Hasil untuk "Economic growth, development, planning"

Robert C. Davis, D. Mcclelland

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.

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.

Xiaozhu Li, Zhi-jun Chen, Xiao-chao Fan et al.

Ying Long, CC Huang

Giulia Cavalleri, Alain Miranville

In this paper, we study an optimal control problem for a brain tumor growth model that incorporates lactate metabolism, viscoelastic effects, and tissue damage. The PDE system, introduced in [G. Cavalleri, P. Colli, A. Miranville, E. Rocca, On a Brain Tumor Growth Model with Lactate Metabolism, Viscoelastic Effects, and Tissue Damage (2025)], couples a Fisher-Kolmogorov type equation for tumor cell density with a reaction-diffusion equation for the lactate, a quasi-static force balance governing the displacement, and a nonlinear differential inclusion for tissue damage. The control variables, representing chemotherapy and a lactate-targeting drug, influence tumor progression and treatment response. Starting from well-posedness, regularity, and continuous dependence results already established, we define a suitable cost functional and prove the existence of optimal controls. Then, we analyze the differentiability of the control-to-state operator and establish a necessary first-order condition for treatment optimality.

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.

Matt Salehi

This study explored how advanced budgeting techniques and economic indicators influence funding levels and strategic alignment in California Community Colleges (CCCs). Despite widespread implementation of budgeting reforms, many CCCs continue to face challenges aligning financial planning with institutional missions, particularly in supporting diversity, equity, and inclusion (DEI) initiatives. The study used a quantitative correlational design, analyzing 30 years of publicly available economic data, including unemployment rates, GDP growth, and CPI, in relation to CCC funding trends. Results revealed a strong positive correlation between GDP growth and CCC funding levels, as well as between CPI and funding levels, underscoring the predictive value of macroeconomic indicators in budget planning. These findings emphasize the need for educational leaders to integrate economic forecasting into budget planning processes to safeguard institutional effectiveness and sustain programs serving underrepresented student populations.

S. Levy

G. Green, Anna L. Haines

Jan Kaczmarzyk

The Monte Carlo simulation is the ultimate solution for considering nearly all possible scenarios in presumably any discounted cash flow valuation. This paper argues that a discount rate expresses an investor’s current requirement and should be respectively perceived as a parameter only. The consequences of qualifying a required rate of return (a discount rate) as a risk factor in a discounted cash flow valuation are described in the paper using a free cash flow financial model of an asset being a hypothetical publicly traded enterprise. The case study is a discounted cash flow valuation using the Monte Carlo simulation for risk analysis. The various sets of assumptions are considered to explain the consequences of qualifying a required rate of return in a discounted cash flow model as a risk factor. As indicated in the paper, the discount rate as an additional risk factor with an attributed probability distribution increases the volatility of a risk variable, then the distribution of a risk variable becomes more flattened. In previous studies, some authors indicated that a discount rate could be considered a risk factor in the Monte Carlo simulation (Krysiak 2000; Damodaran 2018).

Gertjan Wijburg, Richard Waldron

Introduction to the special issue "Social Movements against Housing Financialization".

Yanbing Bai, Jinhua Su, Bin Qiao et al.

Timely and accurate economic data is crucial for effective policymaking. Current challenges in data timeliness and spatial resolution can be addressed with advancements in multimodal sensing and distributed computing. We introduce Senseconomic, a scalable system for tracking economic dynamics via multimodal imagery and deep learning. Built on the Transformer framework, it integrates remote sensing and street view images using cross-attention, with nighttime light data as weak supervision. The system achieved an R-squared value of 0.8363 in county-level economic predictions and halved processing time to 23 minutes using distributed computing. Its user-friendly design includes a Vue3-based front end with Baidu maps for visualization and a Python-based back end automating tasks like image downloads and preprocessing. Senseconomic empowers policymakers and researchers with efficient tools for resource allocation and economic planning.

Seymur Garibov, Wadim Strielkowski

Climate change, deforestation, and biodiversity loss are calling for innovative approaches to effective reforestation and afforestation. This paper explores the integration of artificial intelligence and remote sensing technologies for optimizing tree planting strategies, estimating labor requirements, and determining space needs for various tree species in Gabala District of Azerbaijan. The study employs YOLOv8 for precise identification of potential planting sites and a Retrieval-Augmented Generation approach, combined with the Gemini API, to provide tailored species recommendations. The methodology incorporates time-series modeling to forecast the impact of reforestation on CO2 emissions reduction, utilizing Holt-Winters for predictions. Our results indicate that the AI model can effectively identify suitable locations and species, offering valuable insights into the potential economic and environmental benefits of large-scale tree planting thus fostering sustainable economic development and helping to mitigate the adverse effects of global warming and climate change.

A. Merino-Saum, M. Baldi, I. Gunderson et al.

Abstract Natural Resources are essential inputs for economic and social development. However, unsustainable resource use has led to environmental degradation and resource depletion, endangering the well-being of humanity and the environment. The 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs) represent a plan of action to address these issues. The Green Economy (GE) concept is described by various institutions as a vehicle to move towards sustainable resource management. This paper demonstrates the linkages between SDGs and Natural Resources though the systematic analysis of 494 GE indicators, derived from 12 distinct frameworks focusing on GE or on Green Growth. This articulation provides insights to gain an improved understanding of the links between SDGs and Natural Resources and interpret their inherent complexity. GE indicators focus unevenly on SDG, although each SDG is related to at least one resource category. Two complementary typologies were applied to the Materials subcategory to highlight additional characteristics, leading to the proposal of an adaptable analytical framework for the assessment of sustainability issues and GE transitions.

Carlos Núñez-Molina, Pablo Mesejo, Juan Fernández-Olivares

In the field of Automated Planning there is often the need for a set of planning problems from a particular domain, e.g., to be used as training data for Machine Learning or as benchmarks in planning competitions. In most cases, these problems are created either by hand or by a domain-specific generator, putting a burden on the human designers. In this paper we propose NeSIG, to the best of our knowledge the first domain-independent method for automatically generating planning problems that are valid, diverse and difficult to solve. We formulate problem generation as a Markov Decision Process and train two generative policies with Deep Reinforcement Learning to generate problems with the desired properties. We conduct experiments on three classical domains, comparing our approach against handcrafted, domain-specific instance generators and various ablations. Results show NeSIG is able to automatically generate valid and diverse problems of much greater difficulty (15.5 times more on geometric average) than domain-specific generators, while simultaneously reducing human effort when compared to them. Additionally, it can generalize to larger problems than those seen during training.

Nils Wilken, Lea Cohausz, Christian Bartelt et al.

Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the area of Plan Recognition As Planning, require quite large amounts of computation time to calculate a solution. Mainly to address this issue, recently, Pereira et al. developed an approach that is based on planning landmarks and is much more computationally efficient than previous approaches. However, the approach, as proposed by Pereira et al., also uses trivial landmarks (i.e., facts that are part of the initial state and goal description are landmarks by definition). In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach. The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance.

Md. Tarek Hasan, Mohammad Nazmush Shamael, Arifa Akter et al.

Sustainable development is a framework for achieving human development goals. It provides natural systems' ability to deliver natural resources and ecosystem services. Sustainable development is crucial for the economy and society. Artificial intelligence (AI) has attracted increasing attention in recent years, with the potential to have a positive influence across many domains. AI is a commonly employed component in the quest for long-term sustainability. In this study, we explore the impact of AI on three pillars of sustainable development: society, environment, and economy, as well as numerous case studies from which we may deduce the impact of AI in a variety of areas, i.e., agriculture, classifying waste, smart water management, and Heating, Ventilation, and Air Conditioning (HVAC) systems. Furthermore, we present AI-based strategies for achieving Sustainable Development Goals (SDGs) which are effective for developing countries like Bangladesh. The framework that we propose may reduce the negative impact of AI and promote the proactiveness of this technology.

Victoria Foye

The study analyses the impacts of climate change on macro prices (food prices, interest rate, and exchange rate). Secondary data from 1960–2019 are used, and the nonlinear autoregressive distributed lag method is employed accordingly. The results reveal that there is a long-run relationship among the variables employed. In addition, asymmetry only exists between food prices and exchange rate in the short run while it only subsists for all macro prices, except interest rate as a dependent variable, in the long run. Also, the relative effects of climate change on macro prices grade food prices with the highest effect. In fact, the continual need for climate policies in both financial and real sectors to douse the effect of climate change on macro prices cannot be overemphasised. Therefore, this study recommends that the Nigerian government and policymakers should ratify and pursue policy initiatives and strategies based on both negative and positive changes in macro prices.

Yong Wang, Zhentao Yin, Jianwei Xing

By analyzing high-frequency data of mobile platform transactions in a large Chinese city, this paper explores the effects of digital coupons on catering and retailing businesses. The results show that digital coupons could generate positive and sustainable effects on the turnover and total sales of local catering and retailing businesses. These positive effects are found for catering and retailing businesses of all sizes, especially large merchants. In addition, while digital coupons are effective in boosting the consumption of commodities such as food and cell phones, they do not crowd out spending on other categories.