Coastal zones are exposed to a range of coastal hazards including sea-level rise with its related effects. At the same time, they are more densely populated than the hinterland and exhibit higher rates of population growth and urbanisation. As this trend is expected to continue into the future, we investigate how coastal populations will be affected by such impacts at global and regional scales by the years 2030 and 2060. Starting from baseline population estimates for the year 2000, we assess future population change in the low-elevation coastal zone and trends in exposure to 100-year coastal floods based on four different sea-level and socio-economic scenarios. Our method accounts for differential growth of coastal areas against the land-locked hinterland and for trends of urbanisation and expansive urban growth, as currently observed, but does not explicitly consider possible displacement or out-migration due to factors such as sea-level rise. We combine spatially explicit estimates of the baseline population with demographic data in order to derive scenario-driven projections of coastal population development. Our scenarios show that the number of people living in the low-elevation coastal zone, as well as the number of people exposed to flooding from 1-in-100 year storm surge events, is highest in Asia. China, India, Bangladesh, Indonesia and Viet Nam are estimated to have the highest total coastal population exposure in the baseline year and this ranking is expected to remain largely unchanged in the future. However, Africa is expected to experience the highest rates of population growth and urbanisation in the coastal zone, particularly in Egypt and sub-Saharan countries in Western and Eastern Africa. The results highlight countries and regions with a high degree of exposure to coastal flooding and help identifying regions where policies and adaptive planning for building resilient coastal communities are not only desirable but essential. Furthermore, we identify needs for further research and scope for improvement in this kind of scenario-based exposure analysis.
Abstract One of the greatest challenges humanity faces is feeding the world’s human population in a sustainable, nutritious, equitable and ethical way under a changing climate. Urgent transformations are needed that allow farmers to adapt and develop while also being climate resilient and contributing minimal emissions. This paper identifies several illustrative adaptation and development pathways, recognising the variety of starting points of different types of farmers and the ways their activities intersect with global trends, such as population growth, climate change, rapid urbanisation dietary changes, competing land uses and the emergence of new technologies. The feasibility of some pathways depends on factors such as farm size and land consolidation. For other pathways, particular infrastructure, technology, access to credit and market access or collective action are required. The most viable pathway for some farmers may be to exit agriculture altogether, which itself requires careful management and planning. While technology offers hope and opportunity, as a disruptor, it also risks maladaptations and can create tradeoffs and exacerbate inequalities, especially in the context of an uncertain future. For both the Sustainable Development Goals and the 2015 Paris Agreement to be achieved, a mix of levers that combine policy, technology, education and awareness-raising, dietary shifts and financial/economic mechanisms is required, attending to multiple time dimensions, to assist farmers along different pathways. Vulnerable groups such as women and the youth must not be left behind. Overall, strong good governance is needed at multiple levels, combining top-down and bottom-up processes.
Pierluigi Colli, Elisabetta Rocca, Jürgen Sprekels
In this paper, we study the optimal control of a phase field model for a tumor growth model of Cahn--Hilliard type in which the often assumed parabolic relaxation of the chemical potential is replaced by a hyperbolic one. Both the cases when the double-well potential governing the phase evolution is of either regular or logarithmic type are covered by the analysis. We show the Fréchet differentiability of the associated control-to-state operator in suitable Banach spaces and establish first-order necessary optimality conditions in terms of a variational inequality involving the adjoint state variables. The necessary optimality conditions are then used to derive sparsity results for the optimal controls.
Recent advances in artificial intelligence have produced systems capable of remarkable performance across a wide range of tasks. These gains, however, are increasingly accompanied by concerns regarding long-horizon developmental behavior, as many systems converge toward repetitive solution patterns rather than sustained growth. We argue that a central limitation of contemporary AI systems lies not in capability per se, but in the premature fixation of their performance frontier. To address this issue, we introduce the concept of a \emph{Dynamic Intelligence Ceiling} (DIC), defined as the highest level of effective intelligence attainable by a system at a given time under its current resources, internal intent, and structural configuration. To make this notion empirically tractable, we propose a trajectory-centric evaluation framework that measures intelligence as a moving frontier rather than a static snapshot. We operationalize DIC using two estimators: the \emph{Progressive Difficulty Ceiling} (PDC), which captures the maximal reliably solvable difficulty under constrained resources, and the \emph{Ceiling Drift Rate} (CDR), which quantifies the temporal evolution of this frontier. These estimators are instantiated through a procedurally generated benchmark that jointly evaluates long-horizon planning and structural creativity within a single controlled environment. Our results reveal a qualitative distinction between systems that deepen exploitation within a fixed solution manifold and those that sustain frontier expansion over time. Importantly, our framework does not posit unbounded intelligence, but reframes limits as dynamic and trajectory-dependent rather than static and prematurely fixed. \vspace{0.5em} \noindent\textbf{Keywords:} AI evaluation, planning and creativity, developmental intelligence, dynamic intelligence ceilings, complex adaptive systems
Agricultural exports plays a pivotal role in shaping Ukraine’s foreign trade specialization; however, the predominance of primary commodities intensifies challenges related to long-term competitiveness and environmental sustainability in conditions of European integration. The strengthening of environmental requirements within the EU Common Agricultural Policy necessitates a reassessment of the structural characteristics of Ukraine’s agricultural exports with explicit consideration of environmental externalities. The purpose of this study is to evaluate the competitive position of Ukraine’s agricultural sector using the revealed comparative advantage (RCA) index and the product complexity index (PCI), interpreting the results through the lens of economic externality theory. The object of the research is the structure of Ukraine’s agricultural exports, while the subject is the relationship between comparative advantages, product complexity, and environmental external effects. The methodological framework integrates international trade analysis tools with concepts from institutional and environmental economics. The empirical basis of the study is formed by international trade statistics for 2023. The research employs index analysis, comparative statistical methods, and theoretical interpretation of the obtained results. The findings indicate that Ukraine’s highest revealed comparative advantage (RCA) values are concentrated in product groups with low product complexity index (PCI) scores, reflecting the predominance of a resource-oriented model of export competitiveness. Although this model supports short-term export performance, it is associated with the accumulation of negative environmental externalities that remain largely non-internalized within the existing institutional framework. The scientific novelty of the study lies in the integrated application of RCA and PCI indicators in conjunction with externality theory to assess the sustainability of agricultural competitiveness in the context of Ukraine’s European integration. The practical significance of the results consists in substantiating policy recommendations aimed at developing financial and economic support mechanisms to stimulate higher value-added agricultural production and to mitigate the adverse effects of intensive natural resource use.
The COMPARE Ecosystem aims to improve the compatibility and benchmarking of open-source products for robot manipulation through a series of activities. One such activity is the development of standards and guidelines to specify modularization practices at the component-level for individual modules (e.g., perception, grasp planning, motion planning) and integrations of components that form robot manipulation capabilities at the pipeline-level. This paper briefly reviews our work-in-progress to date to (1) build repositories of open-source products to identify common characteristics of each component in the pipeline, (2) investigate existing modular pipelines to glean best practices, and (3) develop new modular pipelines that advance prior work while abiding by the proposed standards and guidelines.
Abstract While urban systems are expanding at very fast rates all over the world, understanding their spatial development remains a complex and controversial issue, burdened with confusion in the literature. A common understanding of the spatial behavior of expanding urban systems needs robust conceptualization and empirical evidence. The physical growth of cities assumes different spatial patterns, usually in the form of urban sprawl resulting from multi-dimensional drivers and causing multi-dimensional economic, social and ecological impacts. The need to manage urban sprawl and its manifold adverse consequences by promoting compact urban development and urban densification/re-utilization has been widely promoted in science and policy-making. However, ensuring a high quality of life for urbanites demands integrative points-of-view for the types of compact development to promote, in particular regarding urban green spaces within densification processes. It is essential to consider the effects of compact development not only at larger scales, but also at neighborhood and household scales to pursue moderated and qualified densification, securing and (re-)developing urban green spaces and their multi-dimensional positive impacts. Urban sprawl and compact green cities require adequate and robust multi-dimensional spatially explicit indicators to support urban planners and policy makers. Through articles of this special issue, we explore in this synthesis paper the current international state of the art in developing, testing and implementing multi-dimensional ecological, economic, social and multi-scale regional, city, neighborhood indicators characterizing urban sprawl and compact green cities. The articles provide concepts and international case studies for land monitoring and planning recommendations for sustainable urban development. Such indicators give light to capture the social, economic and environmental dimensions of urban development while assessing the degree and extent of sprawl and compact green cities in a global context.
Rasmus Ulfsnes, Nils Brede Moe, Viktoria Stray
et al.
Generative AI (GenAI) has fundamentally changed how knowledge workers, such as software developers, solve tasks and collaborate to build software products. Introducing innovative tools like ChatGPT and Copilot has created new opportunities to assist and augment software developers across various problems. We conducted an empirical study involving interviews with 13 data scientists, managers, developers, designers, and frontend developers to investigate the usage of GenAI. Our study reveals that ChatGPT signifies a paradigm shift in the workflow of software developers. The technology empowers developers by enabling them to work more efficiently, speed up the learning process, and increase motivation by reducing tedious and repetitive tasks. Moreover, our results indicate a change in teamwork collaboration due to software engineers using GenAI for help instead of asking co-workers which impacts the learning loop in agile teams.
Using the generalized Young measure theory, we extend the theory of Young measure solutions to a class of quasilinear parabolic equations with linear growth, and introduce the concept of generalized Young measure solutions. We prove the existence and uniqueness of the generalized Young measure solutions. In addition, for the gradient flow of convex parabolic variational integral, we show that the generalized Young measure solutions are equivalent to the strong solutions.
Stavros Kalogiannidis, Dimitrios Kalfas, Olympia Papaevangelou
et al.
Climate change presents a pressing challenge to regional development, impacting economies, environments, and societies across the globe. Europe, with its diverse regions and commitment to sustainability, serves as a unique case study for exploring the integration of climate change strategies into regional policy and planning. The purpose of this study is to analyze the integration of climate change strategies into policy and planning for regional development in Europe, especially in Greece. Data was collected from 270 environmental experts across Greece using a questionnaire. The results highlight the significance of regional economic growth (gross regional product), infrastructure quality, educational attainment, and a conducive business environment as key measures of regional development. Opportunities arising from climate change strategy integration are explored, revealing economic benefits, environmental opportunities, social enhancements, and technological advancements. These opportunities not only mitigate climate change’s adverse impacts but also foster innovation, economic growth, and community resilience. Successful integration can position regions as global leaders in sustainability and innovation. Correlation and regression analyses reveal that opportunities for integration and common climate change strategies positively influence regional development, while barriers exhibit a counterintuitive positive relationship. However, several barriers hinder integration efforts, including institutional fragmentation, resource constraints, conflicting political and economic priorities, and insufficient stakeholder engagement. This study sheds light on the intricate relationship between climate change, policy integration, and regional development in Greece. It supports the potential for regions to drive sustainability and innovation while navigating the challenges of climate change, ultimately contributing to a more resilient and prosperous future.
با توجه به پایینبودن مصرف سرانه ماهی در ایران نسبت به میانگین جهانی و امکان افزایش مصرف سرانه با توجه بیشتر به سلامت ماهی و همچنین، لزوم توجه به مواد غذایی سالم و ارگانیک، در مطالعة حاضر، به بررسی عوامل مؤثر بر تمایل به اضافه پرداخت افراد برای خرید ماهی قزلآلای پرورشی سالم بین خانوارهای تهرانی در سال 1401 پرداخته و بدین منظور، با بررسی 398 نفر به عنوان نمونة آماری، از الگوی لاجیت ترتیبی استفاده شد. نتایج بررسی نشاندهندة اثر مثبت و معنیدار متغیرهای جنسیت، تحصیلات، شغل کارمندی، سطح درآمد، میزان آگاهی، تعداد دفعات مصرف در هفته و اهمیت بیشتر کیفیت نسبت به قیمت بود؛ از سوی دیگر، متغیر شاخص اعتماد اثر منفی و معنیدار بر تمایل به پرداخت افراد جامعه داشت. این نتیجه اهمیت توجه به فرهنگسازی در مورد محصولات ارگانیک و سالم در سطح جامعه و همچنین، ایجاد اعتماد میان مصرفکنندگان این محصولات را نشان میدهد. همچنین، با ترویج ذهنیت اهمیت بیشتر کیفیت و سلامت محصولات غذایی نسبت به قیمت آن در میان سایر افراد جامعه، میتوان تقاضای اینگونه محصولات و در نتیجه، سلامت کلی جامعه را بهبود بخشید.
Sewin Fathurrohman, Irfan Ricky Afandi, Firman Noor Hasan
This study aims to analyze the sentiment of the Indonesian public regarding the participation of the Indonesian National Team in the 2024 U-23 Asian Cup through the social media platform X. Sentiment analysis is crucial for understanding public perception and its impact on support for the national team. The research methodology involves collecting user comments on X related to the team's performance during the tournament, followed by data cleaning. The dataset is manually labeled, with 80% used as training data for algorithmic model training and the remaining 20% as test data, classified using Naive Bayes and Support Vector Machine algorithms. The analysis results indicate that the SVM algorithm achieves a higher % accuracy rate of 95% compared to Naive Bayes, which achieves 87%. The majority of the 3367 opinions analyzed express positive or satisfactory sentiments towards the national team's participation. However, there are fewer negative sentiments, highlighting areas requiring team management's attention. This study provides valuable insights into public perception of the Indonesian National Team. Furthermore, these findings can inform policymakers and team managers' decision-making to enhance the team's quality and performance in the future.
Single crystals of Ruddlesden-Popper nickelates La$_4$Ni$_3$O$_{10}$ were grown by means of the floating-zone technique at oxygen pressure of 20~bar. Our results reveal the effects of the annealing process under pressure on the crystal structure. We present the requirements for crystal growth and show how a reported ferromagnetic impurity phase can be avoided. The different growth and post-annealing processes result in two distinct phases $P2_1/a$ and {\it Bmab} in which the metal-to-metal transitions occur at 152~K and 136~K, respectively.
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.
Abstract E-sports is an event involving professional game competitions. Despite a significant growth of e-sports industry, research findings and literature, including systematic evaluation and comparison of destinations are still in infant stage. Thus, the current paper attempts to review e-sports industry and address the multiple indicators for success. The results of this study show that the economic impact by esports is significant for a destination. In particular, government strategic plans and investment should be considered for strategic development of e-sports particularly in the wake of the COVID-19 pandemic.