Hasil untuk "Regional planning"

Oluwatosin Oseni, Shengjie Wang, Jun Zhu et al.

Reinforcement Learning (RL) has shown remarkable success in real-world applications, particularly in robotics control. However, RL adoption remains limited due to insufficient safety guarantees. We introduce Nightmare Dreamer, a model-based Safe RL algorithm that addresses safety concerns by leveraging a learned world model to predict potential safety violations and plan actions accordingly. Nightmare Dreamer achieves nearly zero safety violations while maximizing rewards. Nightmare Dreamer outperforms model-free baselines on Safety Gymnasium tasks using only image observations, achieving nearly a 20x improvement in efficiency.

Eduardo Miranda

This paper explains the Visual Milestone Planning (VMP) method using an agile vocabulary to facilitate its adoption by agile practitioners as a front end for a hybrid development process. VMP is a visual and collaborative planning approach which promotes a shared understanding of the work approach and commitment through the direct manipulation by team members of the reified planning constructs involved in the development of the plan. Once the product backlog has been established and relevant milestones identified, a novel construct called the milestone planning matrix is used to document the allocation of product backlog items to milestones. The milestones due dates are later determined by grouping sticky notes representing the work to be performed into time-boxes called work packages and accommodating them on a resource and time scaled scheduling canvas very much as it would be done in a Tetris game.

Abdullah Addas, Abdullah Addas, Muhammad Nasir Khan et al.

IntroductionThe regional disparity in higher education access can only be met when there are strategies for sustainable development and diversification of the economy, as envisioned in Saudi Vision 2030. Currently, 70% of universities are concentrated in the Central and Eastern regions, leaving the Northern and Southern parts of the country with limited opportunities.MethodsThe study created a framework with sensors and generative adversarial networks (GANs) that optimize the distribution of medical universities, supporting equity in access to education and balanced regional development. The research applies an artificial intelligence (AI)-driven framework that combines sensor data with GAN-based models to perform real-time geographic and demographic data analyses on the placement of higher education institutions throughout Saudi Arabia. This framework analyzes multisensory data by examining strategic university placement impacts on regional economies, social mobility, and the environment. Scenario modeling was used to simulate potential outcomes due to changes in university distribution.ResultsThe findings indicated that areas with a higher density of universities experience up to 20% more job opportunities and a higher GDP growth of up to 15%. The GAN-based simulations reveal that redistributive educational institutions in underrepresented regions could decrease environmental impacts by about 30% and enhance access. More specifically, strategic placement in underserved areas is associated with a reduction of approximately 10% in unemployment.DiscussionThe research accentuates the need to include AI and sensor technology to develop educational infrastructures. The proposed framework can be used for continuous monitoring and dynamic adaptation of university strategies to align them with evolving economic and environmental objectives. The study explains the transformative potential of AI-enabled solutions to further equal access to education for sustainable regional development throughout Saudi Arabia.

Ming Ma, Yichen Zhang, Jiquan Zhang et al.

Abstract Earthquakes, as one of the common natural phenomena in China, can directly lead to the collapse of buildings and trigger secondary effects such as landslides and tsunamis, often resulting in significant property losses and casualties. Therefore, conducting seismic risk assessments for regions holds great practical significance. Current disaster research typically requires a comprehensive consideration of the probabilities of disaster-causing factors, the instability of disaster-prone environments, and the vulnerability of disaster-bearing entities. In low seismic risk areas, studying the vulnerability of these entities is particularly important. However, most current research on social vulnerability (SV) in the context of earthquake disasters employs a single weighting method, which is largely constrained by data types and makes it challenging to fully reflect the characteristics of social vulnerability. To address this issue, this study focuses on Changchun City and constructs a comprehensive index system consisting of 12 indicators from the perspectives of exposure, sensitivity, and coping capacity. The TOPSIS model and game theory-based combined weighting method have been utilized to calculate the Social Vulnerability Index (SoVI). In this framework, subjective weights were determined using the G2 method, while objective weights were computed by integrating the CRITIC method and the entropy weighting method, thus forming a more scientifically reasonable weight distribution approach. The results of various weighting combinations demonstrate that the model can effectively express social vulnerability and further elucidate the influence of each indicator on regional vulnerability. The findings indicate that Chaoyang District and Yushu City exhibit high levels of social vulnerability. This paper conducts an in-depth analysis of the reasons behind this phenomenon and proposes corresponding policy recommendations, providing specific guidance for mitigating social vulnerability in these areas. This research is fundamentally significant for promoting the implementation of earthquake disaster prevention and reduction planning in Changchun City and fostering sustainable regional development.

Xuan Zeng, Jibo Wang, Ning Liu et al.

ImportanceColorectal cancer (CRC) constitutes a significant segment of the global cancer burden, thereby warranting an in-depth epidemiological appraisal to inform strategic public health interventions and resource allocation. Previous studies, such as those based on the GBD 2019 dataset, have provided valuable insights into the CRC burden. However, they have limitations in terms of data recency, regional granularity, and comprehensive risk factor analysis.ObjectiveThis research seeks to undertake a thorough analysis of the burden of CRC at global, regional, and national levels, along with its associated risk factors, spanning the period from 1990 to 2021. This analysis will employ data sourced from the Global Burden of Disease (GBD) 2021 study, addressing limitations in previous research by providing a more detailed and updated assessment.MethodsWe assessed the distribution of CRC across 204 countries and territories, focusing on age, gender, and geographic variations. The impact of key risk factors (including behavioral risks, metabolic risks, behavioral risks, metabolic risks) on disability-adjusted life years (DALYs) was evaluated across 21 GBD regions. A Bayesian age-period-cohort (BAPC) model was employed to project CRC trends over the next three decades.FindingsIn 2021, global CRC incidence was approximately 2,194,143 cases, with a prevalence of 11,679,120 and 24,401,100 DALYs. Central Europe exhibited the highest burden, with incidence peaking among individuals aged 84 to 94 years. From 1990 to 2021, age-standardized incidence, mortality, and DALY rates for CRC showed upward trends, particularly among males. The analysis of risk factors across 21 GBD regions reveals significant regional disparities in the colorectal cancer (CRC) burden, with Central Europe showing the highest contribution from risk factors (305.66). Behavioral risks, such as smoking and high alcohol use, have the greatest impact, followed by dietary risks (particularly low whole grain intake and high processed meat consumption) and metabolic risks (including high BMI and high fasting plasma glucose). By 2051, the global ASIR, ASMR, and ASDR of CRC are projected to reach 18.21 (95% UI: 10.83–25.59), 7.10 (95% UI: 4.36–9.84), and 165.21 (95% UI: 102.48–227.93) per 100,000 population, respectively, with the burden remaining higher in males than in females.ConclusionThis study provides the most granular assessment of CRC burden to date, highlighting dietary policies and sex-specific interventions as priorities. Methodological advancements in projection modeling offer actionable insights for long-term public health planning.

Alfina Modiash, Nany Yuliastuti

Program kampung tematik merupakan inovasi pemerintah yang menonjolkan potensi lokal kampung bertujuan mengangkat kearifan lokal, meningkatkan kesejahteraan masyarakat dan kualitas lingkungan permukiman.  Kampung Blangkon Potrojayan merupakan salah satu kampung tematik yang berpotensi dalam peningkatan kesejahteraan masyarakat serta mendukung kearifan lokal menjadi sebuah ikonik di Kota Surakarta berupa produk budaya jawa yaitu blangkon. Namun, masih terdapat permasalahan terkait dukungan infrastruktur kawasan maupun kualitas pengelolaan kampungnya. Salah satunya berupa kegiatan promosi yang masih bersifat konvensional serta jaringan jalan yang dipergunakan sebagai tempat menjemur blangkon. Penelitian ini bertujuan untuk mengetahui tindakan pengembangan kampung tematik yang berkelanjutan di Kampung Blangkon Potrojayan. Dengan menggunakan metode kuantitatif berupa analisis skoring untuk mengetahui kondisi eksisting dan tingkat keberlanjutan kampung serta metode Importance Performance Analysis (IPA) untuk mengetahui tingkatan prioritas tindakan yang dapat menjadi usulan perbaikan. Hasil penelitian didapatkan Kampung Blangkon Potrojayan berada pada tingkat cukup berkelanjutan dengan skor 2,21 dimana terdapat 4 atribut yang menjadi prioritas utama dilakukan pengembangan yaitu kondisi jaringan jalan, jenis media promosi, pekerja yang kompeten, keikutsertaan kegiatan pameran. Prioritas pertama tindakan yang perlu dilakukan dalam mengembangkan Kampung Blangkon Potrojayan berupa perlu ditetapkannya lahan khusus penjemuran blangkon komunal yang terdapat di kawasan rencana kampung wisata dalam rangka meningkatkan kenyamanan pergerakan wisatawan dan warga lokal.

Maria S. Frolova

In 2021-2023 In Yekaterinburg, 3 volumes of author's essays were published on the development of the cultural sphere of the capital of the Urals. The release of review texts was initiated by the Department of Culture of the Yekaterinburg Administration. On 864 pages, using archival materials, unique historical and contemporary photographs, the “spirit of the development of the arts” is presented - music, theater and cinema in Volume 1, sculpture, painting and architecture in Volume 2, literature, art education and the educational system in Volume 3. The chosen genre - essays - is original and productive. Texts are a form of summing up, recording successes in the development of the Yekaterinburg/Sverdlovsk sphere of culture. The tercentenary anniversary of Yekaterinburg (the city can be scientifically categorized as a regional or peripheral capital), which took place in 2023, is an occasion for reflection and further planning. Richly illustrated, gift-type books are deep and original from the point of view of analytics of the development of the cultural sphere. The authors were leading academic researchers and employees of the largest cultural institutions of Yekaterinburg - the Sverdlovsk Regional Museum of Local Lore, UrFU named after the first President of Russia B. N. Yeltsin, the Museum of the History of Yekaterinburg, the Sverdlovsk Music School named after P. I. Tchaikovsky. Using the general scientific critical method, methods of synthesis and analysis, the text of the review provides a brief overview of all three volumes of essays, characterizes the merits of the publication, and provides criticism.

Hélène Rey Valette, Alexandre Richard, Laura Michel et al.

The increase in coastal risks associated with rising sea levels calls for dynamic adaptation measures to reorganise coastal territory. The challenges of appropriating measures and putting them into practice are major. They are multi-level and at the crossroads of different policies: land use planning, tourism, risk management and so on. Faced with these challenges, this article reports on a co-construction process towards a policy of spatial integrated managed retreat in the context of the Occitanie region and its governance. In practice, this resulted in the co-construction of a shared regional framework, building a community of practice and adapting territorial governance arrangements. This article offers a reflective analysis of this co-construction. Firstly, the process (30 months in total) is transcribed by means of a chronicle, detailing the diversity and complementarity of methods and approaches that have been used. The main achievements (common definition, principles of action, scale of intervention, temporal typology of actions) and the choice of a governance framework are then presented and discussed on how they could contribute to gradually build a community of practice as well as the types of constraints encountered. The contributions towards the community of practice are analysed in terms of knowledge sharing, pooling and collective learning. This reflective analysis provides lessons for local stakeholders and, more broadly, for the transition of coastal territory.

Zhongyi Ding, Yuxin Wang, Liang Ma et al.

This research provides an overview of the trade-offs and synergies among ecosystem services (ESs) within the upper Fen River Basin (uFRB) that are crucial for informed land management and regional ecological protection. We utilized methodologies, including the dynamic equivalent factor method and spatial autocorrelation analysis, to track ES and driving factors from 1990 to 2020. This study revealed a 13.27% increase in overall ES value, with notable growth in forest land and water areas. Initially, synergies were dominant, but trade-offs became evident over time, particularly with food production. This study identified road proximity and the Normalized Difference Vegetation Index (NDVI) as primary drivers of ES values, with their impact evolving annually. The analysis also highlighted the importance of considering the temporal dynamics in ES relationships and the influence of driving factors on these services. We propose incorporating socio-ecological factors and ES bundles into spatial planning. This is crucial as it will allow us to optimize multi-ES objectives, thus balancing trade-offs and enhancing synergies for sustainable land use.

Emanuele Calò, Angelo Facchini

In Regional Economics, the attractiveness of regions for capital, migrants, tourists, and other kinds of flows is a relevant topic. Usually, studies in this field explore single flows, characterizing the dimensions of territorial attractiveness separately, rarely considering the interwoven effect of flows. Here, we investigate attractiveness from a multi-dimensional perspective (i.e., dealing with different flows), asking how various types of regional flows collectively shape the attractiveness dynamics of European regions. We analyze eight distinct flow types across NUTS2 regions from 2010 to 2018, employing a multilayer network approach. Notably, the multilayer approach unveils insights that would be missed in single-layer analyses. Community detection reveals complex structures that demonstrate the cohesive power of national borders and the existence of strong cross-border ties in specific regions. Our study contributes to a more nuanced understanding of regional attractiveness, with implications for targeted policy interventions in regional development and European cohesion.

Hussein Ali Jaafar, Cheng-Hao Kao, Sajad Saeedi

With the recent influx in demand for multi-robot systems throughout industry and academia, there is an increasing need for faster, robust, and generalizable path planning algorithms. Similarly, given the inherent connection between control algorithms and multi-robot path planners, there is in turn an increased demand for fast, efficient, and robust controllers. We propose a scalable joint path planning and control algorithm for multi-robot systems with constrained behaviours based on factor graph optimization. We demonstrate our algorithm on a series of hardware and simulated experiments. Our algorithm is consistently able to recover from disturbances and avoid obstacles while outperforming state-of-the-art methods in optimization time, path deviation, and inter-robot errors. See the code and supplementary video for experiments.

أ.م. هدى خالد شعبان

      The regions are the product of regional studies that researchers exchange in their studies in the natural and human sciences of geography, where the applications of the regions have extended in many sciences, such as sociology, economics, soil, planning, soil, plant environmental sciences until a special science of the region emerged called (regional science). The idea of division entered the field of social planning such as planning and development regions with the aim of achieving regional justice between cities. The applications of the regions varied among researchers according to the goal and need for them. They were distinguished by the stability of the natural regions and the mobility of the human regions. To achieve this, the research relied on the descriptive and analytical methods as well as the desk survey.   Received 6/10/2023 , Accepted 26/11/2023  , Published 31/12/2023

Solomon Sisay Mulugeta MD, Mitiku Wale Muluneh MD, Alebachew Taye Belay MD et al.

Introduction Contraception has a clear impact on the health of women and families in developing countries. This study aims to identify multilevel determinants of nonuse of modern contraceptives among Ethiopian rural married women in their productive age group. Method The study relied on data from the 2016 Ethiopian Demographic and Health Surveys. A multilevel logistic regression model was used for analysis. Result In rural areas, nonuse of modern contraceptives is surprising high (81.7%), primarily due to fear of side effects (12.89%) and breastfeeding (8.2%). Among women aged 35 to 49 years (adjusted odds ratio [AOR] = 0.66; 95% confidence interval [CI]: 0.540.81), husbands with secondary and above education levels (AOR = 0.83; 95% CI: 0.7–1), those in the high wealth index (AOR = 0.61; 95% CI: 0.51–0.72), and those who have had 1 to 2 children in the past 5 years (AOR  =  0.28; 95% CI: 0.24–0.33), there was a lower chance of not using contraception. Muslims are less likely to want to use modern contraceptives (AOR = 1.2; 95% CI: 0.96–1.4). Women living in Afar (AOR  =  20.9; 95% CI: 9.6–44.7), Oromia (AOR =  1.5; 95% CI: 1.01–2.3), Somali (AOR = 71.1; 95% CI: 24.1–209.2), Gambela (AOR = 2.3; 95% CI: 1.4–3.9), Harari (AOR  =  4.4; 95% CI: 2.24–8.72), and Dire Dawa (AOR = 3.2; 95% CI: 1.5–6.9), regional states, were less likely to want to use modern contraceptives as compared to those in Tigray. Conclusion Family planning interventions should target younger women, women living in rural areas, the poor, and Muslim women. In order to maximize the effectiveness of family planning promotion policies, it's important to address the reasons for nonuse of contraceptives identified in each region and contextual differences regarding women of reproductive age.

Katherine Burley Farr, Kaihui Song, Zhi Yi Yeo et al.

Abstract Although the potential for cities and regions to contribute to global mitigation efforts is widely acknowledged, there is little evidence on the effectiveness of subnational mitigation strategies. Here we address this gap through a systematic review of 234 quantitative mitigation case studies. We use a meta-analytical approach to estimate expected greenhouse gas emissions reductions from 12 categories of mitigation strategies. We find that strategies related to land use and development, circular economy, and waste management are most effective and reliable for reducing emissions. The results demonstrate that cities and regions are taking widespread action to reduce emissions. However, we find misalignment between the strategies that policymakers and researchers focus on, compared to those with the highest expected impacts. The results inform climate action planning at the city and regional level and the evaluation of subnational climate targets.

Roberta Cucca, Thomas Thaler

The Covid-19 pandemic and energy, climate, and demographic crises have shown how cities are vulnerable to these impacts and how the access to green and blue spaces has become highly relevant to people. One strategy that we can observe is the strong focus on the resilience discourse, meaning implementing more green and blue spaces in urban areas, such as at previous brownfield quarters. However, social justice implications of urban greening have been overlooked for a long time. The implementation of strategies to improve the quality and availability of the green and blue infrastructures may indeed have negative outcomes as far as housing accessibility is concerned by trigging gentrification processes. Issues related to environmental justice and socio-spatial justice are increasing in contemporary cities and call for a better understanding of the global and local mechanisms of production and reproduction of environmental and spatial inequalities. This thematic issue includes eleven articles with different methodologies, with examples from Europe and North America as well as different lenses of green gentrification. Some articles focus more on the question of costs, benefits, and distributional consequences of various infrastructural options for urban greening. Others, instead, discuss how the strategic urban planning tools and policy processes take into account distributional consequences, with specific attention on participatory processes.

Constantinos Chamzas, Caelan Garrett, Balakumar Sundaralingam et al.

Recent work has shown that complex manipulation skills, such as pushing or pouring, can be learned through state-of-the-art learning based techniques, such as Reinforcement Learning (RL). However, these methods often have high sample-complexity, are susceptible to domain changes, and produce unsafe motions that a robot should not perform. On the other hand, purely geometric model-based planning can produce complex behaviors that satisfy all the geometric constraints of the robot but might not be dynamically feasible for a given environment. In this work, we leverage a geometric model-based planner to build a mixture of path-policies on which a task-specific meta-policy can be learned to complete the task. In our results, we demonstrate that a successful meta-policy can be learned to push a door, while requiring little data and being robust to model uncertainty of the environment. We tested our method on a 7-DOF Franka-Emika Robot pushing a cabinet door in simulation.

Kushagra Gupta, David Fridovich-Keil

We present a novel algorithm for game-theoretic trajectory planning, tailored for settings in which agents can only observe one another in specific regions of the state space. Such problems arise naturally in the context of multi-robot navigation, where occlusions due to environment geometry naturally mask agents' view of one another. In this paper, we formalize these settings as dynamic games with a hybrid information structure, which interleaves so-called "open-loop" periods (in which agents cannot observe one another) with "feedback" periods (with full state observability). We present two main contributions. First, we study a canonical variant of these hybrid information games in which agents' dynamics are linear, and objectives are convex and quadratic. Here, we build upon classical solution methods for the open-loop and feedback variants of these games to derive an algorithm for the hybrid information case that matches the cubic runtime of the classical settings. Second, we consider a far broader class of problems in which agents' dynamics are nonlinear, and objectives are nonquadratic; we reduce these problems to sequences of hybrid information linear-quadratic games and empirically demonstrate that iteratively solving these simpler problems with the proposed algorithm yields reliable convergence to approximate Nash equilibria through simulation studies of overtaking and intersection traffic scenarios.

Douglas Beck, Joseph Carlson, Zohreh Davoudi et al.

In preparation for the 2023 NSAC Long Range Plan (LRP), members of the Nuclear Science community gathered to discuss the current state of, and plans for further leveraging opportunities in, QIST in NP research at the Quantum Information Science for U.S. Nuclear Physics Long Range Planning workshop, held in Santa Fe, New Mexico on January 31 - February 1, 2023. The workshop included 45 in-person participants and 53 remote attendees. The outcome of the workshop identified strategic plans and requirements for the next 5-10 years to advance quantum sensing and quantum simulations within NP, and to develop a diverse quantum-ready workforce. The plans include resolutions endorsed by the participants to address the compelling scientific opportunities at the intersections of NP and QIST. These endorsements are aligned with similar affirmations by the LRP Computational Nuclear Physics and AI/ML Workshop, the Nuclear Structure, Reactions, and Astrophysics LRP Town Hall, and the Fundamental Symmetries, Neutrons, and Neutrinos LRP Town Hall communities.

Thomas Chabal, Robin Strudel, Etienne Arlaud et al.

This paper addresses the problem of copying an unknown assembly of primitives with known shape and appearance using information extracted from a single photograph by an off-the-shelf procedure for object detection and pose estimation. The proposed algorithm uses a simple combination of physical stability constraints, convex optimization and Monte Carlo tree search to plan assemblies as sequences of pick-and-place operations represented by STRIPS operators. It is efficient and, most importantly, robust to the errors in object detection and pose estimation unavoidable in any real robotic system. The proposed approach is demonstrated with thorough experiments on a UR5 manipulator.

Dominik Drexler, Jendrik Seipp, Hector Geffner

Recently, sketches have been introduced as a general language for representing the subgoal structure of instances drawn from the same domain. Sketches are collections of rules of the form C -> E over a given set of features where C expresses Boolean conditions and E expresses qualitative changes. Each sketch rule defines a subproblem: going from a state that satisfies C to a state that achieves the change expressed by E or a goal state. Sketches can encode simple goal serializations, general policies, or decompositions of bounded width that can be solved greedily, in polynomial time, by the SIW_R variant of the SIW algorithm. Previous work has shown the computational value of sketches over benchmark domains that, while tractable, are challenging for domain-independent planners. In this work, we address the problem of learning sketches automatically given a planning domain, some instances of the target class of problems, and the desired bound on the sketch width. We present a logical formulation of the problem, an implementation using the ASP solver Clingo, and experimental results. The sketch learner and the SIW_R planner yield a domain-independent planner that learns and exploits domain structure in a crisp and explicit form.