The coastal zone represents a critical intersection of naturally ecological and socio-economic processes. The abundance of data, models, and knowledge derived from various sources in coastal zones facilitates us to integrate them to better understand the evolution of coastal environments. This paper proposes a comprehensive framework of Coastal Zone Information Model (CZIM) to integrate multi-domain coastal information. The core idea of CZIM is to integrate multi-discipline coastal data, models, and knowledge for standardized governance, so as to carry, express, and apply coastal information by the digital system approaching the coastal digital twin. The CZIM framework includes four aspects: coastal data governance, model integration, knowledge engineering, and system construction. We perform a detailed literature review to illustrate the demands and challenges related to those four. The components of each aspect and their interlinks are introduced subsequently, and the future challenges of constructing coastal digital twins relying on CZIM are discussed. CZIM aims to strengthen the ability to organize, manage and apply refined coastal information to support more efficient, scientific, and intelligent decision-making in response to gradually volatile forces from both human activities and natural events, now and in the future. This paper provides a valuable reference for the next generation of coastal digitization in the target of the coastal digital twin.
The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this "digital thread" has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a "Map of Datasets in EDSE." The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas ("data deserts") in early-stage design and system architecture, as well as relatively well-represented areas ("data oases") in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.
This paper gives an overview about aspects of mechanical engineering of undulators. It is based mainly on two types that are used in the SwissFEL facility. The U15 Undulator is an example of an in-vacuum type and the UE38 is an APPLE-X type. It describes the frame, the adjustment of the magnets with flexible keepers and the adjustment of the whole device with eccentric movers.
This paper assesses the contribution that board games can make as decision support tools to offer stakeholders another option to better navigate the complexity and wicked nature of urban challenges using more novel participatory techniques. Using the example of Birmingham, the design, play and evaluate phases of the ‘Urban Placemakers’ game are described and analysed with respect to synergies between key literatures on games and public participation. Using the Urban Placemakers game in a workshop setting to identify and explore the problems facing urban areas, complements traditional approaches to participation and policy-making, but provides additionality through creating more accessible and enjoyable end-user experiences through which policy-focused research models and supporting outputs can be co-developed with stakeholders. The core ingredients of co-design and co-production within the Urban Placemakers game ensure that academic rigour, policy relevance and pragmatism intersect. This convergence space provides a safe hypothetical fertile space for thinking and deliberation that enables players to discuss ‘wicked’ urban problems outside usual agency restrictions, yielding insights to challenges championing innovation and social learning in a fun setting. Whilst playing the game was an enjoyable experience for the majority of participants, it also helped the research team better understand the urban interdependencies within their own work packages and research and was used to help prioritise a set of indicators to explore and diagnose the problems facing the city of Birmingham. This use of a game board approach was found to be a valuable additional method for engaging with urban problems in innovative ways that were grounded in co-creation, play and fun with a computer nowhere in sight.
Ira Puspitasari, Febdian Rusydi, Noorminshah A. Iahad
et al.
Global economic challenges have spurred a rise in household investments worldwide, including in Indonesia. By 2023, retail investors in Indonesia’s capital market have quadrupled, reaching over 12 million, with mutual fund investors driving this surge. Many new investors use mutual fund investment applications to purchase and manage their portfolios. Despite this growth, providers face challenges in maintaining user loyalty and increasing average investments due to Indonesia’s low financial literacy rate of 38.03%. This study investigates the factors influencing users’ adoption and continuous use of mutual fund investment apps. It examines the relationships between perceived service quality, perceived security and privacy, trust, familiarity, user satisfaction, and continuance intention. The research uses a comparative approach, combining Structural Equation Modeling (SEM) with machine learning regression models, including linear regression, support vector regression, and multilayer perceptron, to create a robust and comprehensive model. The findings reveal that trust significantly impacts both user satisfaction (β = 0.463, p < 0.001) and continuance intention (β = 0.194, p < 0.05). Perceived service quality strongly influences user satisfaction (β = 0.523, p < 0.001), while familiarity plays a key role in fostering trust (β = 0.224, p < 0.001) and encouraging continued use (β = 0.206, p < 0.001). Based on these results, strategies to enhance user loyalty, such as improving security, services, and providing transparent recommendations, are proposed. This study contributes to understanding digital investment behavior in emerging markets and offers insights for expanding financial inclusion.
Corrosion affects every sector of the national economy, from industrial and agricultural production to defense technology. It poses a serious threat to the safety of equipment in service, leads to substantial economic losses, and presents significant risks to human life and health. Metal corrosion inhibitors can modify the surface characteristics of metals, increase the activation energy barrier of corrosion reactions, affect surface electrochemical behavior, and slow down the corrosion rate. These inhibitors have advantages such as low dosage, low cost, and high efficiency, making them one of the most widely used methods for corrosion control. However, there are many types of inhibitors with complex mechanisms, which are closely related to environmental factors. Conventional laboratory methods such as precise weight lose analysis or electrochemical measurements such as potentiodynamic polarization and electrochemical impedance spectroscopy are labor-intensive, time-consuming, and costly, which greatly hinders the design and application of high-performance inhibitors. There is an urgent need for a more efficient approach to advance inhibitor research. A recent paradigm shift driven by advancements in materials genome engineering (MGE) is enabling researchers to move beyond the traditional trial-and-error approach. By integrating high-throughput computational tools with fundamental chemical principles, MGE facilitates a more systematic and intelligent exploration of materials science. At the core of this transformation lies machine learning (ML), which serves as a powerful pattern recognition engine. ML algorithms can analyze vast historical experimental data to predict the performance of novel materials and uncover the often hidden, nonlinear relationships between molecular features and their functional properties. In this study, we developed a novel methodology that synergizes a state-of-the-art large language model (LLM) with a predictive ML framework. The LLM was employed to systematically parse and extract meaningful molecular features from thousands of unstructured research papers and experimental datasets, specifically focusing on inhibitors used in CO2-saturated environments. We constructed a comprehensive corrosion inhibitor research dataset by extracting 1152 data entries from 174 peer-reviewed articles on inhibitor development and application in CO2-saturated environments. These entries contain detailed information on molecular structures, corrosion environment parameters, inhibitor concentrations, experimental temperatures, and inhibition efficiency metrics. Statistical analysis revealed that the target variables in our dataset exhibited relatively uniform distributions without significant skewness or clustering, indicating a balanced data structure that supports robust model training and generalization. Our methodology implements a two-stage feature selection strategy based on a collaborative large-small model pipeline. We first established a domain-specific knowledge framework by injecting corrosion science expertise into the Deepseek-R1 LLM, enabling systematic analysis of unstructured scientific texts. This LLM-based approach allowed us to efficiently screen an initial set of 204 molecular descriptors down to 50 candidates that demonstrate clear relevance to CO2 corrosion inhibition mechanisms. We then applied quantitative statistical techniques using a smaller specialized model to further refine the feature set through correlation analysis and recursive feature elimination. This two-phase process reduced the final feature count to 13 non-redundant descriptors that comprehensively captured the interplay between molecular structure, inhibitor concentration, and environmental parameters. The selected 13 features reduced the mean squared error from 121 to 11 of the models. To validate our approach, we built a gradient boosting model incorporating both the selected molecular features and environmental parameters. We identified five representative molecules and their corresponding corrosion environments for experimental testing. The results demonstrated the good generalization ability of the model, confirming its potential for practical application in corrosion inhibitor design and development.
Xiaofeng Bai, Qazi Mazhar Ul Haq, Aftab Alam Khan
et al.
Ball bearings are prone to faults in their inner and outer rings and rolling elements. Timely detection of these faults is crucial, especially when adversarial perturbations are present, as deep learning-based fault diagnosis models may misclassify these faults. To address this issue, this study proposes a hybrid adversarial learning method that combines convolutional neural networks with a generative adversarial network framework. In this method, the generator introduces perturbations and adaptively adjusts them based on their magnitude and gradient information. The discriminator was used to verify the effectiveness of adversarial perturbations. The goal of this hybrid adversarial learning method is to improve the fault recognition accuracy of a model when subjected to perturbation attacks. The experimental results show that under adversarial perturbation attacks, the proposed method outperforms other deep learning models and defence methods, demonstrating the effectiveness of this approach.
In this paper, we study a generalization of Markov games and pseudo-games that we call Markov pseudo-games, which, like the former, captures time and uncertainty, and like the latter, allows for the players' actions to determine the set of actions available to the other players. In the same vein as Arrow and Debreu, we intend for this model to be rich enough to encapsulate a broad mathematical framework for modeling economies. We then prove the existence of a game-theoretic equilibrium in our model, which in turn implies the existence of a general equilibrium in the corresponding economies. Finally, going beyond Arrow and Debreu, we introduce a solution method for Markov pseudo-games and prove its polynomial-time convergence. We then provide an application of Markov pseudo-games to infinite-horizon Markov exchange economies, a stochastic economic model that extends Radner's stochastic exchange economy and Magill and Quinzii's infinite-horizon incomplete markets model. We show that under suitable assumptions, the solutions of any infinite-horizon Markov exchange economy (i.e., recursive Radner equilibria -- RRE) can be formulated as the solution to a concave Markov pseudo-game, thus establishing the existence of RRE and providing first-order methods for approximating RRE. Finally, we demonstrate the effectiveness of our approach in practice by building the corresponding generative adversarial policy neural network and using it to compute RRE in a variety of infinite-horizon Markov exchange economies.
This lecture presents an overview of the basic concepts and fundamentals of Engineering Materials within the framework of accelerator applications. After a short introduction, main concepts relative to the structure of matter are reviewed, like crystalline structures, defects and dislocations, phase diagrams and transformations. The microscopic description is correlated with physical properties of materials, focusing in metallurgical aspects like deformation and strengthening. Main groups of materials are addressed and described, namely, metals and alloys, ceramics, polymers, composite materials, and advanced materials, where brush-strokes of tangible applications in particle accelerators and detectors are given. Deterioration aspects of materials are also presented, like corrosion in metals and degradation in plastics.
Nenad Tomasev, Matija Franklin, Joel Z. Leibo
et al.
The rapid adoption of autonomous AI agents is giving rise to a new economic layer where agents transact and coordinate at scales and speeds beyond direct human oversight. We propose the "sandbox economy" as a framework for analyzing this emergent system, characterizing it along two key dimensions: its origins (emergent vs. intentional) and its degree of separateness from the established human economy (permeable vs. impermeable). Our current trajectory points toward a spontaneous emergence of a vast and highly permeable AI agent economy, presenting us with opportunities for an unprecedented degree of coordination as well as significant challenges, including systemic economic risk and exacerbated inequality. Here we discuss a number of possible design choices that may lead to safely steerable AI agent markets. In particular, we consider auction mechanisms for fair resource allocation and preference resolution, the design of AI "mission economies" to coordinate around achieving collective goals, and socio-technical infrastructure needed to ensure trust, safety, and accountability. By doing this, we argue for the proactive design of steerable agent markets to ensure the coming technological shift aligns with humanity's long-term collective flourishing.
Reducing wealth inequality is a global challenge, and the problems of capitalism stem from the enclosure of the commons and the breakdown of the community. According to previous studies by Polanyi, Karatani, and Graeber, economic modes can be divided into capitalist market economy (enclosure and exchange), power economy (de-enclosure and redistribution), gift economy (obligation to return and reciprocity), and concession economy (de-obligation to return). The concession economy reflects Graeber's baseline communism (from each according to their abilities, to each according to their needs) and Deguchi's We-turn philosophy (the "I" as an individual has a "fundamental incapability" and the subject of physical action, responsibility, and freedom is "We" as a multi-agent system, including the "I"). In this study, we constructed novel network models for these four modes and compared their properties (cluster coefficient, graph density, reciprocity, assortativity, centrality, and Gini coefficient). From the calculation results, it became clear that the market economy leads to inequality; the power economy mitigates inequality but cannot eliminate it; the gift and concession economies lead to a healthy and equal economy; and the concession economy, free from the ties of obligation to return, is possible without guaranteeing reciprocity. We intend to promote the transformation from a capitalist economy to a concession economy through activities that disseminate baseline communism and the We-turn philosophy that promotes concession, that is, developing a cooperative platform to support concession through information technology and empirical research through fieldwork.
Ionut Cristian Scurtu, Katalin Puskas Khetani, Fanel Dorel Scheaua
The current study seeks to explore the underexamined or potentially under-researched social dimensions of circular economy (CE) in the context of buildings. Utilising a meta-synthesis approach, this paper builds on the two primary theoretical frameworks in the well-being literature: the eudaimonic and hedonic perspectives. The analysis of the selected articles reveals that these frameworks foster distinct modes of interaction and perception concerning one’s environment. A consensus is evident among the studies reviewed, advocating for integrating both eudaimonic and hedonic elements to achieve optimal well-being and happiness. Moreover, some scholars argue that for the attainment of sustainability goals and, by extension, CE objectives, the eudaimonic approach to well-being should be emphasised over the currently predominant hedonic inclinations. The research also attempts to open a discourse between the sometimes rather comprehensive, holistic, and hard-to-quantify dimensions of human well-being and the more logical, measurable, and tangible results-oriented approach towards the built environment. This investigation illustrates how well-designed building elements, aligned with CE principles, can play a pivotal role in fostering both environmental sustainability and human flourishing in the built environment.
Concrete reinforcement is essential for ensuring the safety and durability of concrete structures. Bonding steel plates to reinforce concrete is widely used to renovate or strengthen concrete beam structures. Due to construction quality and the influence of factors such as environment and fatigue, debonding often occurs between the steel plate and concrete, making monitoring and early warning after concrete structure reinforcement challenging. This paper proposes a novel approach to monitor the degree of debonding between the steel plate and concrete beam using active sensing technology. The method uses lead zirconate titanate (PZT) as an actuator to generate stress waves. It prepares strip sensors with polyvinylidene fluoride as the sensing element to monitor stress waves passing through the steel plate and concrete beam. The monitoring system detects the degree of debonding between the steel plate and the concrete beam by monitoring the change in surface voltage of the sensor. Experiments show that the degree of debonding significantly correlates with the received voltage signal; the higher the debonding, the larger the received voltage signal. It is also observed that, at the same degree of debonding, the actuator and sensor attachment position have a particular impact on the received voltage signal. Through experiments and numerical simulation analysis, it is found that when the sensor is attached to the left side of the steel plate, that is, the bonded section of the steel plate, the amplitude of the voltage signal collected by the dynamic information acquisition system is the smallest, i.e., V_debonded section > V_middle > V_bonded section. Based on the above research, the active sensing technology proposed in this paper has good sensitivity to the degree of debonding between the steel plate and concrete. It is expected to become an effective monitoring and evaluation method for the degree of debonding between steel plates and concrete.
Abstract Despite its bad reputation as a greenhouse gas and pollutant, carbon dioxide can be viewed as a renewable, non-toxic and cheap feedstock for chemical synthesis. The chemical utilization of CO2 not only could reduce greenhouse gas emissions, but also saves the fossil fuels resources. In this work, the chemical conversions relevant to large-scale utilization of CO2 including use of CO2 as an oxidant, conversion of CO2 to energy materials and synthesis of CO2-based polymers are reviewed with emphasis on catalysis and reaction engineering as well as technology readiness and processes. Environmental metrics such as atom economy, life cycle analysis and exergy efficiency are also considered. A circular economy based on CO2 is possible if renewable energies, catalyst development and separation technologies are integrated and further developed.
Kwang-Cheng Chen, Shih-Chun Lin, Jen-Hao Hsiao
et al.
Smart manufacturing based on artificial intelligence and information communication technology will become the main contributor to the digital economy of the upcoming decades. In order to execute flexible production, smart manufacturing must holistically integrate wireless networking, computing, and automatic control technologies. This article discusses the challenges of this complex system engineering from a wireless networking perspective. Starting from enabling flexible reconfiguration of a smart factory, we discuss existing wireless technology and the trends of wireless networking evolution to facilitate multirobot smart factories. Furthermore, the special sequential decision-making of a multirobot manufacturing system is examined. Social learning can be used to extend the resilience of precision operation in a multirobot system by taking network topology into consideration, which also introduces a new vision for the cybersecurity of smart factories. A summary of highlights of technological opportunities for holistic facilitation of wireless networked multirobot smart factories rounds off this article.
Unmanned aerial vehicles (UAVs) are used to acquire measurement data for an increasing number of applications. Photogrammetric studies based on UAV data, thanks to the significant development of computer vision techniques, photogrammetry, and equipment miniaturization, allow sufficient accuracy for many engineering and non-engineering applications to be achieved. In addition to accuracy, development time and cost of data acquisition and processing are also important issues. The aim of this paper is to present potential limitations in the use of UAVs to acquire measurement data and to present measurement and processing techniques affecting the optimisation of work both in terms of accuracy and economy. Issues related to the type of drones used (multi-rotor, fixed-wing), type of shutter in the camera (rolling shutter, global shutter ), camera calibration method (pre-calibration, self-calibration), georeferencing method (direct, indirect), technique of measuring the external images orientation parameters (RTK, PPK, PPP), flight design methods and the type of software used were analysed.
Technological competitiveness and emerging technologies are more necessary in the organizational strategy to cope with industrial advances and improve the nation's economy. In this sense, technological innovation, computational developments, smart devices, and other technologies are shaping the new industrial revolutions. Therefore, the technological competitiveness and emerging technologies of industry 4.0 and industry 5.0 are holistically analyzed to identify the key elements of developed economies and emerging economies. For this, we used a bibliometric analysis with Biblioshiny, a systematic review of the literature and a content analysis. The results in terms of technological competitiveness in developed economies show the importance of the competences and engineering skills in the personnel approach; R+D+i and the supply chain in the organizational approach; and the use of emerging technologies such as the internet of things and big data. The comparison with emerging economies indicates the importance of key elements such as training and education, and skills in the personnel approach; sustainability and structure in the organizational approach; and emerging technologies such as the internet of things and digitalization.
Matthew Cotton, David Tyfield, Nicholas Gray
et al.
Freeports are special economic zones, providing tax and customs benefits aimed at reducing economic friction and encouraging regional development. This place-based policy analysis of UK freeports draws upon qualitative interviews and deliberative workshops with leading industry, government, and civil society stakeholders in the two largest Freeport regions – Teesside and Liverpool. We find first, that purported tax, customs, and planning benefits are deemed less economically important than the agglomeration of innovation industries within a defined geographic boundary. Second, that stronger action on environmental and economic (in)justice is needed – Freeports could be a just transition mechanism if they can avoid capture by a ‘closed shop’ of industry players. Third, Freeports could facilitate cross-sectoral low-carbon economic regeneration, though they are subject to cycles of expectation, hype, and disappointment. We conclude that national policymakers must acknowledge the competing geographic and governance scales emerging within Freeport-hosting communities, as distributive environmental injustices between different locations remain broadly unaddressed. Finally, though cognisant of changes in political leadership on the horizon, we conclude that Freeports will increase the geographic spread of environmental injustice if this model of low-tax and low-regulation economics becomes a political norm within UK regional economic redevelopment strategy.