans incorporating historical three‐dimensional‐conformal SBRT planning practices via DCA‐based approach prior to VMAT optimization in an automated planning engine. Conventional KBPs and k‐DCA plans were compared with clinically treated plans per RTOG‐0618 requirements for target conformity, tumor dose heterogeneity, intermediate dose fall‐off and organs‐at‐risk (OAR) sparing. Treatment planning time, treatment delivery efficiency, and accuracy were recorded.
Results
KBPs and k‐DCA plans were similar or better than clinical plans. Average planning target volume for validation was 22.4 ± 14.1 cc (7.1–62.3 cc). KBPs and k‐DCA plans provided similar conformity to clinical plans with average absolute differences of 0.01 and 0.01, respectively. Maximal doses to OAR were lowered in both KBPs and k‐DCA plans. KBPs increased monitor units (MU) on average 1316 (P < 0.001) while k‐DCA reduced total MU on average by 1114 (P < 0.001). This routine can create k‐DCA plan in less than 30 min. Independent Monte Carlo calculation demonstrated that k‐DCA plans showed better agreement with planned dose distribution.
Conclusion
A k‐DCA planning routine was developed in concurrence with a knowledge‐based approach for the treatment of peripherally located lung tumors. This method minimizes plan complexity associated with model‐based KBP techniques and improve plan quality and treatment planning efficiency.
Journal Article
A framework to integrate multifunctionality analyses into green infrastructure planning
ContextGreen infrastructure (GI) has become an integral part of the process leading toward urban sustainability because it provides multiple ecosystem services that contribute to urban ecosystems and human health. Planners and managers have therefore attempted to understand and improve GI multifunctionality.ObjectivesThis study has characterized and mapped GI multifunctionality in the Fengtai District of Beijing based on the ecosystem services (ES) perspective and has developed an adaptive model to improve its multifunctionality. The study has aimed to: (1) assess and map GI multifunctional degree, (2) characterize GI multifunctional types, and (3) propose adaptive solutions based on characterization of GI multifunctional types.MethodsBiophysical models and social questionnaires were used to quantify and map ES, ES hotspots, and ES bundles to identify the degree of multifunctionality and characterize GI multifunctional types. An adaptive model was designed to improve GI multifunctionality for local planning and design practice.ResultsThree GI multifunctional degrees were mapped, and areas with high multifunctional degree were found to account for only 5.55% of the study area. Seven GI multifunctional types were identified by the distinct heterogeneity of their compositions and function sets. These types of GI also implied different improvement strategies for GI planning and design practice. The adaptive model offers integrated solutions for preserving, restoring, and embedding levels that correspond to the characterization of GI multifunctional types.ConclusionsThe ES-based framework proposed in this paper integrates multifunctionality analyses and can be helpful to urban planners and designers in adaptive GI planning.
Journal Article
Integration of Vehicle–Terrain Interaction and Fuzzy Cost Adaptation for Robust Path Planning
This paper proposes an adaptive path-planning algorithm for unmanned ground vehicles (UGVs) in three-dimensional terrain environments. The algorithm first constructs an interference model between the UGV chassis and the three-dimensional terrain, taking into account the impact of terrain undulations on vehicle driving stability. A dynamic cost-adjustment mechanism for multi-task modes was designed, which introduces a learning model to automatically identify task types and dynamically adjust the weights of various cost factors in path planning accordingly. This paper constructs simulation environments for sparse obstacle scenes and high-density obstacle scenes, respectively, to verify the effectiveness of the path-planning results of the algorithm in different task modes. The experimental results show that the proposed method can generate smoother, safer, and more task-matched trajectory paths while ensuring path feasibility, verifying the good adaptability and robustness of this algorithm for complex unstructured environments under multi-task driving conditions.
Journal Article
Commissioning an in‐room mobile CT for adaptive proton therapy with a compact proton system
Purpose
To describe the commissioning of AIRO mobile CT system (AIRO) for adaptive proton therapy on a compact double scattering proton therapy system.
Methods
A Gammex phantom was scanned with varying plug patterns, table heights, and mAs on a CT simulator (CT Sim) and on the AIRO. AIRO‐specific CT‐stopping power ratio (SPR) curves were created with a commonly used stoichiometric method using the Gammex phantom. A RANDO anthropomorphic thorax, pelvis, and head phantom, and a CIRS thorax and head phantom were scanned on the CT Sim and AIRO. Clinically realistic treatment plans and nonclinical plans were generated on the CT Sim images and subsequently copied onto the AIRO CT scans for dose recalculation and comparison for various AIRO SPR curves. Gamma analysis was used to evaluate dosimetric deviation between both plans.
Results
AIRO CT values skewed toward solid water when plugs were scanned surrounded by other plugs in phantom. Low‐density materials demonstrated largest differences. Dose calculated on AIRO CT scans with stoichiometric‐based SPR curves produced over‐ranged proton beams when large volumes of low‐density material were in the path of the beam. To create equivalent dose distributions on both data sets, the AIRO SPR curve's low‐density data points were iteratively adjusted to yield better proton beam range agreement based on isodose lines. Comparison of the stoichiometric‐based AIRO SPR curve and the “dose‐adjusted” SPR curve showed slight improvement on gamma analysis between the treatment plan and the AIRO plan for single‐field plans at the 1%, 1 mm level, but did not affect clinical plans indicating that HU number differences between the CT Sim and AIRO did not affect dose calculations for robust clinical beam arrangements.
Conclusion
Based on this study, we believe the AIRO can be used offline for adaptive proton therapy on a compact double scattering proton therapy system.
Journal Article
Multi-UAV Collaborative Target Search Method in Unknown Dynamic Environment
The challenge of search inefficiency arises when multiple UAV swarms conduct dynamic target area searches in unknown environments. The primary sources of this inefficiency are repeated searches in the target region and the dynamic motion of targets. To address this issue, we present the distributed adaptive real-time planning search (DAPSO) technique, which enhances the search efficiency for dynamic targets in uncertain mission situations. To minimize repeated searches, UAVs utilize localized communication for information exchange and dynamically update their situational awareness regarding the mission environment, facilitating collaborative exploration. To mitigate the effects of target mobility, we develop a dynamic mission planning method based on local particle swarm optimization, enabling UAVs to adjust their search trajectories in response to real-time environmental inputs. Finally, we propose a distance-based inter-vehicle collision avoidance strategy to ensure safety during multi-UAV cooperative searches. The experimental findings demonstrate that the proposed DAPSO method significantly outperforms other search strategies regarding the coverage and target detection rates.
Journal Article
Deformable and Fragile Object Manipulation: A Review and Prospects
Deformable object manipulation (DOM) is a primary bottleneck for the real-world application of autonomous robots, requiring advanced frameworks for sensing, perception, modeling, planning, and control. When fragile objects such as soft tissues or fruits are involved, ensuring safety becomes the paramount concern, fundamentally altering the manipulation problem from one of pure trajectory optimization to one of constrained optimization and real-time adaptive control. Existing DOM methodologies, however, often fall short of addressing fragility constraints as a core design feature, leading to significant gaps in real-time adaptiveness and generalization. This review systematically examines individual components in DOM with a focus on their effectiveness in handling fragile objects. We identified key limitations in current approaches and, based on this analysis, discussed a promising framework that utilizes both low-latency reflexive mechanisms and global optimization to dynamically adapt to specific object instances.
Journal Article