Hasil untuk "Regional planning"

Eduardo Miranda

Milestone Driven Agile Execution is a hybrid management framework where the empirical control component of agile development is retained but the prioritization of the backlog is done according to a macro or strategic (milestone) plan that drives the execution of the project. MDAX is method agnostic, in the sense that the development approach is not embedded in the execution mechanism but in the plan that drives it. This allows organizations using it to choose the development approach that suites them most,

Wenfei Luan, Jingyao Zhu, Wensheng Wang et al.

Urban expansion and its driving factors are frequently analyzed within administrative regions to inform regional urban planning, yet such analyses often fall short at the natural basin scale (referring to the spatial extent defined by hydrological drainage boundaries) due to the scarcity of statistical data. Geographic and socio-economic spatial data can offer more detailed information across various research scales compared to traditional data (such as administrative statistical data, survey-based data, etc.), providing a potential solution to this limitation. Thus, this study took the Indus Basin as an example to reveal its urban expansion patterns and driving mechanism based on natural–economic–social time-series (2000–2020) spatial data, landscape expansion index, and geographical detector model (GDM). Future urban expansion distribution under different scenarios was also projected using Cellular Automata and Markov model (CA-Markov). The results indicated the following: (1) The Indus River Basin experienced rapid urban expansion during 2000–2020 dominated by edge-expansion, with urban expansion intensity showing a continuous increase. (2) Between 2000 and 2010 as well as 2010 and 2020, the dominant factor influencing urban expansion shifted from altitude to population (Pop), while the strongest interacting factors shifted from fine particulate matter (PM_2.5) and altitude to Gross Domestic Product (GDP) and Pop. (3) Future urban expansion probably occupies substantial mountainous area under the normal scenario, while the expansion region shifts towards the central plains to protect more ecological zones under a sustainable development scenario. Findings in this study would deepen the understanding of urban expansion characteristics of the Indus Basin and benefit its future urban planning.

Maarit Kahila-Tani, M. Kyttä, S. Geertman

Abstract While participatory urban and regional planning have become a widely accepted approach to enhance the democratic aims of community and urban development, challenges still remain. Planners lack the knowledge of usable tools to reach broader groups of participants, which can turn participation into a small-group elitist activity. Also, the quality and utilisation of the knowledge produced is problematic, the collected data remains invisible and systematic analysis is often not realized. In this article, we ask whether digitally supported PPGIS (public participation Geographical Information Systems) tools can help addressing these challenges. Through a critical analysis and reflection upon over 200 real life planning cases in Finland (62%) and other countries (38%) using PPGIS methodology we study the ability of PPGIS tools to (1) enhance effective arrangements of public participation, (2) reach a broad spectrum of people and 3) produce high quality and versatile knowledge. Our results indicate a variety of advantages and disadvantages in using PPGIS methodology in urban and regional planning practice. By categorizing the pros and cons of using PPGIS in practise, we enable planners to implement more inclusive and people-centred urban and regional planning in the future.

Qian Cao, Jielin Chen, Junchao Zhao et al.

The spatial layout of urban sites shapes land-use efficiency and spatial organization. Traditional site planning often relies on experiential judgment and single-source data, limiting systematic quantification of multifunctional layouts. We propose a Site Planning Layout Indicator (SPLI) system, a data-driven framework integrating empirical knowledge with heterogeneous multi-source data to produce structured urban spatial information. The SPLI supports multimodal spatial data systems for analytics, inference, and retrieval by combining OpenStreetMap (OSM), Points of Interest (POI), building morphology, land use, and satellite imagery. It extends conventional metrics through five dimensions: (1) Hierarchical Building Function Classification, refining empirical systems into clear hierarchies; (2) Spatial Organization, quantifying seven layout patterns (e.g., symmetrical, concentric, axial-oriented); (3) Functional Diversity, transforming qualitative assessments into measurable indicators using Functional Ratio (FR) and Simpson Index (SI); (4) Accessibility to Essential Services, integrating facility distribution and transport networks for comprehensive accessibility metrics; and (5) Land Use Intensity, using Floor Area Ratio (FAR) and Building Coverage Ratio (BCR) to assess utilization efficiency. Data gaps are addressed through deep learning, including Relational Graph Neural Networks (RGNN) and Graph Neural Networks (GNN). Experiments show the SPLI improves functional classification accuracy and provides a standardized basis for automated, data-driven urban spatial analytics.

Yi Wang, Goran Strbac

Large renewable penetration has been witnessed in power systems, resulting in reduced levels of system inertia and increasing requirements for frequency response services. There have been plenty of studies developing frequency-constrained models for power system security. However, most existing literature only considers uniform frequency security, while neglecting frequency spatial differences in different regions. To fill this gap, this paper proposes a novel planning model for the optimal sizing problem of power systems, capturing regional frequency security and inter-area frequency oscillations. Specifically, regional frequency constraints are first extracted via an enhanced input convex neural network (ICNN) and then embedded into the original optimisation for frequency security, where a principled weight initialisation strategy is adopted to deal with the gradient vanishing issues of non-negative weights in traditional ICNNs and enhance its fitting ability. An adaptive genetic algorithm with sparsity calculation and local search is developed to separate the planning model into two stages and effectively solve it iteratively. Case studies have been conducted on three different power systems to verify the effectiveness of the proposed frequency-constrained planning model in ensuring regional system security and obtaining realistic investment decisions.

Maximilian Bernecker, Smaranda Sgarciu, Xiaoming Kan et al.

The expansion of wind and solar power is driving the European energy system transformation, thereby also driving our reliance on this weather-dependent resources. Integrating renewable scarcity events into long-term planning has therefore become essential. This study demonstrates how worst-case regional renewable scarcity events - such as the Dunkelflaute, prolonged periods of low wind and solar availability - can be incorporated endogenously into the planning of a weather-robust, interconnected energy system. We develop a capacity expansion model for a fully decarbonized European electricity system using an adaptive robust optimization framework which incorporates multiple extreme weather realizations within a single optimization run. Results show that system costs rise nonlinearly with the geographic extent of these events: a single worst-case regional disruption increases costs by 9%, but broader disruptions across multiple regions lead to much sharper increases, up to 51%. As Dunkelflaute conditions extend across most of Europe, additional cost impacts level off, with a maximum increase of 71%. The optimal technology mix evolves with the severity of weather stress: while renewables, batteries, and interregional transmission are sufficient to manage localized events, large-scale disruptions require long-term hydrogen storage and load shedding to maintain system resilience. Central European regions, especially Germany and France, emerge as systemic bottlenecks, while peripheral regions bear the cost of compensatory overbuilding. These findings underscore the need for a coordinated European policy strategy that goes beyond national planning to support cross-border infrastructure investment, scale up flexible technologies such as long-duration storage, and promote a geographically balanced deployment of renewables to mitigate systemic risks associated with Dunkelflaute events.

Dongqi Liu, Xi Yu, Vera Demberg et al.

Lay summaries for scientific documents typically include explanations to help readers grasp sophisticated concepts or arguments. However, current automatic summarization methods do not explicitly model explanations, which makes it difficult to align the proportion of explanatory content with human-written summaries. In this paper, we present a plan-based approach that leverages discourse frameworks to organize summary generation and guide explanatory sentences by prompting responses to the plan. Specifically, we propose two discourse-driven planning strategies, where the plan is conditioned as part of the input or part of the output prefix, respectively. Empirical experiments on three lay summarization datasets show that our approach outperforms existing state-of-the-art methods in terms of summary quality, and it enhances model robustness, controllability, and mitigates hallucination.

Xin Guan, Yunshan Li, Zekun Wu et al.

As AI systems move into high stakes domains such as legal reasoning, medical diagnosis, and financial decision making, regulators and practitioners increasingly demand auditability. Auditability means the ability to trace exactly what each step in a multi step workflow saw and did. Current large language model based workflows are fundamentally opaque. Context pollution, defined as the accumulation of information across reasoning steps, causes models to hallucinate and lose track of constraints. At the same time, implicit data flow makes it impossible to reconstruct what any given step actually received as input. We present NormCode, a semi formal language that makes AI workflows auditable by construction. Each inference step operates in enforced data isolation and can access only explicitly passed inputs. This eliminates cross step contamination and ensures that every intermediate state can be inspected. A strict separation between semantic operations, meaning probabilistic language model reasoning, and syntactic operations, meaning deterministic data flow, allows auditors to clearly distinguish inference from mechanical restructuring. The multi format ecosystem, consisting of NCDS, NCD, NCN, and NCDN files, allows developers, domain experts, and auditors to inspect the same plan in formats suited to their individual needs. A four phase compilation pipeline transforms natural language intent into executable JSON repositories. A visual Canvas application provides real time graph visualization and breakpoint debugging. We validate the approach by achieving full accuracy on base X addition and by self hosted execution of the NormCode compiler itself. These results demonstrate that structured intermediate representations can bridge human intuition and machine rigor while maintaining full transparency.

Seyid Abdellahi Ebnou Abdem, Rida Azmi, El Bachir Diop et al.

Access to waste management services is crucial for urban sustainability, impacting public health, environmental well-being, and overall quality of life. This study employs logistic regression analysis on survey data collected from 1,032 household heads residing in Nouakchott, the capital of Mauritania. The survey investigated key household factors that determine access to waste management services. The findings reveal a significant interplay among waste service provision, the presence of cisterns, housing type and size, and access to electricity. Socioeconomic disparity in service access, with poorer housing formats like shacks receiving substandard services. In contrast, areas with robust electrification report better service access, although inconsistencies remain amid power outages. The research highlights the challenges faced by Riyadh municipality, particularly rapid growth and inadequate infrastructure, which hinder waste management efficiency. Overall, the results not only illuminate Nouakchott’s unique challenges in service provision but also propose actionable recommendations for a sustainable urban future. These recommendations aim to inform and guide targeted policies for improving living conditions and environmental sustainability in urban Mauritania.

Xuyang Zhao, Chengpu Yu, Erpei Xu et al.

Exploration systems are critical for enhancing the autonomy of robots. Due to the unpredictability of the future planning space, existing methods either adopt an inefficient greedy strategy or require a lot of resources to obtain a global solution. In this work, we address the challenge of obtaining global exploration routes with minimal computing resources. A hierarchical planning framework dynamically divides the planning space into subregions and arranges their orders to provide global guidance for exploration. Indicators that are compatible with the subregion order are used to choose specific exploration targets, thereby considering estimates of spatial structure and extending the planning space to unknown regions. Extensive simulations and field tests demonstrate the efficacy of our method in comparison to existing 2D LiDAR-based approaches. Our code has been made public for further investigation.

Anna Tanina, Alexander Orel, Olga Zaborovskaia et al.

Sustainable development has become a prominent reference point in strategic planning and territorial improvement. Economic growth necessitates intensified efforts to utilize resources, often resulting in increased pressure on the environment and heightened social inequality. The application of sustainable development principles holds particular importance for urbanized territories. Assessing the regional sustainability integral indicator can help alleviate unequal socio-economic development among municipalities. This indicator comprises indexes of the sustainability of individual territories. The authors propose the integral indicator as the arithmetic mean of indexes reflecting the sustainable development level of each component (economic, social, environmental). The authors applied this tool to municipalities in the Leningrad Region. Additionally, they conducted a ranking of municipal districts in the region based on the integral indicator. The leaders in sustainable development were the districts included in the St. Petersburg agglomeration. The authors suggest that a significant factor in the sustainable development of a territory is the presence of small enterprises, which possess the necessary flexibility for innovation in the social and environmental spheres. The authors propose distributing the elements of small and medium-sized enterprises (SME) potential according to the sustainable development factors of the region. The obtained data will enable the making of administrative decisions at the municipal and regional levels, including those related to the intensity and support for SMEs operating in relevant industries. This methodological approach to assessing the sustainability of the region and its internal municipalities, particularly concerning SMEs, can be utilized to make optimal administrative decisions related to government support for specific business areas.

Marina Filipović, Tihomir Frangen, Josip Terzić et al.

ABSTRACTOur study focuses on a sizeable transboundary karst catchment in Croatia and Bosnia and Herzegovina, extending over 2000 km2. A complex underground conduit system and extreme karst forms heterogeneity are the main characteristics of the area in question. Since determining the boundary of such a large and complex catchment is difficult, we used different kinds of data sets, of which the most relevant are the available geological data, hydrochemical data, hydrological data, and tracing tests data, to divide the regional catchment into six subcatchments. We also examined past archived reports and carried out new hydrological investigations of several major and minor springs. Our research results in a hydrogeological map that can be used as a base for establishing site-specific groundwater protection zones, for water balance calculations and the planning of new research in this area, especially the ones regulating combined cross-border efforts to prevent groundwater contamination and ensure sufficient drinking water.

Antika Fardilla, Rifta Septiavi, Ratna Juwita T et al.

Land use change is an important issue for urban and regional planners and policy makers, but it is also very useful in conservation planning, food security, and hydrological modeling. Data, information and analysis tools become obstacles in detecting changes in land use. With increasing access to data and current technology, it is hoped that land use observations can be carried out in a simple way but have more accurate results. This study aimed to analyze land cover changes in Padang City 2018-2022, using Landsat Imagery and Geographic Information System (GIS) analysis. Firstly, observations on ESRI Land Cover which displays a global map of land use or land cover (LULC) derived from ESA Sentinel-2 Imagery at a resolution of 10 m. The results showed that the area of forest cover has decreased and the built-up area has increased in the 2017-2018 and 2021-2022. Secondly, using the EO Browser, namely Sentinel-2, that was done in one to search for and compare images using high resolution at these sources, there were 19 land cover changes, such as increasing residential land use, while forest land allotment decreased.

Ali Yousefi, Saeedeh Ketabi, Iraj Abedi

There is a huge problem and time-consuming computation to optimize the IMRT treatment plan. Extracting the optimized plan from the predicted 3D3 so-called optimizing the KBP is also involved in this challenge. Some algorithms and methods have been presented for clustering and down-sampling the voxels to make the problem smaller, in recent years. In the current research, a novel down-sampling method is presented for optimizing the knowledge-based planning more efficiently. The concept of SVSIDB and corresponding down-sampling algorithm are proposed under title of SMP-2. The algorithm has been run on the data of 30 patients from the Open-KBP dataset. For each patient, there are 19 sets of dose prediction data in this dataset. Therefore, a total of 570 KBP-optimizing problems have been solved by applying the QuadLin model in the CVX framework. Resulted plans are evaluated and compared regarding two main fields which are the quality of the treatment plan as well as the computation efficiency. Solve time is the evaluation criteria for the latter field i.e. computation efficiency. The results of the current study indicated a remarkable improvement in the computation efficiency. Accordingly, the proposed method, SMP-2, reduced the average solving time by 46% in comparison to the full-data QuadLin model. The results also show an up to 53% reduction in solve time along with up to 22% improvement in clinical criteria compared to the previous research. Evaluation of the research results indicated that the SVSIDB has not only reduced the solve time but also improved the quality of the treatment plans. This is a remarkable achievement of the proposed model compared to the previous research and confirmed the significant effectiveness of the SVSIDB method which has the potential of even more improvement of the computation efficiency.

Van Dan Dang, Hoang Chung Nguyen

Purpose ─ The paper empirically explores the conditioning role of loan portfolio diversification in the monetary policy pass-through via the bank lending and risk-taking channels. Methods ─ Data of Vietnamese commercial banks during 2007–2019 is employed to perform regression using the two-step system generalized method of moments in dynamic panel models. For robustness, we approach different choices of monetary policy indicators, ranging from interest-based tools to quantitative-based policy, and consider a rich set of sectoral exposure measures to proxy loan portfolio diversification. Findings ─ Lower interest rates or greater liquidity injection during monetary expansion may increase bank lending and bank risk, thus confirming the working of the bank lending and risk-taking channels of monetary policy transmission. Notably, the potency of these banking channels may be weakened for banks diversifying loan portfolios more into various economic sectors. Implication ─ The findings call for monetary authorities to concentrate on certain types of banks, depending on their loan portfolios when setting monetary policy. When managing banking supervision, banking supervisors should also acknowledge the tradeoff between bank lending and bank risk in response to monetary shocks. Originality ─ For the first time, this paper explores the conditional role of loan portfolio composition and thus further supports the recent upsurge in empirical studies highlighting the role of business models in monetary policy pass-through.

Hrishav Bakul Barua

Approximate computing is a computation domain which can be used to trade time and energy with quality and therefore is useful in embedded systems. Energy is the prime resource in battery-driven embedded systems, like robots. Approximate computing can be used as a technique to generate approximate version of the control functionalities of a robot, enabling it to ration energy for computation at the cost of degraded quality. Usually, the programmer of the function specifies the extent of degradation that is safe for the overall safety of the system. However, in a collaborative environment, where several sub-systems co-exist and some of the functionality of each of them have been approximated, the safety of the overall system may be compromised. In this paper, we consider multiple identical robots operate in a warehouse, and the path planning function of the robot is approximated. Although the planned paths are safe for individual robots (i.e. they do not collide with the racks), we show that this leads to a collision among the robots. So, a controlled approximation needs to be carried out in such situations to harness the full power of this new paradigm if it needs to be a mainstream paradigm in future.

Tiantian Liu, Huan Li, Hua Lu et al.

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.

Clement Gehring, Masataro Asai, Rohan Chitnis et al.

Recent advances in reinforcement learning (RL) have led to a growing interest in applying RL to classical planning domains or applying classical planning methods to some complex RL domains. However, the long-horizon goal-based problems found in classical planning lead to sparse rewards for RL, making direct application inefficient. In this paper, we propose to leverage domain-independent heuristic functions commonly used in the classical planning literature to improve the sample efficiency of RL. These classical heuristics act as dense reward generators to alleviate the sparse-rewards issue and enable our RL agent to learn domain-specific value functions as residuals on these heuristics, making learning easier. Correct application of this technique requires consolidating the discounted metric used in RL and the non-discounted metric used in heuristics. We implement the value functions using Neural Logic Machines, a neural network architecture designed for grounded first-order logic inputs. We demonstrate on several classical planning domains that using classical heuristics for RL allows for good sample efficiency compared to sparse-reward RL. We further show that our learned value functions generalize to novel problem instances in the same domain.

Hongkai Ye, Tianyu Liu, Chao Xu et al.

For real-time multirotor kinodynamic motion planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages. In this paper, we address this issue by a hybrid scheme. We firstly propose a fast regional optimizer exploiting the information of local environments and then integrate it into a global sampling process to ensure faster convergence. The incorporation of local optimization on different sampling-based methods shows significantly improved success rates and less planning time in various types of challenging environments. We also present a refinement module that fully investigates the resulting trajectory of the global sampling and greatly improves its smoothness with negligible computation effort. Benchmark results illustrate that compared to the state-of-the-art ones, our proposed method can better exploit a previous trajectory. The planning methods are applied to generate trajectories for a simulated quadrotor system, and its capability is validated in real-time applications.

Natalya V. RODNINA

The article discusses issues of the state of the agro-industrial complex of the region in connection with the need to fulfill the targets approved in January 2020 by the new edition of the Food Security Doctrine of Russia. The purpose of the study is to identify the problems of the northern region and to develop proposals for changing the situation for the successful implementation of the Doctrine. The scientific problem discussed in the article relates to the determination of the methodological foundations of the most relevant areas of agrarian policy for the current state, ensuring the implementation of the Food Security Doctrine of Russia and increasing the level of self-sufficiency of the region. The author's research develops the theory of determining the socio-economic role of effective interaction between authorities of different levels, scientific justification of the prospects for the further development of the agro-industrial complex due to the transformation of the industry development management system, innovative approach to training personnel for the agricultural sector, and introduction of new technological solutions as highly relevant. It has been established that such factors as the lack of effective interaction between the state and municipal authorities, and also the lack of young qualified personnel, whose competence meets the modern requirements, have a negative impact on the economy of agricultural production and food self-sufficiency of the region as a whole. The article draws attention to the underestimated opportunities for improving the situation in the agro-industrial complex due to a change in the scheme of interaction between the state and municipal authorities during the implementation of the program-targeted method of regulation and strategic planning, as well as the creation of a scientific and educational complex in the region for the training of competitive specialists for the agro-industrial complex. In order to improve the situation, it is necessary to consolidate the formation of a regional agricultural system on the basis of a set of similar systems developed by the municipalities themselves, based on the relevant climatic conditions, financial, material and labor resources of these areas. Besides, it is necessary to start the training of personnel for the agroindustrial complex system from school, applying new educational standards, based on scientific developments.