Hasil untuk "Regional planning"

R. Naiman, H. Décamps, M. Pollock

Hehui Jin, Y. Qian, Barry R. Weingast

Aaqib Suhail Mir , Waqrul Neesa

Sir, Insulin autoimmune syndrome (IAS), also known as Hirata’s disease, is a rare cause of endogenous hyperinsulinemic hypoglycemia characterized by the presence of insulin autoantibodies. The occurrence of IAS during pregnancy is exceedingly uncommon, and its coexistence with type 1 diabetes mellitus poses significant perioperative glycemic challenges due to unpredictable insulin–antibody interactions. We report the anesthetic management of a parturient with type 1 diabetes mellitus complicated by IAS undergoing elective cesarean delivery, highlighting the importance of meticulous perioperative glucose control. A 28-year-old primigravida at 37+ weeks of gestation with a known history of type 1 diabetes mellitus was diagnosed with IAS following recurrent unexplained hypoglycemic episodes. After a multidisciplinary discussion involving anesthesiology, obstetrics, and endocrinology teams, an elective cesarean section was planned. In the operating theater, standard American Society of Anesthesiologists monitoring was instituted along with left uterine displacement. Baseline capillary blood glucose was 104 mg/dL. Considering the risk of masked hypoglycemia and exaggerated stress response, spinal anesthesia was preferred. Subarachnoid block was administered at the L3–L4 interspace using 1.8 mL of 0.5% hyperbaric bupivacaine combined with fentanyl 20μg. Intraoperative blood glucose monitoring was performed every 10 min. A continuous infusion of 10% dextrose at 50 mL/h was maintained. No intraoperative insulin was administered. Blood glucose values ranged between 66 and 124 mg/dL. Two episodes of hypoglycemia were promptly treated with 25% dextrose boluses. Delivery was achieved within 8 min of skin incision. A healthy male neonate weighing 3.0 kg was delivered with Apgar scores of 8 and 9 at 1 and 5 min, respectively. Maternal hemodynamics remained stable, and hypotension episodes were managed with phenylephrine boluses. Postoperatively, the patient was monitored in a high-dependency unit. Dextrose infusion was continued for 12 h with hourly glucose monitoring initially, followed by 4-hourly measurements. Low-dose insulin therapy was restarted under endocrinology guidance. No further hypoglycemic episodes were observed, and the postoperative course was uneventful. IAS complicating type 1 diabetes represents a rare and high-risk metabolic scenario. The binding of circulating insulin by autoantibodies leads to delayed and unpredictable insulin release, resulting in recurrent hypoglycemia. Pregnancy further compounds metabolic instability due to altered insulin sensitivity and hormonal fluctuations. Anesthetic management in such patients should focus on maintaining euglycemia during fasting and surgical stress, avoiding triggering medications, and ensuring frequent glucose monitoring. Regional anesthesia offers advantages by attenuating the neuroendocrine stress response and allowing early recognition of hypoglycemic symptoms. This case highlights that spinal anesthesia can be safely administered in parturients with IAS when meticulous perioperative planning and close multidisciplinary coordination are ensured.

Grazia Brunetta, Danial Mohabat Doost, Erblin Berisha et al.

In contemporary spatial planning, territorial resilience is rapidly gaining relevance, referring to a territory’s capacity to withstand, adapt to, recover from, and transform in response to environmental, social, and economic pressures. However, several constraints limit its operationalisation in planning. A key element to addressing this gap is to investigate where and which interventions are most urgently needed to tackle the impact of hazards on territories. This can be achieved by understanding and localising the vulnerabilities of territorial systems, thereby enabling the definition of appropriate mitigation and adaptation measures. This paper presents the application of R3C-GeoResilience, an open-source GIS tool and its methodological framework, which allows mapping territorial vulnerabilities across different geographical contexts and spatial scales. The methodology is applied to the Italian case of the Union of Bassa Romagna (UBR), aiming to build capacity for local practitioners to implement resilience thinking in decision-making processes. Findings underscore the potential of R3C-GeoResilience to enhance evidence-based planning and policymaking, supporting adaptive and transformative strategies to address territorial vulnerabilities. The application of the research demonstrates the replicability and adaptability of the methodological framework for integrating participatory vulnerability mapping into local governance and urban planning strategies, thereby enhancing the resilience of territories.

Daniel Štraub, Daniel Baldwin Hess

Yanjun Liu, Xiaomei Li, Shangjun Lu et al.

The landscapes of China’s multi-ethnic areas are rich in natural and cultural value, but they are threatened by homogenization and urbanization. This study aims to establish a method for identifying and classifying the landscape characters in China’s multi-ethnic areas to support the protection and sustainable development of the landscape in these areas. Taking the Miao Frontier Corridor as an example, the study optimized a parameterization method of landscape character assessment (LCA), integrated relevant cultural and natural elements, and used the K-means clustering algorithm to determine the landscape character types and regions of the Miao Frontier Corridor. The results show that (1) the natural conditions, ethnic exchanges, and historical institutions of the Miao Frontier Corridor have had a significant impact on its overall landscape; and (2) using ethnic group culture as a cultural element in LCA helps to reveal the unique cultural value of areas with different landscape characters. This study expands the LCA framework and applies it to multi-ethnic areas in China, thereby establishing a database that can serve as the basis for cross-regional landscape protection, management, and development planning in these areas. The research methods can be widely used in other multi-ethnic areas in China.

Benned Hedegaard, Yichen Wei, Ahmed Jaafar et al.

Task and motion planning is a well-established approach for solving long-horizon robot planning problems. However, traditional methods assume that each task-level robot action, or skill, can be reduced to kinematic motion planning. We address the challenge of combining motion planning with closed-loop motor controllers that go beyond mere kinematic considerations. We propose a novel framework that integrates these policies into motion planning using Composable Interaction Primitives (CIPs), enabling the use of diverse, non-composable pre-learned skills in hierarchical robot planning. We validate our Task and Skill Planning (TASP) approach through real-world experiments on a bimanual manipulator and a mobile manipulator, demonstrating that CIPs allow diverse robots to combine motion planning with general-purpose skills to solve complex, long-horizon tasks.

Pulkit Verma, Ngoc La, Anthony Favier et al.

Large language models (LLMs) have demonstrated impressive capabilities across diverse tasks, yet their ability to perform structured symbolic planning remains limited, particularly in domains requiring formal representations like the Planning Domain Definition Language (PDDL). In this paper, we present a novel instruction tuning framework, PDDL-Instruct, designed to enhance LLMs' symbolic planning capabilities through logical chain-of-thought reasoning. Our approach focuses on teaching models to rigorously reason about action applicability, state transitions, and plan validity using explicit logical inference steps. By developing instruction prompts that guide models through the precise logical reasoning required to determine when actions can be applied in a given state, we enable LLMs to self-correct their planning processes through structured reflection. The framework systematically builds verification skills by decomposing the planning process into explicit reasoning chains about precondition satisfaction, effect application, and invariant preservation. Experimental results on multiple planning domains show that our chain-of-thought reasoning based instruction-tuned models are significantly better at planning, achieving planning accuracy of up to 94% on standard benchmarks, representing a 66% absolute improvement over baseline models. This work bridges the gap between the general reasoning capabilities of LLMs and the logical precision required for automated planning, offering a promising direction for developing better AI planning systems.

Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim et al.

Large language models (LLMs) have shown remarkable advancements in enabling language agents to tackle simple tasks. However, applying them for complex, multi-step, long-horizon tasks remains a challenge. Recent work have found success by separating high-level planning from low-level execution, which enables the model to effectively balance high-level planning objectives and low-level execution details. However, generating accurate plans remains difficult since LLMs are not inherently trained for this task. To address this, we propose Plan-and-Act, a novel framework that incorporates explicit planning into LLM-based agents and introduces a scalable method to enhance plan generation through a novel synthetic data generation method. Plan-and-Act consists of a Planner model which generates structured, high-level plans to achieve user goals, and an Executor model that translates these plans into environment-specific actions. To train the Planner effectively, we introduce a synthetic data generation method that annotates ground-truth trajectories with feasible plans, augmented with diverse and extensive examples to enhance generalization. We evaluate Plan-and-Act using web navigation as a representative long-horizon planning environment, demonstrating a state-of-the-art 57.58% success rate on the WebArena-Lite benchmark as well as a text-only state-of-the-art 81.36% success rate on WebVoyager.

Yuhao Zhang, Ru Guo, Kaiming Peng et al.

In the context of global efforts to address climate change, research into regional carbon neutrality strategies has become especially critical. For developing countries and regions, it is essential to scientifically and rationally assessing the paths for small-scale regional transformations under carbon neutrality imperatives to effectively implement low-carbon transition measures. This study utilizes Chongming District in Shanghai of China as a case to establish a framework for forecasting carbon emission and sink from a multi-dimensional natural-social perspective. This facilitates the simulation and optimization of pathways for carbon neutrality transformation. The results indicate: (1) From 2000 to 2020, the total regional carbon emission exhibited a rising trend, while the total carbon sink initially declined then increased, indicating potential enhancement zone with significant potential and space for carbon neutrality development. (2) Enhanced management of ecological spaces and land use planning result in notable increases in carbon sink. Strategic measures such as emission and consumption reductions, alongside energy transitions, effectively controlled carbon emission growth and facilitated comprehensive decarbonization. (3) By combining ecological priority with enhanced control and balanced development with enhanced control, the region can achieve carbon neutrality. This showcases the effective role of policy regulation in facilitating high-quality carbon–neutral transformations. (4) Effective ecosystem management along with robust reduction and transition strategies enable county-level carbon–neutral transformations, offering a model and methodological support for other developing regions facing the twin challenges of economic growth and environmental sustainability.

Tebogo Mogotloane, Valery Louw

Background: The poor resolution of customer complaints in the domain of public service is often attributed to a lack of accountability, transparency, communication, leadership, competent personnel, and well-defined complaint-management policies. Aim: This article examined the efficacy and efficiency of the processes and procedures used to carry out the customer complaints management policy within the public service. Setting: The study focused on the Department of Employment and Labour in the Free State province. Methods: A qualitative study design was adopted, with self-administered questionnaires used to collect data from 20 purposefully selected participants from the Department of Employment and Labour – Free State province. Results: The research revealed several key findings. Firstly, there was a lack of consequences for subpar performance. Secondly, inadequate communication and coordination hindered the timely resolution of customer complaints, and minimal frontline staff training on the customer complaints management policy. Thirdly, it was discovered that a lack of capacity resulted in underreporting of complaints, which has a detrimental impact on how quickly and effectively customer complaints are handled. Conclusion and contribution: The implications of this study, therefore, draw attention to redress mechanisms as a vehicle to turn around and improve public service delivery. The study recommends that the Department of Employment and Labour should consider increasing the capacity of staff in handling customer complaints, developing appropriate customer complaints management training manuals, and establishing a business unit or directorate that deals with customer complaints.

Angelo Bertoni

The professional trajectory of Étienne de Groër provides the opportunity to follow the development of town planning theories in the inter-war period which was marked by the gradual affirmation of the regional scale and the strengthening of the combination of the urban plan and regulations. His career also reveals the difficulties and wanderings of the town planning profession, which in the 1930s was facing the still emerging public commission and the competition from other professions. In this context, the close collaboration with a renowned urban planner, Donat-Alfred Agache, and the international mobility played a key role in Étienne de Groër’s professional affirmation. Lisbon and other major Portuguese cities gave him the opportunity to implement and refine his town planning ideas, in which the concept of the garden city figured prominently.

Marie Schmidt, Anita Schöbel

For all line-based transit systems like bus, metro and tram, the routes of the lines and the frequencies at which they are operated are determining for the operational performance of the system. However, as transit line planning happens early in the planning process, it is not straightforward to predict the effects of line planning decisions on relevant performance indicators. This challenge has in more than 40 years of research on transit line planning let to many different models. In this chapter, we concentrate on models for transit line planning including transit line planning under uncertainty. We pay particular attention to the interplay of passenger routes, frequency and capacity, and specify three different levels of aggregation at which these can be modeled. Transit line planning has been studied in different communities under different names.The problem can be decomposed into the components line generation, line selection, and frequency setting. We include publications that regard one of these individual steps as well as publications that combine two or all of them. We do not restrict to models build with a certain solution approach in mind, but do have a focus on models expressed in the language of mathematical programming.

Jinbang Huang, Allen Tao, Rozilyn Marco et al.

Task and motion planning (TAMP) frameworks address long and complex planning problems by integrating high-level task planners with low-level motion planners. However, existing TAMP methods rely heavily on the manual design of planning domains that specify the preconditions and postconditions of all high-level actions. This paper proposes a method to automate planning domain inference from a handful of test-time trajectory demonstrations, reducing the reliance on human design. Our approach incorporates a deep learning-based estimator that predicts the appropriate components of a domain for a new task and a search algorithm that refines this prediction, reducing the size and ensuring the utility of the inferred domain. Our method is able to generate new domains from minimal demonstrations at test time, enabling robots to handle complex tasks more efficiently. We demonstrate that our approach outperforms behavior cloning baselines, which directly imitate planner behavior, in terms of planning performance and generalization across a variety of tasks. Additionally, our method reduces computational costs and data amount requirements at test time for inferring new planning domains.

Andrew Coles, Erez Karpas, Andrey Lavrinenko et al.

Standard temporal planning assumes that planning takes place offline and then execution starts at time 0. Recently, situated temporal planning was introduced, where planning starts at time 0 and execution occurs after planning terminates. Situated temporal planning reflects a more realistic scenario where time passes during planning. However, in situated temporal planning a complete plan must be generated before any action is executed. In some problems with time pressure, timing is too tight to complete planning before the first action must be executed. For example, an autonomous car that has a truck backing towards it should probably move out of the way now and plan how to get to its destination later. In this paper, we propose a new problem setting: concurrent planning and execution, in which actions can be dispatched (executed) before planning terminates. Unlike previous work on planning and execution, we must handle wall clock deadlines that affect action applicability and goal achievement (as in situated planning) while also supporting dispatching actions before a complete plan has been found. We extend previous work on metareasoning for situated temporal planning to develop an algorithm for this new setting. Our empirical evaluation shows that when there is strong time pressure, our approach outperforms situated temporal planning.

Aydin Shahtakhtinskiy, Shuhab D. Khan, Sara S. Rojas

Historically, the Texas Gulf Coast has been affected by many tropical storms and hurricanes. The most recent severe impact was caused by Hurricane Harvey, which made landfall in August 2017 on the central Texas coast. We evaluated the impact of Hurricane Harvey on the barrier islands of the central Texas coast, including San Jose Island, Mustang Island, and North Padre Island. We used public data sets, including 1 m resolution bare-earth digital elevation models (DEMs), derived from airborne lidar acquisitions before (2016) and after (2018) Hurricane Harvey, and sub-meter scale aerial imagery pre- and post-Harvey to evaluate changes at a regional scale. Shoreline proxies were extracted to quantify shoreline retreat and/or advance, and DEM differencing was performed to quantify net sediment erosion and accretion or deposition. Unmanned aerial vehicle surveys were conducted at each island to produce high-resolution (cm scale) imagery and topographic data used for morphological and change analyses of beaches and dunes at the local scale. The results show that Hurricane Harvey caused drastic local shoreline retreat, reaching 59 m, and significant erosion levels of beach−dune elements immediately after its landfall. Erosion and recovery processes and their levels were influenced by the local geomorphology of the beach−foredune complexes. It is also observed that local depositional events contributed to their post-storm rebuilding. This study aims to enhance the understanding of major storm impacts on coastal areas and help in future protection planning of the Texas coast. It also has broader implications for coastlines on Earth affected by major storms.

David Pehlke

The steering of the residential development on the regional level is one of the major tasks of regional planning. Nevertheless, no nationwide information is yet available on the implementation of the planning instruments in regional plans. Moreover, for potential steering types, only one approach on the level of the German federal states exists. To reduce this information deficit, a plan content analysis is used to determine which positive planning instruments were implemented in the state development plans and regional plans valid in 2017. The data basis for negative planning instruments is the spatial development plan monitor of the Federal Office for Building and Regional Planning (BBSR). With these data, a non-linear principal component analysis and a cluster analysis is carried out to identify specific steering types. As a result, six regional planning steering types of pre-use planning, quantitative control, settlement axes, positive planning location control, intra-municipal framework with extensive mono-functional open space protection and extensive location control through multifunctional open space protection can be identified. The different steering types are often spatially clustered, so that a significant influence of state planning requirements can be assumed.

Hayyan Helal, Gerhard Lakemeyer

Many planning formalisms allow for mixing numeric with Boolean effects. However, most of these formalisms are undecidable. In this paper, we will analyze possible causes for this undecidability by studying the number of different occurrences of actions, an approach that proved useful for metric fluents before. We will start by reformulating a numeric planning problem known as restricted tasks as a search problem. We will then show how an NP-complete fragment of numeric planning can be found by using heuristics. To achieve this, we will develop the idea of multi-valued partial order plans, a least committing compact representation for (sequential and parallel) plans. Finally, we will study optimization techniques for this representation to incorporate soft preconditions.

Luis Böttcher, Christian Fröhlich, Steffen Kortmann et al.

The shift towards clean energy brings about notable transformations to the energy system. In order to optimally plan a future energy system, it is necessary to consider the influence of several sectors as well as the interaction of the transmission grid and distribution grid. The concept of Feasible Operation Region (FOR) is a detailed approach to representing the operational dependencies between the transmission and distribution grid. However, in previous planning procedures, only a simplified expansion of the distribution grids can be taken into account. With the method presented in this paper, a Feasible Planning Region (FPR) is developed, which represents the operational boundaries of the distribution grids for several expansion stages and thus represents an admissible solution space for the planning of distribution grids in systemic planning approaches. It hence enables a more detailed representation of the necessary distribution grid expansion for the integration of distributed technologies in an optimized energy system of the future. In this paper, we present the method by which the FPR is formed and its integration into an energy system planning formulation. In the results, the FPR is presented for different voltage levels, and its use in power system planning is demonstrated.

Valentin N. Hartmann, Joaquim Ortiz-Haro, Marc Toussaint

The path planning problems arising in manipulation planning and in task and motion planning settings are typically repetitive: the same manipulator moves in a space that only changes slightly. Despite this potential for reuse of information, few planners fully exploit the available information. To better enable this reuse, we decompose the collision checking into reusable, and non-reusable parts. We then treat the sequences of path planning problems in manipulation planning as a multiquery path planning problem. This allows the usage of planners that actively minimize planning effort over multiple queries, and by doing so, actively reuse previous knowledge. We implement this approach in EIRM* and effort ordered LazyPRM*, and benchmark it on multiple simulated robotic examples. Further, we show that the approach of decomposing collision checks additionally enables the reuse of the gained knowledge over multiple different instances of the same problem, i.e., in a multiquery manipulation planning scenario. The planners using the decomposed collision checking outperform the other planners in initial solution time by up to a factor of two while providing a similar solution quality.