Boris Caroline A Andrew S Mari Amir Molla Oladimeji M Mo Bikbov Purcell Levey Smith Abdoli Abebe Adebayo Af, B. Bikbov, Caroline A. Purcell
et al.
Summary Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI. Funding Bill & Melinda Gates Foundation.
Ayyoob Sharifi, Amir Reza Khavarian-Garmsir, Z. Allam
et al.
The global population has rapidly urbanized over the past century, and the urbanization rate is projected to reach about 70% by 2050. In line with these trends and the increasing recognition of the significance of cities in addressing local and global challenges, a lot of research has been published on urban studies and planning since the middle of the twentieth century. While the number of publications has been rapidly increasing over the past decades, there is still a lack of studies analyzing the field's knowledge structure and its evolution. To fill this gap, this study analyzes data related to more than 100,000 articles indexed under the "Urban Studies” and "Regional & Urban Planning” subject categories of the Web of Science. We conduct various analyses such as term co-occurrence, co-citation, bibliographic coupling, and citation analysis to identify the key defining thematic areas of the field and examine how they have evolved. We also identify key authors, journals, references, and organizations that have contributed more to the field's development. The analysis is conducted over five periods: 1956–1975 (the genesis period), 1976–1995 (economic growth and environmentalism), 1996–2015 (sustainable development and technological innovation), 2016–2019 (climate change and SDGs), and 2020 onwards (post-COVID urbanism). Four major thematic areas are identified: 1) socio-economic issues and inequalities, 2) economic growth and innovation, 3) urban ecology and land use planning, and 4) urban policy and governance and sustainability. The first two are recurring themes over different periods, while the latter two have gained currency over the past 2–3 decades following global events and policy frameworks related to global challenges like sustainability and climate change. Following the COVID-19 pandemic, issues related to smart cities, big data analytics, urban resilience, and governance have received particular attention. We found disproportionate contributions to the field from the Global North. Some countries from the Global South with rapid urbanization rates are underrepresented, which may have implications for the future of urbanization. We conclude the study by highlighting thematic gaps and other critical issues that need to be addressed by urban scholars to accelerate the transition toward sustainable and resilient cities.
In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of conditions may no longer remain valid as the distribution of states or the environment dynamics change, which in turn causes previously learned strategies to fail. In this work, we propose a theoretical framework for planning under partial observability using Partially Observable Markov Decision Processes (POMDPs) formulated using causal knowledge. By representing shifts in the environment as interventions on this causal POMDP, the framework enables evaluating plans under hypothesized changes and actively identifying which components of the environment have been altered. We show how to maintain and update a belief over both the latent state and the underlying domain, and we prove that the value function remains piecewise linear and convex (PWLC) in this augmented belief space. Preservation of PWLC under distribution shifts has the advantage of maintaining the tractability of planning via $α$-vector-based POMDP methods.
Amanuel Kumsa Bojer, Muluneh Woldetsadik Abshare, Fitsum Mesfin
et al.
Abstract Climate and land use changes are critical factors affecting watershed water yields, with significant implications for water resources at both local and regional levels. This study examined the combined effects of temporal and spatial climate variability and land use/land cover (LULC) changes on surface water yield and availability in the Gilgel Gibe watershed, Ethiopia, from 1993 to 2023. Utilizing the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) hydrological models, machine learning, and remote sensing techniques, this study assessed variations in water resources and their impacts on basin water yield. This study utilized Landsat (30 m), MODIS (500 m–1 km), and 4 km resolution climate datasets from the United States Geological Survey (USGS) and NASA POWER for large-scale climate and land-use analyses from 1993 to 2023. An ensemble of machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), were used to evaluate the effects of climate variability and land use on annual water yield. The study revealed significant land cover changes over a 30-year period. Shrubland decreased from 1,108.37 km2 (21.54%) in 1993 to 295.22 km2 (5.74%) in 2023. Grasslands and wetlands also showed declining trends. In contrast, water bodies increased from 12.51 km2 (0.24%) to 41.57 km2 (0.81%), primarily due to the construction of the Gilgel Gibe hydroelectric dam, and forested areas slightly decreased from 626.73 km2 (12.18%) to 534.18 km2 (10.38%). The surface runoff decreased to 15.78% in 2021 and 15.28% in 2022, whereas the water yield dropped from 1.22% in 1993 to 0.83% by 2023. This study also showed a reduction in lateral flow and higher evapotranspiration levels in 2000 and 2017. The decrease in runoff can be attributed to the loss of wetlands and grasslands, reduced precipitation, and regulatory effects of hydropower operations. In contrast, elevated evapotranspiration levels were primarily attributed to temperature extremes, vegetation stress, and potential increases in irrigation practices. These findings underscore the importance of climatic elements in regulating river discharge and the necessity for smart land use planning to prevent negative environmental consequences on water resources.
Abstract Introduction There is a continued increase of older dependant adults in England. Foundation Dentists (FDs) are often the dental workforce being tasked with providing dental care to dependant older adults resident in care home settings. This study explores whether FDs have the experience and confidence to deliver this. Aim This service evaluation aimed to explore FDs’ attitudes, perceptions and experiences delivering dentistry to dependant older adults’ resident in care home settings; to help inform workforce and service delivery planning. Methods All North West England (NW) FDs were invited to complete a semi-structured questionnaire at a regional study session. Results were analysed using descriptive and thematic analysis. Results There were 93 (80.1%) respondents, with the majority aged 20–24 years old (56, 60.2%), female (57, 61.3%) and with an United Kingdom undergraduate dental degree (88, 94.6%). Most respondents had no experience in delivering care in a care home setting at either undergraduate (85, 91.4%) or FD level (84, 90.3%). Only 14 respondents (15.1%) reported confidence to deliver dentistry in a care home setting. Conclusion To deliver dental care for dependant older adults resident in care home settings, FDs require additional training and clinical support. There is a need to review the undergraduate dental curriculum and NHS postgraduate training programmes to increase knowledge and skills for this vulnerable group.
Kristina Hasselgren, Caroline Williamsson, Johanna Wennerblom
et al.
Abstract Introduction Patients with pancreatic ductal adenocarcinoma (PDAC) have a dismal prognosis. The majority of patients are diagnosed at an advanced stage, and for these patients, the only possible treatment is palliative chemotherapy. There are increasing data from retrospective studies indicating that a subgroup of patients with liver-limited metastases may benefit from surgical treatment of liver metastases. However, there is a need for prospective trials. Objective The aim of this study is to prospectively investigate the safety and feasibility of surgically treating patients who are resectable, including those with borderline venous resectable, histopathologically confirmed PDAC, and histopathologically or radiologically confirmed liver metastases. Methods Five Swedish and one Finnish hepatopancreaticobiliary (HPB) centre will participate. Eligible patients will be identified at regional multidisciplinary conferences (MDTs). Before inclusion, they will undergo computed tomography (CT), magnetic resonance imaging (MRI, ) and (positron emission tomography computed tomography)PET-CT to rule out extrahepatic metastases. To be included, patients will have to have four or fewer liver metastases, which must be no larger than 5 cm for patients planning for resection and no larger than 2 cm for patients planning for ablation. The metastases may be either synchronous or metachronous. Patients will undergo four months of chemotherapy before surgical treatment (either resection or ablation), and postoperatively, they will undergo two months of chemotherapy. For those with synchronous metastases, resection of the pancreatic tumour will be performed. Follow-up will be performed over two years postoperatively with regular CT scans and assessments of quality of life. Conclusions In conclusion, this trial will provide increased knowledge concerning whether surgical treatment of liver metastases from pancreatic cancer can result in improved survival. Clinical Trial Number Clinical.Trials.gov (NCT05271110), registered February 26th 2022
We introduce lower-bound certificates for classical planning tasks, which can be used to prove the unsolvability of a task or the optimality of a plan in a way that can be verified by an independent third party. We describe a general framework for generating lower-bound certificates based on pseudo-Boolean constraints, which is agnostic to the planning algorithm used. As a case study, we show how to modify the $A^{*}$ algorithm to produce proofs of optimality with modest overhead, using pattern database heuristics and $h^\textit{max}$ as concrete examples. The same proof logging approach works for any heuristic whose inferences can be efficiently expressed as reasoning over pseudo-Boolean constraints.
Israel Puerta-Merino, Carlos Núñez-Molina, Pablo Mesejo
et al.
Recent advances in Large Language Models (LLMs) are fostering their integration into several reasoning-related fields, including Automated Planning (AP). However, their integration into Hierarchical Planning (HP), a subfield of AP that leverages hierarchical knowledge to enhance planning performance, remains largely unexplored. In this preliminary work, we propose a roadmap to address this gap and harness the potential of LLMs for HP. To this end, we present a taxonomy of integration methods, exploring how LLMs can be utilized within the HP life cycle. Additionally, we provide a benchmark with a standardized dataset for evaluating the performance of future LLM-based HP approaches, and present initial results for a state-of-the-art HP planner and LLM planner. As expected, the latter exhibits limited performance (3\% correct plans, and none with a correct hierarchical decomposition) but serves as a valuable baseline for future approaches.
The purpose of this study is to shed light on the possibilities of higher-level development policy involvement of the financial intermediary institutional system, with particular regard to regional development. The investigation was primarily based on the analysis of the Hungarian financial intermediary system of refundable subsidies from the European Union that operated from 2007 through 2013. The reason for this is that, in terms of the diversity of the institutional system, both concerning the preceding and the 2014-2020 development cycle, this period had the highest diversity of institutions mediating subsidies, which plays a crucial role in the development of solutions that are more precisely suited to the financing needs of the final beneficiaries. After reviewing the available literature and development policy documents, I applied a research method based on a Delphi analysis. The investigation revealed a new finding: the market experience of the institutions' experts was the key factor in the successful placement of intermediary institutions' resources. This aspect had not been previously highlighted in evaluations or literature findings. In addition, the research pointed out, among other things, that for more optimal use of financial instruments for cohesion purposes, an integrated policy mix should be created at a higher level, including primarily the social and financial sectors, as well as the territorial development policy. All this would enable the introduction of combined support products at the implementation level, linking financial instruments with other non-financial types of support. For example, loan or guarantee products could be supplemented with consultancy, education, or mentoring support (especially concerning management, organization development, and strategic planning).
Economic theory. Demography, Economic history and conditions
Nuraddin Kerimov, Aleksandr Onegin, Konstantin Yakovlev
Planning safe paths in 3D workspace for high DoF robotic systems, such as manipulators, is a challenging problem, especially when the environment is populated with the dynamic obstacles that need to be avoided. In this case the time dimension should be taken into account that further increases the complexity of planning. To mitigate this issue we suggest to combine safe-interval path planning (a prominent technique in heuristic search) with the randomized planning, specifically, with the bidirectional rapidly-exploring random trees (RRT-Connect) - a fast and efficient algorithm for high-dimensional planning. Leveraging a dedicated technique of fast computation of the safe intervals we end up with an efficient planner dubbed SI-RRT. We compare it with the state of the art and show that SI-RRT consistently outperforms the competitors both in runtime and solution cost. Our implementation of SI-RRT is publicly available at https://github.com/PathPlanning/ManipulationPlanning-SI-RRT
Urban regeneration presents significant challenges within the context of urbanization, requiring adaptive approaches to tackle evolving needs. Leveraging advancements in large language models (LLMs), we propose Cyclical Urban Planning (CUP), a new paradigm that continuously generates, evaluates, and refines urban plans in a closed-loop. Specifically, our multi-agent LLM-based framework consists of three key components: (1) Planning, where LLM agents generate and refine urban plans based on contextual data; (2) Living, where agents simulate the behaviors and interactions of residents, modeling life in the urban environment; and (3) Judging, which involves evaluating plan effectiveness and providing iterative feedback for improvement. The cyclical process enables a dynamic and responsive planning approach. Experiments on the real-world dataset demonstrate the effectiveness of our framework as a continuous and adaptive planning process.
Luca Barbaglia, Lorenzo Frattarolo, Niko Hauzenberger
et al.
Timely information about the state of regional economies can be essential for planning, implementing and evaluating locally targeted economic policies. However, European regional accounts for output are published at an annual frequency and with a two-year delay. To obtain robust and more timely measures in a computationally efficient manner, we propose a mixed-frequency dynamic factor model that accounts for national information to produce high-frequency estimates of the regional gross value added (GVA). We show that our model produces reliable nowcasts of GVA in 162 regions across 12 European countries.
Classical planners are powerful systems, but modeling tasks in input formats such as PDDL is tedious and error-prone. In contrast, planning with Large Language Models (LLMs) allows for almost any input text, but offers no guarantees on plan quality or even soundness. In an attempt to merge the best of these two approaches, some work has begun to use LLMs to automate parts of the PDDL creation process. However, these methods still require various degrees of expert input or domain-specific adaptations. We present NL2Plan, the first fully automatic system for generating complete PDDL tasks from minimal natural language descriptions. NL2Plan uses an LLM to incrementally extract the necessary information from the short text input before creating a complete PDDL description of both the domain and the problem which is finally solved by a classical planner. We evaluate NL2Plan on seven planning domains, five of which are novel and thus not in the LLM training data, and find that NL2Plan outperforms directly generating the files with an LLM+validator combination. As such, NL2Plan is a powerful tool for assistive PDDL modeling and a step towards solving natural language planning task with interpretability and guarantees.
ABSTRACT This study examines the extent and quality of climate adaptation integration within strategic plans of local governments in British Columbia, Canada. Strategic plans (n = 39) were assessed using plan content analysis in order to understand whether regional planning leads to adaptation action by municipalities. Framed through an institutional resilience lens, we find that regional policy guidance is critical for initiating the uptake of municipal climate adaptation; however, lack of granular adaptation policies informed by appropriate climate data constrains implementation in practice. Through collaboration and leveraging strengths of different levels of government, adaptation barriers can be addressed and the quality of adaptation policies improved.
Lestari Juniawati Ani, Edi Nugroho Lukito, Insap Santosa Paulus
The implementation of the smart city concept in Indonesia has become a necessity and is no longer an option, but a necessity. Indeed, the complexity of the problems facing the government is very high and requires smart solutions. As a form of supporting local governments in Indonesia in developing smart city master plans, the Ministry of Communication and Informatics of the Republic of Indonesia in 2017 launched the "Movement Towards 100 Smart Cities" program. During the implementation of the program, the Ministry of Communication and Informatics has compiled a guidebook that was used by local governments. However, the guidebook is considered unable to accommodate all the needs of local governments to develop smart city master plans. This research aims to identify the synchronization between RPJMD and smart city master plans in Indonesia by using literature analysis and document analysis methods that aim to facilitate local governments in preparing smart city master plans. The analysis results show a link between the RPJMD document and the smart city master plan based on the mapping carried out on the RPJMD document which has previously been prepared as a regional development planning document.
Modelling the lime requirement (LR) is a fast and efficient way to determine the amount of lime required to obtain a pH that can overcome the adverse effects caused by soil acidification. This study aimed to model the LR based on the properties of soil and lime. A total of 17 acidic soils and 39 lime samples underwent soil–lime incubation in the laboratory. The predictive equations for the LR (t ha<sup>−1</sup>) were modelled using ∆pH (the difference between the target pH and initial pH), the neutralizing value (NV, mmol kg<sup>−1</sup>) of lime, soil pH, soil clay content (%), soil bulk density (BD, g cm<sup>−3</sup>), and the depth of soil (h, cm) as the factors in an exponential equation. The generic predictive equation, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">L</mi><mi mathvariant="normal">R</mi><mo>=</mo><mo>∆</mo><mi mathvariant="normal">p</mi><mi mathvariant="normal">H</mi><mo>×</mo><msup><mrow><mi mathvariant="normal">e</mi></mrow><mrow><mo>−</mo><mn>3.88</mn><mo>−</mo><mn>0.069</mn><mo>×</mo><mi mathvariant="normal">N</mi><mi mathvariant="normal">V</mi><mo>+</mo><mn>0.51</mn><mo>×</mo><mi mathvariant="normal">p</mi><mi mathvariant="normal">H</mi><mo>+</mo><mn>0.025</mn><mo>×</mo><mi mathvariant="normal">C</mi><mi mathvariant="normal">l</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">y</mi></mrow></msup><mo>×</mo><mi mathvariant="normal">B</mi><mi mathvariant="normal">D</mi><mo>×</mo><mi mathvariant="normal">h</mi></mrow></semantics></math></inline-formula>, was validated as the most reliable model under field conditions. Simplified predictive equations for different soil textures when limed with quicklime and limestone are also provided. Furthermore, the LR proportions provided by hydrated lime, quicklime, limestone, and dolomite in commercially available lime can be expressed as 0.58:0.64:0.97:1.00. This study provides a novel and robust model for predicting the amount of lime product containing components with different neutralizing abilities that are required to neutralize soils with a wide range of properties. It is of great significance to agronomic activities and soil remediation projects.
Large Language Models (LLMs) have shown remarkable performance in various basic natural language tasks. For completing the complex task, we still need a plan for the task to guide LLMs to generate the specific solutions step by step. LLMs can directly generate task plans, but these plans may still contain factual errors or are incomplete. A high-quality task plan contains correct step-by-step solutions for solving all situations and behavioral instructions for avoiding mistakes. To obtain it, we propose the Learning to Plan method, which involves two phases: (1) In the first learning task plan phase, it iteratively updates the task plan with new step-by-step solutions and behavioral instructions, which are obtained by prompting LLMs to derive from training error feedback. (2) In the subsequent test phase, the LLM uses the learned task plan to guide the inference of LLM on the test set. We demonstrate the effectiveness of our method on the five different reasoning type tasks (8 datasets). Further, our analysis experiment shows that the task plan learned by one LLM can directly guide another LLM to improve its performance, which reveals a new transfer learning paradigm. We release the code at \url{https://github.com/Eureka6174/LearnNLPlan}
This paper presents VBMO, the Voting-Based Multi-Objective path planning algorithm, that generates optimal single-objective plans, evaluates each of them with respect to the other objectives, and selects one with a voting mechanism. VBMO does not use hand-tuned weights, consider the multiple objectives at every step of search, or use an evolutionary algorithm. Instead, it considers how a plan that is optimal in one objective may perform well with respect to others. VBMO incorporates three voting mechanisms: range, Borda, and combined approval. Extensive evaluation in diverse and complex environments demonstrates the algorithm's ability to efficiently produce plans that satisfy multiple objectives.
This chapter examines regional planning and development in relation to sustainability and highlights sustainability challenges in various regional planning case studies. Creating smart cities addresses the problems that arise from rapid urbanisation and growth of the urban population. This chapter provides an overview of smart cities and discusses several global smart city efforts. It introduces the idea of smart energy highlighting the smart grid components and how it tackles environmental challenges in regional planning. Additionally, it analyses several threats to the smart grid that may hinder its efficient operation and makes suggestions on how to deal with them so that sustainable energy may be delivered to smart cities.