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Worldwide, cities rely on the proper functioning of critical infrastructures (CIs) such as electricity, telecommunication, water supply and transportation. Failure of those infrastructures can lead to significant and long-lasting impacts, even far beyond the flooded areas due to cascading effects. Local authorities are eager to take action to reduce flood risk and strive to increase the resilience of their communities. However, CI are often not considered in flood risk assessments. One of the reasons is that CI operators do not share their CI data and internal risk assessments. Therefore, an integral view on flood risk is lacking and risks may be unidentified or underestimated. To overcome this limitation, in this paper we propose an integrated framework for flood risk assessment of urban critical infrastructures (UCIs) for local authorities, which is based on publicly available and field-surveyed CI data. The proposed framework supports cities to carry out cross-sectoral risk screenings on urban district level to evaluate the need for in-depth risk assessments and risk dialogues with CI operators.
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De beleidsuitdagingen in Nederland en Europa zijn fors: woningnood, economische groei, klimaat, geopolitiek en veiligheid. Wij werken graag mee aan de nadere uitwerking van het coalitieakkoord ‘Aan de Slag’. Wij zien dat de stimulering van biogrondstoffen ter vervanging van fossiele grondstoffen een essentieel onderdeel kan zijn van de oplossingen op al deze beleidsterreinen. Inzet van biogrondstoffen draagt bij aan versnelde woningbouw, innovatie en groei in Nederlandse focussectoren, lagere CO2-uitstoot en meer strategische onafhankelijkheid voor gevoelige grondstoffen. Wij pleiten voor de ontwikkeling van een Nederlandse interdepartementale strategie. In deze paper beschrijven we zowel de reikwijdte als de mogelijke onderdelen van zo’n strategie en bieden onze hulp aan.
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Iedereen die met E-Discovery of digitaal forensisch onderzoek te maken heeft, herkent het spanningsveld: de hoeveelheid data groeit elk jaar, maar de tijd om die data zorgvuldig te doorzoeken niet. We hebben zoektechnologie, reviewprocessen en inmiddels ook taalmodellen, maar in de praktijk doen professionals nog veel werk handmatig, ziet Hans Henseler.
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This chapter “Sustainable City Logistics” explores how cities are transitioning toward more efficient, low-emission, and multimodal logistics systems. Traditional urban freight has long depended on vans and trucks, which, while essential, contribute significantly to congestion, emissions, and noise. As cities pursue zero-emission targets, a variety of alternative delivery modes—both traditional and technology-driven—are reshaping the logistics landscape. Previously overlooked modes such as walking, crowdshipping, barge transport, and public transport are now gaining policy attention. Walking plays a vital role in last-meter deliveries, especially when supported by microhubs and designated unloading zones. Crowdshipping leverages citizens’ daily travel patterns for deliveries, promoting social inclusion and sustainability, though it faces challenges regarding trust, regulation, and labour standards. Similarly, the use of waterways and public transport networks for goods distribution offers new opportunities to reduce road traffic and emissions. At the same time, technology-driven innovations are accelerating. Light Electric Freight Vehicles (LEFVs)—including e-cargo bikes and small electric vans—are increasingly used in dense urban areas due to their agility, low emissions, and compatibility with zero-emission zones. Drones and delivery robots are already operational in several Asian cities, offering flexible, contactless last-mile solutions, though most countries still face legal and regulatory hurdles before adopting them widely. Underground freight and Hyperloop systems remain in experimental stages but promise transformative efficiency gains for the future. Overall, the evolution of city logistics reflects a shift from vehicle-focused to system-oriented approaches. Success depends on combining multiple modes, supported by data-driven management, smart infrastructure, and coordinated policies. Sustainable logistics will require collaboration between governments, logistics providers, and citizens to balance efficiency, accessibility, and liveability in the urban environment—moving toward truly zero-impact cities. All authors of this chapter are member of the research community Low Impact Lastmile logisticS (LILS).
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Stedelijke logistiek staat onder druk door groeiende e-commerce, ruimtegebrek en ambitieuze klimaatdoelen. Dit artikel laat zien dat de toekomst van stadslogistiek niet ligt in één dominante vervoersvorm, maar in slimme combinaties van modaliteiten. Van lopen en cargobikes tot watertransport, drones en zelfs Hyperloop: elke modaliteit heeft een eigen rol in een efficiënt en emissiearm systeem. Op basis van een brede literatuurstudie wordt inzicht gegeven in kansen, beperkingen en randvoorwaarden voor succesvolle toepassing. Het artikel biedt beleidsmakers en onderzoekers een kader om modaliteitskeuzes systematisch te beoordelen en beter op elkaar af te stemmen.
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This study aims to identify, prioritize, and evaluate performance criteria that matter most to two key logistics stakeholders—trucking companies and receiving companies—in the context of container transportation. It further investigates how these stakeholders perceive the Connected and Autonomous Transport System (CATS) compared to the Traditional Transport System (TTS). The research is timely due to industry-wide challenges such as driver shortages, safety concerns, rising operational costs, and the need for higher efficiency and sustainability. A mixed-method approach was applied, combining qualitative interviews with industry actors and quantitative analysis using the Multi-Actor Multi-Criteria Analysis (MAMCA) and the Best–Worst Method (BWM). This combination allowed the study to identify stakeholder-specific priorities and understand their expectations regarding autonomous transport.
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Agent-based modeling (ABM) is a powerful tool for simulating building users’ dynamic behavior in demand response (DR) programs. However, ABM faces several challenges, particularly in encoding building users’ natural language features and common sense into rules or mathematical equations. To overcome these limitations, this paper proposes an agent framework based on large language models (LLMs) to simulate building users’ air-conditioning setpoint adjustment behavior under DR. This framework leverages LLMs’ natural language processing capabilities to replicate building users’ reasoning and decision making processes. It consists of five modules: persona, perception, decision, reflection, and memory. Agents are assigned diverse personas through natural language descriptions based on empirical survey data. LLMs drive agents to reason and make decisions based on incentive prices and historical experiences. The results show that the LLM-based agent has common sense derived from natural language-defined personas and exhibits human-like irrational characteristics. This demonstrates the feasibility of replacing rules with natural language in ABM. The LLM-based agent can more effectively model hard-to-parameterize human features and provide decision explanations through LLM outputs. The results show that the inclusion of reflection and memory modules enables the agent to learn from previous decisions and reduce unreasonable choices.
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Agent-based modeling (ABM) is a powerful tool for simulating building users’ dynamic behavior in demand response (DR) programs. However, ABM faces several challenges, particularly in encoding building users’ natural language features and common sense into rules or mathematical equations. To overcome these limitations, this paper proposes an agent framework based on large language models (LLMs) to simulate building users’ air-conditioning setpoint adjustment behavior under DR. This framework leverages LLMs’ natural language processing capabilities to replicate building users’ reasoning and decision making processes. It consists of five modules: persona, perception, decision, reflection, and memory. Agents are assigned diverse personas through natural language descriptions based on empirical survey data. LLMs drive agents to reason and make decisions based on incentive prices and historical experiences. The results show that the LLM-based agent has common sense derived from natural language-defined personas and exhibits human-like irrational characteristics. This demonstrates the feasibility of replacing rules with natural language in ABM. The LLM-based agent can more effectively model hard-to-parameterize human features and provide decision explanations through LLM outputs. The results show that the inclusion of reflection and memory modules enables the agent to learn from previous decisions and reduce unreasonable choices.
MULTIFILE
This study systematically reviews the evolution, present focus, and future potential of floating ports/ harbours within the wider maritime industry. Using a bigram-based search strategy across Scopus and Web of Science, 884 relevant publications were identified, of which 140 directly addressed floating ports or related applications. A structured classification based on the “Elements of the Maritime Industry” framework revealed a strong concentration on construction aspects, with significant gaps in management, logistics, and ancillary activities. Keyword mapping through VOSviewer highlights a progression from safety-driven designs to sustainable, multifunctional, and climate-resilient infrastructures. In addition, the study introduces two working definitions; I) offshore floating ports and II) floating solutions for onshore/nearshore port infrastructure, to clarify emerging directions in the conceptual and functional development of floating port systems. The findings underline both the scarcity and growing importance of floating ports as a critical component of future maritime logistics and governance.
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Planning of container terminal operations is a complex task, which requires the accurate scheduling of operations that are highly interrelated and uncertain. This study aims to investigate the integration of quayside operational planning functions under uncertain parameters by applying a reactive approach. A mixed integer linear programming (MILP) model is formulated to optimally assign and schedule quay cranes to multiple vessels simultaneously. The model will derive a baseline schedule that minimises the cost of waiting and departure delays of vessels. To address the uncertainty, a reactive strategy is formulated to generate a rescheduling plan when two types of disruptions, delays in vessel arrivals and quay crane breakdowns, occur during the operation. The reactive strategy will take the baseline schedule as input and derive a reactive schedule that minimizes the cost of deviations from the baseline schedule. The numerical experiments demonstrate the performance and effectiveness of the proposed approach to solve the integrated formulation under uncertainty.
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