Hasil untuk "Regional planning"

Zhenyu Gao, Courtney Davis, Alyse M. Thomas et al.

Persistent and ramping neural activity in the frontal cortex anticipates specific movements1–6. Preparatory activity is distributed across several brain regions7,8, but it is unclear which brain areas are involved and how this activity is mediated by multi-regional interactions. The cerebellum is thought to be primarily involved in the short-timescale control of movement9–12; however, roles for this structure in cognitive processes have also been proposed13–16. In humans, cerebellar damage can cause defects in planning and working memory13. Here we show that persistent representation of information in the frontal cortex during motor planning is dependent on the cerebellum. Mice performed a sensory discrimination task in which they used short-term memory to plan a future directional movement. A transient perturbation in the medial deep cerebellar nucleus (fastigial nucleus) disrupted subsequent correct responses without hampering movement execution. Preparatory activity was observed in both the frontal cortex and the cerebellar nuclei, seconds before the onset of movement. The silencing of frontal cortex activity abolished preparatory activity in the cerebellar nuclei, and fastigial activity was necessary to maintain cortical preparatory activity. Fastigial output selectively targeted the behaviourally relevant part of the frontal cortex through the thalamus, thus closing a cortico-cerebellar loop. Our results support the view that persistent neural dynamics during motor planning is maintained by neural circuits that span multiple brain regions17, and that cerebellar computations extend beyond online motor control13–15,18. The cerebellum is critical for the coding of future movement in the frontal cortex.

Fengchao Chen, Tingmin Wu, Van Nguyen et al.

Large Language Models (LLMs) have enabled agents to move beyond conversation toward end-to-end task execution and become more helpful. However, this helpfulness introduces new security risks stem less from direct interface abuse than from acting on user-provided content. Existing studies on agent security largely focus on model-internal vulnerabilities or adversarial access to agent interfaces, overlooking attacks that exploit users as unintended conduits. In this paper, we study user-mediated attacks, where benign users are tricked into relaying untrusted or attacker-controlled content to agents, and analyze how commercial LLM agents respond under such conditions. We conduct a systematic evaluation of 12 commercial agents in a sandboxed environment, covering 6 trip-planning agents and 6 web-use agents, and compare agent behavior across scenarios with no, soft, and hard user-requested safety checks. Our results show that agents are too helpful to be safe by default. Without explicit safety requests, trip-planning agents bypass safety constraints in over 92% of cases, converting unverified content into confident booking guidance. Web-use agents exhibit near-deterministic execution of risky actions, with 9 out of 17 supported tests reaching a 100% bypass rate. Even when users express soft or hard safety intent, constraint bypass remains substantial, reaching up to 54.7% and 7% for trip-planning agents, respectively. These findings reveal that the primary issue is not a lack of safety capability, but its prioritization. Agents invoke safety checks only conditionally when explicitly prompted, and otherwise default to goal-driven execution. Moreover, agents lack clear task boundaries and stopping rules, frequently over-executing workflows in ways that lead to unnecessary data disclosure and real-world harm.

Renos Zabounidis, Yue Wu, Simon Stepputtis et al.

LM-based agents excel when given high-level action APIs but struggle to ground language into low-level control. Prior work has LLMs generate skills or reward functions for RL, but these one-shot approaches lack feedback to correct specification errors. We introduce SCALAR, a bidirectional framework coupling LLM planning with RL through a learned skill library. The LLM proposes skills with preconditions and effects; RL trains policies for each skill and feeds back execution results to iteratively refine specifications, improving robustness to initial errors. Pivotal Trajectory Analysis corrects LLM priors by analyzing RL trajectories; Frontier Checkpointing optionally saves environment states at skill boundaries to improve sample efficiency. On Craftax, SCALAR achieves 88.2% diamond collection, a 1.9x improvement over the best baseline, and reaches the Gnomish Mines 9.1% of the time where prior methods fail entirely.

Joaquín Raul Rodríguez, Erik Esteban Ramírez, Mario González-Durán

Mexicali, capital of Baja California, has 1,049,792 inhabitants and lies in a high-seismic-hazard zone in northwestern Mexico, according to CENAPRED, the MDOC-CFE-2015 seismic regionalization, and the ASCE 7-22 “Hazard Toolkit”. This study develops a probabilistic seismic hazard map to estimate peak ground accelerations with a 2% probability of exceedance in 50 years, using the OpenQuake platform. The study area coincides with the 2025 urban development plan polygon for the central population area defined by the Municipal Institute for Research and Urban Planning of Mexicali. The Imperial and Cerro Prieto faults, the Pescaderos–Indiviso fault system, and the Laguna Salada fault were modeled as seismic sources. Four PEER-NGA ground motion prediction equations and regional geophysical and geotechnical data were employed to characterize shear-wave velocity (Vs30). Design response spectra were generated for each grid point for the 21 periods specified in ASCE 7-22. A representative Vs30 of 236 m/s was obtained, and the a, b, and Mc parameters were derived for the seismic catalog. Resulting peak ground accelerations range from 0.842 g to 1.221 g, with a maximum spectral pseudo-acceleration of 2.23 g at 0.30 s.

Sunandita Patra, Mehtab Pathan, Mahmoud Mahfouz et al.

Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement towards using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud and on-prem servers. The objective of this work is to perform capacity planning, i.e., estimate resource requirements, and job scheduling for on-prem grid computing environments. A key contribution of our approach is handling uncertainty in both resource usage and duration of the jobs, a critical aspect in the finance industry where stochastic market conditions significantly influence job characteristics. For capacity planning and scheduling, we simultaneously balance two conflicting objectives: (a) minimize resource usage, and (b) provide high quality-of-service to the end users by completing jobs by their requested deadlines. We propose approximate approaches using deterministic estimators and pair sampling-based constraint programming. Our best approach (pair sampling-based) achieves much lower peak resource usage compared to manual scheduling without compromising on the quality-of-service.

Matthew Lai, Keegan Go, Zhibin Li et al.

Modern robotic manufacturing requires collision-free coordination of multiple robots to complete numerous tasks in shared, obstacle-rich workspaces. Although individual tasks may be simple in isolation, automated joint task allocation, scheduling, and motion planning under spatio-temporal constraints remain computationally intractable for classical methods at real-world scales. Existing multi-arm systems deployed in the industry rely on human intuition and experience to design feasible trajectories manually in a labor-intensive process. To address this challenge, we propose a reinforcement learning (RL) framework to achieve automated task and motion planning, tested in an obstacle-rich environment with eight robots performing 40 reaching tasks in a shared workspace, where any robot can perform any task in any order. Our approach builds on a graph neural network (GNN) policy trained via RL on procedurally-generated environments with diverse obstacle layouts, robot configurations, and task distributions. It employs a graph representation of scenes and a graph policy neural network trained through reinforcement learning to generate trajectories of multiple robots, jointly solving the sub-problems of task allocation, scheduling, and motion planning. Trained on large randomly generated task sets in simulation, our policy generalizes zero-shot to unseen settings with varying robot placements, obstacle geometries, and task poses. We further demonstrate that the high-speed capability of our solution enables its use in workcell layout optimization, improving solution times. The speed and scalability of our planner also open the door to new capabilities such as fault-tolerant planning and online perception-based re-planning, where rapid adaptation to dynamic task sets is required.

Leonardo Rosa Amado, Adriano Vogel, Dalvan Griebler et al.

Data pipeline frameworks provide abstractions for implementing sequences of data-intensive transformation operators, automating the deployment and execution of such transformations in a cluster. Deploying a data pipeline, however, requires computing resources to be allocated in a data center, ideally minimizing the overhead for communicating data and executing operators in the pipeline while considering each operator's execution requirements. In this paper, we model the problem of optimal data pipeline deployment as planning with action costs, where we propose heuristics aiming to minimize total execution time. Experimental results indicate that the heuristics can outperform the baseline deployment and that a heuristic based on connections outperforms other strategies.

Haifeng Yang, Guofang Zhai, Yifu Ge et al.

Spatial planning, recognized as a systematic policy instrument for regional development and governance, plays a crucial role in achieving carbon peak and carbon neutrality. This study establishes a framework for carbon sources/sinks estimation and carbon compensation optimization and conducts empirical research in a representative coal resource-based city. We analyzed the spatial–temporal distribution characteristics of net carbon emissions in Huaibei from 2006 to 2020 using a spatial correlation model and an improved Carnegie–Ames–Stanford approach (CASA). Then, we applied the normalized revealed comparative advantage (NRCA) index and the SOM-K-means clustering model to categorize the carbon pattern into payment, balance, and compensation areas. These areas were further integrated with the “Three-zones and Three-lines” to reclassify nine spatial partition optimization types. Finally, we proposed a targeted emission reduction and sink enhancement optimization scheme. We found that urban carbon emissions and carbon sinks exhibit a significant mismatch, with the net carbon emission intensity reaching 166.76–383.27 t·hm⁻² from 2006 to 2020, showing a rapid increase followed by stabilization. The high-value area, centered in Xiangshan District, exhibits a circularly decreasing spatial characteristic, gradually extending to the central city of Suixi County. In the optimized payment area, the level of the carbon emission contributive coefficient surpasses the ecological support coefficient (3.92 < ECC < 6.04, 2.09 < ESC < 3.58). The optimized space in the balance area type is primarily situated in mining subsidence areas, leading to a lower overall level (0.42 < ECC < 0.57, 0.49 < ESC < 1.13). The optimized space in the compensation area type (2.24 < ECC < 3.25, 4.59 < ESC < 5.69) requires economic or non-economic compensation from the payment area. The study combines the “Three-zones and Three-lines” with the results of carbon compensation to formulate an urban emission reduction and sink enhancement program, which not only helps to consolidate the theory of low-carbon cities but also effectively promotes the realization of the regional carbon peak goal.

Yapeng Duan, Mingxia Chen, Yue Liu et al.

Traditional villages face many difficulties in the era of globalization, especially in light of fast industrialization and urbanization. The breakdown of settlement patterns and the erosion of local characteristics and cultural identities pose critical issues for the sustainable development of these communities. While research on traditional villages and dwellings in core cultural areas is relatively advanced, there remains a significant gap in studies focusing on traditional villages and dwellings in multicultural intermingling regions. By clarifying the characteristics of traditional villages and the cultural landscapes of dwellings under the influence of multiple cultures, as well as their differentiation and underlying mechanisms, this research aims to provide theoretical support for the protective planning of world cultural heritage, which is increasingly characterized by clustering and regionalization. Taking the traditional villages and dwellings in the Jiangxi and Anhui junction area as a case study, we developed a cultural landscape factor system for traditional villages and dwellings across four dimensions: natural environment, spatial configuration, dwelling typology, and historical and cultural context. Using geographic information systems (GIS) zoning methods and statistical spatial analysis, we divided the area into three distinct cultural landscape zones. The findings indicate that the cultural landscapes within each zone exhibit unique regional characteristics at both the village and dwelling levels, particularly in site selection, settlement patterns, and architectural aesthetics. Differentiation across zones is shaped by natural factors, such as topography and water systems, as well as by regional culture, historical migration, the chronological sequence of regional development, commerce and trade growth, and the evolution of administrative systems, alongside broader cultural, economic, and social factors, showing consistent patterns. This study demonstrates that utilizing a scientific and objective zoning approach to accurately identify the cultural landscape characteristics and differentiation patterns across various cultural zones, while clarifying the historical evolution of villages and the transformation of dwelling forms, provides practical insights for cultural landscape zoning in other multicultural regions. Furthermore, it provides scientific guidance to advance China’s rural revitalization strategy and supports the regional protection and sustainable development of world cultural heritage.

Alok Tiwari

Urban systems are transforming as artificial intelligence (AI) evolves from automation to Agentic Urban AI (AI systems with autonomous goal-setting and decision-making capabilities), which independently define and pursue urban objectives. This shift necessitates reassessing governance, planning, and ethics. Using a conceptual-methodological approach, this study integrates urban studies, AI ethics, and governance theory. Through a literature review and case studies of platforms like Alibaba’s City Brain and CityMind AI Agent, it identifies early agency indicators, such as strategic adaptation and goal re-prioritisation. A typology distinguishing automation, autonomy, and agency clarifies AI-driven urban decision-making. Three trajectories are proposed: fully autonomous Agentic AI, collaborative Hybrid Urban Agency, and constrained Non-Agentic AI to mitigate ethical risks. The findings highlight the need for participatory, transparent governance to ensure democratic accountability and social equity in cognitive urban ecosystems.

Miguel Chicchon, Francisco James Leon Trujillo, Ivan Sipiran et al.

An accurate land-cover segmentation of very-high-resolution aerial images is essential for a wide range of applications, including urban planning and natural resource management. However, the automation of this process remains a challenge owing to the complexity of images, variability in land surface features, and noise. In this study, a method for training convolutional neural networks and transformers to perform land-cover segmentation on very-high-resolution aerial images in a regional context was proposed. We assessed the U-Net-scSE, FT-U-NetFormer, and DC-Swin architectures, incorporating transfer learning and active contour loss functions to improve performance on semantic segmentation tasks. Our experiments conducted using the OpenEarthMap dataset, which includes images from 44 countries, demonstrate the superior performance of U-Net-scSE models with the EfficientNet-V2-XL and MiT-B4 encoders, achieving an mIoU of over 0.80 on a test dataset of urban and rural images from Peru.

Rushang Karia, Pulkit Verma, Alberto Speranzon et al.

This paper introduces a new approach for continual planning and model learning in relational, non-stationary stochastic environments. Such capabilities are essential for the deployment of sequential decision-making systems in the uncertain and constantly evolving real world. Working in such practical settings with unknown (and non-stationary) transition systems and changing tasks, the proposed framework models gaps in the agent's current state of knowledge and uses them to conduct focused, investigative explorations. Data collected using these explorations is used for learning generalizable probabilistic models for solving the current task despite continual changes in the environment dynamics. Empirical evaluations on several non-stationary benchmark domains show that this approach significantly outperforms planning and RL baselines in terms of sample complexity. Theoretical results show that the system exhibits desirable convergence properties when stationarity holds.

Carlos Núñez-Molina, Juan Fernández-Olivares, Raúl Pérez

In this work we propose a planning and acting architecture endowed with a module which learns to select subgoals with Deep Q-Learning. This allows us to decrease the load of a planner when faced with scenarios with real-time restrictions. We have trained this architecture on a video game environment used as a standard test-bed for intelligent systems applications, testing it on different levels of the same game to evaluate its generalization abilities. We have measured the performance of our approach as more training data is made available, as well as compared it with both a state-of-the-art, classical planner and the standard Deep Q-Learning algorithm. The results obtained show our model performs better than the alternative methods considered, when both plan quality (plan length) and time requirements are taken into account. On the one hand, it is more sample-efficient than standard Deep Q-Learning, and it is able to generalize better across levels. On the other hand, it reduces problem-solving time when compared with a state-of-the-art automated planner, at the expense of obtaining plans with only 9% more actions.

Fariz Fadhlillah

This study was conducted in Bandung, the provincial capital of West Java, to reevaluate the tactile paving installation system based on the movement patterns of visually impaired pedestrians in urban networks. Given the rising global prevalence of low vision and blindness, creating inclusive urban environments has become a critical health and social issue. The research aimed to address the gap in accessibility by focusing on how tactile paving can better align with the daily rhythms and needs of visually impaired individuals. Data collection involved interviews, and data analysis utilized a mixed-methods approach. Findings revealed that the movement patterns of visually impaired pedestrians are intricately connected to their essential activities. The study concludes that a hierarchical approach to tactile paving installation can improve efficiency, particularly in cities with limited funding, thus promoting broader and more effective development of inclusive urban networks. These insights are valuable for both immediate facility improvements and future transit-oriented development planning.

Gultekin Hasanaliyeva, Ourania Giannakopoulou, Juan Wang et al.

Modern potato varieties from high-input, conventional farming-focused breeding programs produce substantially (up to 45%) lower yields when grown in organic production systems, and this was shown to be primarily due to less efficient fertilization and late blight (<i>Phytophthora infestans</i>) control methods being used in organic farming. It has been hypothesized that the breeding of potato varieties suitable for the organic/low-input sector should (i) focus on increasing nutrient (especially N) use efficiency, (ii) introduce durable late blight resistance, and (iii) be based on selection under low-input conditions. To test this hypothesis, we used an existing long-term factorial field experiment (the NEFG trials) to assess the effect of crop management practices (rotation design, fertilization regime, and crop protection methods) used in conventional and organic farming systems on crop health, tuber yield, and mineral composition parameters in two potato varieties, Santé and Sarpo mira, that were developed in breeding programs for high and low-input farming systems, respectively. Results showed that, compared to Santé, the variety Sarpo mira was more resistant to foliar and tuber blight but more susceptible to potato scab (<i>Streptomyces scabies</i>) and produced higher yields and tubers with higher concentrations of nutritionally desirable mineral nutrients but lower concentrations of Cd. The study also found that, compared to the Cu-fungicides permitted for late blight control in organic production, application of synthetic chemical fungicides permitted and widely used in conventional production resulted in significantly lower late blight severity in Sante but not in Sarpo mira. Results from both ANOVA and redundancy analysis (RDA) indicate that the effects of climatic (precipitation, radiation, and temperature) and agronomic (fertilization and crop protection) explanatory variables on crop health and yield differed considerably between the two varieties. Specifically, the RDA identified crop protection as a significant driver for Santé but not Sarpo mira, while precipitation was the strongest driver for crop health and yield for Sarpo mira but not Santé. In contrast, the effect of climatic and agronomic drivers on tuber mineral and toxic metal concentrations in the two varieties was found to be similar. Our results support the hypothesis that selection of potato varieties under low agrochemical input conditions can deliver varieties that combine (i) late blight resistance/tolerance, (ii) nutrient use efficiency, and (iii) yield potential in organic farming systems.

Halyna Kryshtal , Viktoriia Tomakh , Tetiana Ivanova et al.

The study is aimed at summarizing the processes of eco-innovative (green) transformation of urban infrastructure and researching possible prospects for the development of Ukraine in this context. In the course of the research, the possibilities of "green" transformation of urban infrastructure were considered and it was noted that the use of the principles of eco-innovative transformation in the post-war period can only take place under the condition of proper planning, state support and the creation of favourable market conditions. The authors noted that the success of such a transformation requires the establishment of green goals in all aspects of the development of Ukrainian cities. Auto-frame considered the financial possibilities of the development of urban infrastructure and proposed the location of support offices for the eco-innovative transformation of urban infrastructure at the regional level. The principles of achieving eco-innovative transformation of urban infrastructure are revealed, namely maximum energy efficiency, energy transition, "zero waste", environmental sustainability of buildings, adaptation to climate change, popularization of a green lifestyle, resource conservation, citizen involvement and circular economy. It is proposed to create a platform that would unite architects, builders, urban planners, citizens, artists and other interested persons. This platform should contribute to the search for answers to the question of how to ensure a quick, ecological, attractive and safe "green" transformation of urban infrastructure. Ukraine should cooperate with the European Union within various green platforms and networks that help cities in green transformation. All the above-mentioned tools and solutions should contribute to the creation of green, sustainable and people-oriented cities in Ukraine. The authors have considered the possibilities of financing the restoration of Ukrainian cities after the destruction in terms of the necessary financial resources, donor countries, and reconstruction expenditures.

Marcin Spyra, Nica Claudia Caló, Guillermo J. Martínez Pastur et al.

Trade-offs in ecosystem services (ESs) manifest when the enhancement of one service leads to the diminishment of another. These trade-offs pose a notable challenge, impacting the sustainability of particular socioecological system peri-urban landscapes (PULs). This issue arises from the dynamic processes associated with peri-urbanization, which threaten natural ecosystems and their services in peri-urban areas. Additionally, the escalating demand for ecosystem services in PULs contributes to these trade-offs. Policymaking and planning concerning ES trade-offs in PULs should prioritize promoting a balance between conflicting services and fostering synergies among them. However, it is noteworthy that ES trade-offs in PULs are not given high priority in policy and planning agendas. Knowledge regarding policy development and planning for ES trade-offs in PULs often remains concealed within specific country and regional case studies. Consequently, this research seeks to characterize the ES trade-offs in selected PUL case studies, with the objective of identifying potential commonalities among them. Furthermore, this study aims to identify (i) the factors driving ES trade-offs, (ii) challenges related to how policymaking and planning address ES trade-offs in PULs, and (iii) recommendations for enhancing governance practices to better manage peri-urban ES trade-offs. We designed a semi-quantitative survey and collected information about 24 case studies located across the world. The answers from this survey were analyzed using principal component analysis. The results showed that the most common trade-offs occurred between “cultural and provisioning” and “regulating and provisioning” ESs. It was found that urban development is the primary driver behind the emergence of the examined trade-offs. To address this issue at the governance level, this study recommends establishing mechanisms to facilitate collaboration among stakeholders. This should be accompanied by robust dissemination efforts and the promotion of awareness among actors regarding the fundamental concepts of ESs and PULs.

Viani Naufalia

The objective of this research is to find out how the determinants of market conduct monitoring contribute to fostering customer trust and security, supported by the mediating variable customer satisfaction index. The researcher used quantitative research methods from customers of financial service products in DKI Jakarta, then data analysis techniques used SMART PLS 4.0 application and CSI score calculations. The results of this research show that market conduct monitoring has a significant positive determination in increasing customer confidence by 50.2% and customer sense of security by 25.2%, and can be mediated by a customer satisfaction index of 31.4%.

Dhruv Saxena, Maxim Likhachev

We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide, tilt, lean or topple. A recently proposed algorithm, M4M, determines which objects need to be moved and where by solving a Multi-Agent Pathfinding MAPF abstraction of this problem. It then utilises a nonprehensile push planner to compute actions for how the robot might realise these rearrangements and a rigid body physics simulator to check whether the actions satisfy physics constraints encoded in the problem. However, M4M greedily commits to valid pushes found during planning, and does not reason about orderings over pushes if multiple objects need to be rearranged. Furthermore, M4M does not reason about other possible MAPF solutions that lead to different rearrangements and pushes. In this paper, we extend M4M and present Enhanced-M4M (E-M4M) -- a systematic graph search-based solver that searches over orderings of pushes for movable objects that need to be rearranged and different possible rearrangements of the scene. We introduce several algorithmic optimisations to circumvent the increased computational complexity, discuss the space of problems solvable by E-M4M and show that experimentally, both on the real robot and in simulation, it significantly outperforms the original M4M algorithm, as well as other state-of-the-art alternatives when dealing with complex scenes.

Anna Jacobson, Filippo Pecci, Nestor Sepulveda et al.

Energy systems planning models identify least-cost strategies for expansion and operation of energy systems and provide decision support for investment, planning, regulation, and policy. Most are formulated as linear programming (LP) or mixed integer linear programming (MILP) problems. Despite the relative efficiency and maturity of LP and MILP solvers, large scale problems are often intractable without abstractions that impact quality of results and generalizability of findings. We consider a macro-energy systems planning problem with detailed operations and policy constraints and formulate a computationally efficient Benders decomposition separating investments from operations and decoupling operational timesteps using budgeting variables in the master model. This novel approach enables parallelization of operational subproblems and permits modeling of relevant constraints coupling decisions across time periods (e.g. policy constraints) within a decomposed framework. Runtime scales linearly with temporal resolution; tests demonstrate substantial runtime improvement for all MILP formulations and for some LP formulations depending on problem size relative to analagous monolithic models solved with state-of-the-art commercial solvers. Our algorithm is applicable to planning problems in other domains (e.g. water, transportation networks, production processes) and can solve large-scale problems otherwise intractable. We show that the increased resolution enabled by this algorithm mitigates structural uncertainty, improving recommendation accuracy.