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Climate change intensifies the likelihood of extreme flood events worldwide, amplifying the potential for compound flooding. This evolving scenario represents an escalating risk, emphasizing the urgent need for comprehensive climate change adaptation strategies across society. Vital to effective response are models that evaluate damages, costs, and benefits of adaptation strategies, encompassing non-linearities and feedback between anthropogenic and natural systems. While flood risk modeling has progressed, limitations endure, including inadequate stakeholder representation and indirect risks such as business interruption and diminished tax revenues. To address these gaps, we propose an innovative version of the Climate-economy Regional Agent-Based model that integrates a dynamic, rapidly expanding agglomeration economy populated by interacting households and firms with extreme flood events. Through this approach, feedback loops and cascading effects generated by flood shocks are delineated within a socio-economic system of boundedly-rational agents. By leveraging extensive behavioral data, our model incorporates a risk layering strategy encompassing bottom-up and top-down adaptation, spanning individual risk reduction to insurance. Calibrated to resemble a research-rich coastal megacity in China, our model demonstrates how synergistic adaptation actions at all levels effectively combat the mounting climate threat. Crucially, the integration of localized risk management with top-down approaches offers explicit avenues to address both direct and indirect risks, providing significant insights for constructing climate-resilient societies.

The reliance of the petrochemical industry on fossil-based sources will need to be reduced by the introduction of Alternative carbon sources (ACS). Introducing ACS in a petrochemical cluster will require existing processes to be modified or replaced, potentially affecting other chemical processes within the cluster due to existing material and energy interconnections. Therefore, it is important to understand the current level of interconnections, functioning, and performance of the petrochemical cluster before introducing ACS. In this work, a representative cluster model based on the petrochemical cluster of the Port of Rotterdam was developed and considered as a case study. This model was analyzed using complex network analysis and environmental and technical key performance indicators. The selected key performance indicators (KPIs) provide insight into the performance of a petrochemical cluster, while the network properties give an understanding of the exchange of material and energy in an industrial cluster., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Energy and Industry, System Engineering

Bioregenerative life support systems (BLSS) are vital for long-duration and remote space missions to increase mission sustainability. These systems break down human waste materials into nutrients and CO2 for plants and other edible organisms, which in turn provide food, fresh water, and oxygen for astronauts. The central idea is to create a materially closed loop, which can significantly reduce mission mass and volume by cutting down or even eliminating disposable waste. In most BLSS studies only a fraction of the resources, such as food, are provided by the system itself, with the rest taken on board at departure or provided through resupply missions. However, for autonomous long-duration space missions without any possibility of resupply, a BLSS that generates all resources with minimal or no material loss, is essential. The goal of this study is to develop a stoichiometric model of a conceptually fully closed BLSS that provides all the metabolic needs of the crew and organisms. The MELiSSA concept of the European Space Agency is used as reference system, consisting of five interconnected compartments, each inhabited by different types of organisms. A detailed review of publicly available MELiSSA literature from 1989 to 2022 revealed that no existing stoichiometric model met the study’s requirements. Therefore, a new stoichiometric model was developed to describe the cycling of the elements C, H, O, and N through all five MELiSSA compartments and one auxiliary compartment. A compact set of chemical equations with fixed coefficients was established for this purpose. A spreadsheet model simulates the flow of all relevant compounds for a crew of six. By balancing the dimensions of the different compartments, a high degree of closure is attained at steady state, with 12 out of 14 compounds exhibiting zero loss, and oxygen and CO2 displaying only minor losses between iterations. This is the first stoichiometric model of a MELiSSA-inspired BLSS that describes a continuo, System Engineering

Agent-based modelling and simulation (ABMS), whether simple toy models or complex data-driven ones, is regularly applied in various domains to study the system-level patterns arising from individual behaviour and interactions. However, ABMS still faces diverse challenges such as modelling more representative agents or improving computational efficiency. Research shows that machine learning (ML) techniques, when used in ABMS can address such challenges. Yet, the ABMS literature is still marginally leveraging the benefits of ML. One reason is the vastness of the ML domain, which makes it difficult to choose the appropriate ML technique to overcome a specific modelling challenge. This paper aims to bring ML more within reach of the ABMS community. We first conduct a structured literature review to investigate how the ABMS process uses ML techniques. We focus specifically on articles where ML is applied for the structural specifications of models such as agent decision-making and behaviour, rather than just for analysing output data. Given that modelling challenges are mainly linked to the purpose a model aims to serve (e.g., behavioural accuracy is required for predictive models), we frame our analysis within different modelling purposes. Our results show that Reinforcement Learning algorithms may increase the accuracy of behavioural modelling. Moreover, Decision Trees, and Bayesian Networks are common techniques for data pre-processing of agent behaviour. Based on the literature review results, we propose guidelines for purposefully integrating ML in ABMS. We conclude that ML techniques are specifically fit for currently underrepresented modelling purposes of social learning and illustration; they can be used in a transparent and interpretable manner., Energie and Industrie, System Engineering, Applied Sciences

Fast growth of cities decreases the quality of life in these places. In response, Municipalities install policies aiming to improve local livability. While literature suggests social structures to have a defining impact on policy effectiveness, current evaluation metrics are not able to take this into account. This paper presents the Social Neighbourhood model, an agent-based model used to simulate and explore how livability changes in a neighbourhood given various social structures and policies. The model is applied to a neighbourhood in The Hague, Netherlands. The main result of the modelling experiments is that social structures have a very strong influence on whether or not a policy to improve livability is effective. Three hypotheses, concerning this relationship between social structures, livability, and policy interventions are drawn up as a starting point for future research., System Engineering

Current strategies to target RNA splicing mutant myeloid cancers proposes targeting the remaining splicing apparatus. This approach has only been modestly sensitizing and is also toxic to non-mutant-bearing wild-type cells. To explore potentially exploitable genetic interactions with spliceosome mutations, we combined data mining and functional screening for synthetic lethal interactions with an Srsf2P95H/+ mutation. Analysis of missplicing events in a series of both human and murine SRSF2P95H mutant samples across multiple myeloid diseases (acute myeloid leukemia, myelodysplastic syndromes, chronic myelomonocytic leukemia) was performed to identify conserved missplicing events. From this analysis, we identified that the cell-cycle and DNA repair pathways were overrepresented within the conserved misspliced transcript sets. In parallel, to functionally define pathways essential for survival and proliferation of Srsf2P95H/+ cells, we performed a genome-wide Clustered regularly interspaced short palindromic repeat loss-of-function screen using Hoxb8 immortalized R26-CreERki/+Srsf2P95H/+ and R26-CreERki/+Srsf2+/+ cell lines. We assessed loss of single guide RNA representation at 3 timepoints: immediately after Srsf2P95H/+ activation, and at 1 week and 2 weeks after Srsf2P95H/+ mutation. Pathway analysis demonstrated that the cell-cycle and DNA damage response pathways were among the top synthetic lethal pathways with Srsf2P95H/+ mutation. Based on the loss of guide RNAs targeting Cdk6, we identified that palbociclib, a CDK6 inhibitor, showed preferential sensitivity in Srsf2P95H/+ cell lines and in primary nonimmortalized lin-cKIT+Sca-1+ cells compared with wild-type controls. Our data strongly suggest that the cell-cycle and DNA damage response pathways are required for Srsf2P95H/+ cell survival, and that palbociclib could be an alternative therapeutic option for targeting SRSF2 mutant cancers.

The mechanism of action of eprenetapopt (APR-246, PRIMA-1MET) as an anticancer agent remains unresolved, although the clinical development of eprenetapopt focuses on its reported mechanism of action as a mutant-p53 reactivator. Using unbiased approaches, this study demonstrates that eprenetapopt depletes cellular antioxidant glutathione levels by increasing its turnover, triggering a nonapoptotic, iron-dependent form of cell death known as ferroptosis. Deficiency in genes responsible for supplying cancer cells with the substrates for de novo glutathione synthesis (SLC7A11, SHMT2, and MTHFD1L), as well as the enzymes required to synthesize glutathione (GCLC and GCLM), augments the activity of eprenetapopt. Eprenetapopt also inhibits iron-sulfur cluster biogenesis by limiting the cysteine desulfurase activity of NFS1, which potentiates ferroptosis and may restrict cellular proliferation. The combination of eprenetapopt with dietary serine and glycine restriction synergizes to inhibit esophageal xenograft tumor growth. These findings reframe the canonical view of eprenetapopt from a mutant-p53 reactivator to a ferroptosis inducer.

Neuroblastoma is a deadly childhood cancer arising in the developing sympathetic nervous system. High-risk patients are currently treated with intensive chemotherapy, which is curative in only 50% of children and leaves some surviving patients with life-long side effects. microRNAs (miRNAs) are critical regulators of neural crest development and are deregulated during neuroblastoma tumorigenesis, making miRNA-based drugs an attractive therapeutic avenue. A functional screen of >1,200 miRNA mimics was conducted in neuroblastoma cell lines to discover miRNAs that sensitized cells to low doses (30% inhibitory concentration [IC30]) of doxorubicin and vincristine chemotherapy used in the treatment of the disease. Three miRNAs, miR-99b-5p, miR-380-3p, and miR-485-3p, had potent chemosensitizing activity with doxorubicin in multiple models of high-risk neuroblastoma. These miRNAs underwent genomic loss in a subset of neuroblastoma patients, and low expression predicted poor survival outcome. In vitro functional assays revealed each of these miRNAs enhanced the anti-proliferative and pro-apoptotic effects of doxorubicin. We used RNA sequencing (RNA-seq) to show that miR-99b-5p represses neuroblastoma dependency genes LIN28B and PHOX2B both in vitro and in patient-derived xenograft (PDX) tumors. Luciferase reporter assays demonstrate that PHOX2B is a direct target of miR-99b-5p. We anticipate that restoring the function of the tumor-suppressive miRNAs discovered here may be a valuable therapeutic strategy for the treatment of neuroblastoma patients.

This paper presents a theoretical framework for a new concept of organizational control, that stimulates organizational change and adaptation. It introduces Participatory Control Systems (PCS) as a distinct type of control based on the complex adaptive system literature. These control systems are fundamentally different from the traditional notion of (management) control. Building on the notion of complex systems and the concept of social learning, PCS increase organizational adaptivity by enabling and facilitating social learning processes that may emerge to transformational change over time. To illustrate the PCS concept in practice, three examples are given in this paper. Moreover, some key implications for internal auditors and suggestions for future research are provided., System Engineering

Cities consume almost 80 percent of world’s energy and account for 60 percent of all the emissions of carbon dioxide and significant amounts of other greenhouse gases (GHG). The ongoing rapid urbanization will further increase GHG emissions of cities. The quantification of the environmental impact generated in cities is an important step to curb the impact. In fact, quantifying the consumption activities taking place inside a city, if differentiated by socioeconomic and demographic groups, can provide important insights for sustainable-consumption policies. However, the lack of high-resolution data related to these activities makes it difficult to quantify urban GHG emissions (as well as other impacts). This paper presents a methodology that can quantify the carbon footprint of households in cities using consumption data from a national or European level, where the resource consumption is linked to socioeconomic attributes of a population. The methodology is applied to analyzing the environmental impact by household resource consumption in the city of The Hague in the Netherlands. The key insights reveal potential intervention areas regarding resource consumption categories and demographic groups that can be targeted to reduce GHG emissions due to consumption-driven activities in the city., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., System Engineering, Policy Analysis

Energy communities are gaining momentum in the context of the energy transition. Given the distributed and collective action nature of energy communities, energy security of these local energy systems is more than just security of supply and related to issues such as affordability and acceptability of energy to members of the community. We build an agent-based model of energy communities to explore their security challenges. The security dimensions we consider are availability, affordability, accessibility and acceptability, which are referred to as the 4As. The results confirmed that there is always a trade-off between all four dimensions and that although it is difficult to achieve a high energy security performance, it is feasible. Results also showed that among factors influencing energy security, the investment of the community plays the biggest role., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Energy and Industry, System Engineering

Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streams ends. Regarding the current societal need for a shift towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the effect of these behaviours on ISN robustness by using an agent-based model (ABM). The constructed model is based on insights from both literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchange and compost production. The Theory of Planned Behaviour (TPB) was used to model agent behaviour in time-dependent bilateral negotiations and synergy evaluation processes. We explored model behaviour with and without TPB logic across a range of possible TPB input variables. The simulation results show how the modelled planned behaviour affects the cash flow outcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by offering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour., Energy & Industry, System Engineering, Applied Sciences

The viability of novel network-level circular business models (CBMs) is debated heavily. Many companies are hesitant to implement CBMs in their daily practice, because of the various roles, stakes and opinions and the resulting uncertainties. Testing novel CBMs prior to implementation is needed. Some scholars have used digital simulation models to test elements of business models, but this this has not yet been done systematically for CBMs. To address this knowledge gap, this paper presents a systematic iterative method to explore and improve CBMs prior to actual implementation by means of agent-based modelling and simulation. An agent-based model (ABM) was co-created with case study participants in three Industrial Symbiosis networks. The ABM was used to simulate and explore the viability effects of two CBMs in different scenarios. The simulation results show which CBM in combination with which scenario led to the highest network survival rate and highest value captured. In addition, we were able to explore the influence of design options and establish a design that is correlated to the highest CBM viability. Based on these findings, concrete proposals were made to further improve the CBM design, from company level to network level. This study thus contributes to the development of systematic CBM experimentation methods. The novel approach provided in this work shows that agent-based modelling and simulation is a powerful method to study and improve circular business models prior to implementation., Energy & Industry, System Engineering, Applied Sciences

Human papilloma viruses (HPV) are the main culprit in cervical and oropharyngeal cancers. HPV positive (+) cancers are regarded as 'oncogene addicted', displaying an absolute requirement for the continued expression of the oncogenes for their viability owing their survival, and thus making these genes salient targets for developing specific therapeutic agents. There is a strong association between HPV and oropharyngeal squamous cell carcinomas (OPSCC), a subset of head and neck cancers (HNCs). Alarmingly, HPV-associated OPSCC are on the rise globally, and the number of cases of HPV + OPSCCs surpasses that of cervical cancer in the USA. Here, we show that major HPV oncogenes, E6 and E7, are essential for the survival of HPV positive (+) OPSCCs, making these oncogenes salient targets for HPV-driven OPSCCs. HPV E7 is known to interact with STING, a component of the viral DNA-sensing cGAS-STING machinery which activates a pro-typical anti-viral type I interferon (IFN) response. Our recent work showed that E7 from HPV type 16 is responsible for the blockade of cGAS-STING responses in HPV + OPSCC cells. In this study, we show that CRISPR/Cas9-mediated loss of E7 from HPV + OPSCC cells, SCC2 and SCC104, restored cGAS-STING responses. Future work could involve HPV oncogene targeting leading to HPV + OPSCC tumour regression and that the combined use of STING agonists would induce favourable tumour clearance by activating appropriate anti-tumour responses.

Flood adaptation measures implemented at the household level play an important role in reducing communities' vulnerability. The aim of this study is to enhance the current modelling practices of human–flood interaction to draw new insights for flood risk management (FRM) policy design. The paper presents a coupled agent-based and flood model for the case of Hamburg, Germany, to explore how individual adaptation behaviour is influenced by flood event scenarios, economic incentives and shared and individual strategies. Simulation results show that a unique trajectory of adaptation measures and flood damages emerges from different flood event series. Another finding is that providing subsidies increases the number of coping households in the long run. Households' social network also has a strong influence on their coping behaviour. The paper also highlights the role of simple measures such as adapted furnishings, which do not incur any monetary cost, in reducing households' vulnerability and preventing millions of euros of contents damages. Generally, we demonstrate that coupled agent-based and flood models can potentially be used as decision support tools to examine the role of household adaptation measures in flood risk management. Although the findings of the paper are case-specific, the improved modelling approach shows the potential to be applied in testing policy levers and strategies considering heterogeneous individual behaviours.

System Engineering, Organisation and Governance