Design Choices Research Articles

Performance analysis of deep borehole heat exchangers for decarbonization of heating systems

AbstractMeeting the climate change mitigation targets will require a substantial shift from fossil to clean fuels in the heating sector. Heat pumps with deep borehole exchangers are a promising solution to reduce emissions. Here the thermal behavior of deep borehole exchangers (DBHEs) ranging from 1 to 2 km was analyzed for various heat flow profiles. A strong correlation between thermal energy extraction and power output from DBHEs was found, also influenced by the heating profile employed. Longer operating time over the year typically resulted in higher energy production, while shorter one yielded higher average thermal power output, highlighting the importance of the choice of heating strategy and system design for optimal performance of DBHEs. Short breaks in operation for regenerating the borehole, for example, with waste heat, proved to be favorable for the performance yielding an overall heat output close to the same as with continuous extraction of heat. The results demonstrate the usefulness of deep boreholes for dense urban areas with less available space. As the heat production from a single DBHE in Finnish conditions ranges from half up to even a few GWh a year, the technology is best suitable for larger heat loads.

A theory of policy advisory system quality: Hirschman 2.0 or what makes for good policy advice?

Not everyone’s ideas count equally in terms of influencing and informing policy design and instrument choices. As the literature on policy advice has shown, such advice arises from many different actors interacting with each other often over relatively long timeframes. Actors within these ‘policy advisory systems’ operate within the confines of an existing set of political and economic institutions and governing norms, and each actor brings with them different interests, ideas and resources. Studying who these actors are, how they act and how their actions affect the overall nature of the advice system and its contents are critical aspects of current public policy research. But not all these elements have been equally well conceptualised or studied, especially those concerning their impact on the quality of policy advice emerging from a system. In this article, the general nature of policy advisory systems is set out, their major components described and a model of individual and organisational behaviour within them outlined inspired by a modification of the ‘exit, voice, loyalty’ rubric of Albert Hirschman. Our findings show how aggregated individual organisational behaviour along the lines suggested by Hirschman can over time result in very different kinds of advice being provided by an advisory system, with predictable consequences for its nature and quality.

Design and Development of an Electronic Controller for Accurate Temperature Management for Storage of Biological and Chemical Samples in Healthcare

This paper presents the design and development of an electronic controller for accurate temperature management for the storage of biological and chemical samples in healthcare applications. In the introduction, some important application aspects related to the use of temperature control devices in healthcare are discussed. Keeping these aspects in mind, a brief overview of some related works is presented. The findings are then translated to specific requirements for an electronic controller, which is to be used in a temperature control device. These requirements made necessary the development of a custom controller, as no readily available solutions could be obtained. The paper proceeds with the design of a suitable architecture and discusses some of the design choices. Then, some implementation details are presented and the prototype controller, together with its user interface, is illustrated. Experiments are conducted and several points for improvement are identified. Overall, the main task of keeping accurate, traceable temperature at all times is accomplished successfully, and the electronic controller proves to be a viable solution that conforms to the identified requirements. Future versions will improve the speed of the temperature adaptation and include better user interface and wireless connectivity for remote monitoring and control.

ENHANCED TERRAIN RECOGNITION WITH DEEP LEARNING

Terrain recognition is vital for numerous real-world applications, from autonomous navigation to disaster management. Convolutional Neural Networks (CNNs) have emerged as potent tools for addressing terrain recognition challenges. In this paper, we propose an innovative approach to significantly improve terrain recognition accuracy using CNNs. We meticulously curate a dataset from Kaggle, comprising 1989 high-resolution images categorized into four terrain classes: Grassy, Sandy, Rocky, and Marshy. Our methodology revolves around the systematic design and implementation of deep learning techniques, primarily focusing on CNN architectures. Additionally, we contribute by training a CNN model tailored for classifying images into the four terrain classes. Leveraging the computational resources of Google Colab, we conduct extensive experimentation and analysis to evaluate the performance of our CNN-based terrain recognition system. Empirical results demonstrate substantial advancements in terrain recognition accuracy, underscoring the transformative role of CNNs in enhancing the efficiency and precision of terrain classification systems. Furthermore, we delve into the intricacies of our CNN model's architecture, exploring key design choices and optimization strategies. These insights deepen our understanding of CNN- based terrain recognition systems and provide valuable guidance for future research endeavors. Overall, our study highlights the practical relevance and transformative potential of CNNs in elevating terrain recognition accuracy. Keywords: Terrain recognition, Convolutional Neural Networks (CNNs), Image classification, Real-world applications, Dataset, Performance evaluation, Optimization strategies, Deep learning

Understanding Patient Perspectives on the Use of Gamification and Incentives in mHealth Apps to Improve Medication Adherence: Qualitative Study.

Medication nonadherence remains a significant health and economic burden in many high-income countries. Emerging smartphone interventions have started to use features such as gamification and financial incentives with varying degrees of effectiveness on medication adherence and health outcomes. A more consistent approach to applying these features, informed by patient perspectives, may result in more predictable and beneficial results from this type of intervention. This qualitative study aims to identify patient perspectives on the use of gamification and financial incentives in mobile health (mHealth) apps for medication adherence in Australian patients taking medication for chronic conditions. A total of 19 participants were included in iterative semistructured web-based focus groups conducted between May and December 2022. The facilitator used exploratory prompts relating to mHealth apps, gamification, and financial incentives, along with concepts raised from previous focus groups. Transcriptions were independently coded to develop a set of themes. Three themes were identified: purpose-driven design, trust-based standards, and personal choice. All participants acknowledged gamification and financial incentives as potentially effective features in mHealth apps for medication adherence. However, they also indicated that the effectiveness heavily depended on implementation and execution. Major concerns relating to gamification and financial incentives were perceived trivialization and potential for medication abuse, respectively. The study's findings provide a foundation for developers seeking to apply these novel features in an app intervention for a general cohort of patients. However, the study highlights the need for standards for mHealth apps for medication adherence, with particular attention to the use of gamification and financial incentives. Future research with patients and stakeholders across the mHealth app ecosystem should be explored to formalize and validate a set of standards or framework.

AeroSense: Sensing Aerosol Emissions from Indoor Human Activities

The types of human activities occupants are engaged in within indoor spaces significantly contribute to the spread of airborne diseases through emitting aerosol particles. Today, ubiquitous computing technologies can inform users of common atmosphere pollutants for indoor air quality. However, they remain uninformed of the rate of aerosol generated directly from human respiratory activities, a fundamental parameter impacting the risk of airborne transmission. In this paper, we present AeroSense, a novel privacy-preserving approach using audio sensing to accurately predict the rate of aerosol generated from detecting the kinds of human respiratory activities and determining the loudness of these activities. Our system adopts a privacy-first as a key design choice; thus, it only extracts audio features that cannot be reconstructed into human audible signals using two omnidirectional microphone arrays. We employ a combination of binary classifiers using the Random Forest algorithm to detect simultaneous occurrences of activities with an average recall of 85%. It determines the level of all detected activities by estimating the distance between the microphone and the activity source. This level estimation technique yields an average of 7.74% error. Additionally, we developed a lightweight mask detection classifier to detect mask-wearing, which yields a recall score of 75%. These intermediary outputs are critical predictors needed for AeroSense to estimate the amounts of aerosol generated from an active human source. Our model to predict aerosol is a Random Forest regression model, which yields 2.34 MSE and 0.73 r2 value. We demonstrate the accuracy of AeroSense by validating our results in a cleanroom setup and using advanced microbiological technology. We present results on the efficacy of AeroSense in natural settings through controlled and in-the-wild experiments. The ability to estimate aerosol emissions from detected human activities is part of a more extensive indoor air system integration, which can capture the rate of aerosol dissipation and inform users of airborne transmission risks in real time.

Parametric Dynamic Simulation and Bayesian Design Optimization of a Front-Loading Washing Machine

Abstract Objective This paper presents the dynamic modeling and design optimization of a fully parametrized front-loading washing machine. Methods A thorough mathematical analysis is performed to capture the effect of nine design variables (including rotation speed, spring stiffness, weight balancer mass density, damping coefficient, spring and damper angles) on vibrational characteristics of the washing machine. The parametric simulation reveals a complex relationship between the vibrational dynamics and design variables, entailing a computationally efficient optimization approach. A Bayesian Optimization (BO) framework is developed, in which a Gaussian process model and Genetic Algorithm (GA) are utilized along with carefully selected acquisition functions to enable adaptive sampling and search for optimal design values. The objective function is to minimize the maximum amplitude of the tub displacement inside the washing machine body. Two case studies are performed to consider a different number of design variables and choices of various infill methods. Results Results show that the fully parametrized front-loading washing machine model is applicable for parametric simulation and design optimization. The proposed BO is able to successfully find a set of design variable values corresponding to the lowest maximum displacement for vibration reduction. A comparison analysis is also carried out to exhibit the difference in optimization convergence and computational cost caused by the choice of acquisition functions. Conclusion It is shown that the fully parametrized washing machine model yields a lower maximum displacement than the model with fewer design variables. Of the two acquisition functions studied for optimization, the Expected Improvement function converges more rapidly than the Lower Bound criterion.

Vacuum chambers for Swiss Light Source arcs

The vacuum chambers for diffraction limited storage ring differ from the previous storage rings generation in three main aspects: the cross-section dimension, which is divided by a factor of 2 or more to fit smaller magnet apertures; the material, which includes much more copper for heat dissipation and to limit resistive wakefields; the coating of the inner surface with a nonevaporable getter (NEG) to ensure good pumping level despite low conductance. This paper gives a detailed description of the vacuum chambers’ design used in the arc section of SLS 2.0, starting with conceptual design choices based among others on synchrotron radiation heat and wakefield considerations. The particular case of the main bending magnet vacuum chamber is explained in detail from design to manufacturing, including the development of an appropriate NEG coating procedure. Finally, the overall assembly of a 17 m long arc, its activation to reach pressure in the 10−11 mbar range followed by transport and installation into the magnets is presented. This validates the choice of activation for an arc vessel made out of copper, with wall thicknesses as small as 1 mm and with less than 0.5 mm clearance to magnet poles. Published by the American Physical Society 2024

Community-based participatory-research through co-design: supporting collaboration from all sides of disability

BackgroundAs co-design and community-based participatory research gain traction in health and disability, the challenges and benefits of collaboratively conducting research need to be considered. Current literature supports using co-design to improve service quality and create more satisfactory services. However, while the ‘why’ of using co-design is well understood, there is limited literature on ‘how’ to co-design. We aimed to describe the application of co-design from start to finish within a specific case study and to reflect on the challenges and benefits created by specific process design choices.MethodsA telepractice re-design project has been a case study example of co-design. The co-design was co-facilitated by an embedded researcher and a peer researcher with lived experience of disability. Embedded in a Western Australian disability organisation, the co-design process included five workshops and a reflection session with a team of 10 lived experience and staff participants (referred to as co-designers) to produce a prototype telepractice model for testing.ResultsThe findings are divided into two components. The first describes the process design choices made throughout the co-design implementation case study. This is followed by a reflection on the benefits and challenges resulting from specific process design choices. The reflective process describes the co-designers’ perspective and the researcher’s and organisational experiences. Reflections of the co-designers include balancing idealism and realism, the value of small groups, ensuring accessibility and choice, and learning new skills and gaining new insights. The organisational and research-focused reflections included challenges between time for building relationships and the schedules of academic and organisational decision-making, the messiness of co-design juxtaposed with the processes of ethics applications, and the need for inclusive dissemination of findings.ConclusionsThe authors advocate that co-design is a useful and outcome-generating methodology that proactively enables the inclusion of people with disability and service providers through community-based participatory research and action. Through our experiences, we recommend community-based participatory research, specifically co-design, to generate creative thinking and service design.

HierCGRA: A Novel Framework for Large-scale CGRA with Hierarchical Modeling and Automated Design Space Exploration

Coarse-grained reconfigurable arrays (CGRAs) are promising design choices in computation-intensive domains, since they can strike a balance between energy efficiency and flexibility. A typical CGRA comprises processing elements (PEs) that can execute operations in applications and interconnections between them. Nevertheless, most CGRAs suffer from the ineffectiveness of supporting flexible architecture design and solving large-scale mapping problems. To address these challenges, we introduce HierCGRA, a novel framework that integrates hierarchical CGRA modeling, Chisel-based Verilog generation, LLVM-based data flow graph (DFG) generation, DFG mapping, and design space exploration (DSE). With the graph homomorphism (GH) mapping algorithm, HierCGRA achieves a faster mapping speed and higher PE utilization rate compared with the existing state-of-the-art CGRA frameworks. The proposed hierarchical mapping strategy achieves 41× speedup on average compared with the ILP mapping algorithm in CGRA-ME. Furthermore, the automated DSE based on Bayesian optimization achieves a significant performance improvement by the heterogeneity of PEs and interconnections. With these features, HierCGRA enables the agile development for large-scale CGRA and accelerates the process of finding a better CGRA architecture.

Environmental life cycle assessment of reusable launch vehicle fleets: Large climate impact driven by rocket exhaust emissions

After the success of the reusable Falcon 9 rocket, space actors are pursuing competitive space access by developing Reusable Launch Vehicles (RLVs). While this initiative may enhance recycling rates, it may also trigger the Jevons’ paradox as it amplifies the overall environmental footprint due to increased launch frequencies. It is therefore essential to quantify RLVs’ impacts and identify key design drivers to enable efficient design choices while mitigating undesirable environmental effects.Consequently, this article uses a space specific Life Cycle assessment (LCA) approach to evaluate the environmental footprint, in terms of climate impact, water depletion and land use, of different RLV fleets designed to serve a forecasted European space market. The results show that the LH2 fleet options have 2–8 times lower carbon footprint when compared to the LCH4 fleet as a result of lower propellant consumption and lack of black carbon emissions, suggesting that the environmental burdens are mostly driven by propellant choice. Moreover, the analysis reveals a potential underestimation of climate impacts in previous LCA’s by 2–3 orders of magnitude due to the absence of high altitude characterisation of rocket exhaust emissions and demised aluminium oxides. This increased forcing could lead to fleet choices surpassing the Earth’s carrying capacity given by its planetary boundaries.The methodology and results within this study can support further integration of launch and reentry emissions within LCA by refining modelling techniques, improving impact characterisation and quantifying uncertainties. These advancements can ultimately enable robust eco-design strategies for launch vehicles.

Terrestrial very-long-baseline atom interferometry: Workshop summary

This document presents a summary of the 2023 Terrestrial Very-Long-Baseline Atom Interferometry Workshop hosted by CERN. The workshop brought together experts from around the world to discuss the exciting developments in large-scale atom interferometer (AI) prototypes and their potential for detecting ultralight dark matter and gravitational waves. The primary objective of the workshop was to lay the groundwork for an international TVLBAI proto-collaboration. This collaboration aims to unite researchers from different institutions to strategize and secure funding for terrestrial large-scale AI projects. The ultimate goal is to create a roadmap detailing the design and technology choices for one or more kilometer--scale detectors, which will be operational in the mid-2030s. The key sections of this report present the physics case and technical challenges, together with a comprehensive overview of the discussions at the workshop together with the main conclusions.

Tailoring Network Topology in Mechanically Robust Hydrogels for 3D Printing and Injection.

Tissue engineering and regenerative medicine are confronted with a persistent challenge: the urgent demand for robust, load-bearing, and biocompatible scaffolds that can effectively endure substantial deformation. Given that inadequate mechanical performance is typically rooted in structural deficiencies─specifically, the absence of energy dissipation mechanisms and network uniformity─a crucial step toward solving this problem is generating synthetic approaches that enable exquisite control over network architecture. This work systematically explores structure-property relationships in poly(ethylene glycol)-based hydrogels constructed utilizing thiol-yne chemistry. We systematically vary polymer concentration, constituent molar mass, and cross-linking protocols to understand the impact of architecture on hydrogel mechanical properties. The network architecture was resolved within the molecular model of Rubinstein-Panyukov to obtain the densities of chemical cross-links and entanglements. We employed both nucleophilic and radical pathways, uncovering notable differences in mechanical response, which highlight a remarkable degree of versatility achievable by tuning readily accessible parameters. Our approach yielded hydrogels with good cell viability and remarkably robust tensile and compression profiles. Finally, the hydrogels are shown to be amenable to advanced processing techniques by demonstrating injection- and extrusion-based 3D printing. Tuning the mechanism and network regularity during the cell-compatible formation of hydrogels is an emerging strategy to control the properties and processability of hydrogel biomaterials by making simple and rational design choices.

An Interactive Feeder to Induce and Assess Emotions from Vocalisations of Chickens.

Understanding the emotional states of animals is a long-standing research endeavour that has clear applications in animal welfare. Vocalisations are emerging as a promising way to assess both positive and negative emotional states. However, the vocal expression of emotions in birds is a relatively unexplored research area. The goal of this study was to develop an interactive feeding system that would elicit positive and negative emotional states, and collect recordings of the vocal expression of these emotions without human interference. In this paper, the mechatronic design and development of the feeder is described. Design choices were motivated by the desire for the hens to voluntarily interact with the feeder and experience the different stimuli that were designed to induce (1) positive low-arousal, (2) positive high-arousal, (3) negative low-arousal, and (4) negative high-arousal states. The results showed that hens were motivated to engage with the feeder despite the risk of receiving negative stimuli and that this motivation was sustained for at least 1 week. The potential of using the interactive feeder to analyse chicken vocalisations related to emotional valence and arousal is being explored, offering a novel proof of concept in animal welfare research. Preliminary findings suggest that hens vocalised in response to all four stimulus types, with the number of vocalisations, but not the probability of vocalising, distinguishing between low- and high-arousal states. Thus, the proposed animal-computer interaction design has potential to be used as an enrichment device and for future experiments on vocal emotions in birds.

Understanding the municipal-level design and adaptation of pay-for-performance schemes across two states of Brazil.

The design of complex health systems interventions, such as pay for performance (P4P), can be critical to determining such programmes' success. In P4P programmes, the design of financial incentives is crucial in shaping how these programmes work. However, the design of such schemes is usually homogenous across providers within a given scheme. Consequently, there is a limited understanding of the strengths and weaknesses of P4P design elements from the implementers' perspective. This study takes advantage of the unique context of Brazil, where municipalities adapted the federal incentive design, resulting in variations in incentive design across municipalities. The study aims to understand why municipalities in Brazil chose certain P4P design features, the associated challenges and the local adaptations made to address problems in scheme design. This study was a multiple case study design relying on qualitative data from 20 municipalities from two states in Northeastern Brazil. We conducted two key informant interviews with municipal-level stakeholders and focus group discussions with primary care providers. We also reviewed municipal Primary Care Access and Quality laws in each municipality. We found substantial variation in the design choices made by municipalities regarding 'who was incentivized', the 'payment size' and 'frequency'. Design choices affected relationships within municipalities and within teams. Challenges were chiefly associated with fairness relating to 'who received the incentive', 'what is incentivized' and the 'incentive size'. Adaptations were made to improve fairness, mostly in response to pressure from the healthcare workers. The significant variation in design choices across municipalities and providers' response to them highlights the importance of considering local context in the design and implementation of P4P schemes and ensuring flexibility to accommodate local preferences and emerging needs. Attention is needed to ensure that the choice of 'who is incentivized' and the 'size of incentives' are inclusive and fair and the allocation and 'use of funds' are transparent.

Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge–discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery’s SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.

Perspective on statistical power and equivalence tests.

The use of both sexes or genders should be considered in experimental design, analysis, and reporting. Since there is no requirement to double the sample size or to have sufficient power to study sex differences, challenges for the statistical analysis can arise. In this article, we focus on the topics of statistical power and ways to increase this power. We also discuss the choice of an appropriate design and statistical method and include a separate section on equivalence tests needed to show the absence of a relevant difference.

Integrating NLP Chatbot into Career Guidance Web Application

Artificial Intelligence (AI) is the development of computer systems that can perform tasks that typically require human intelligence. The development of an advanced web-based chatbot system involves the integration of various technologies and methodologies to create a seamless conversational interface. Leveraging HTML, CSS, and JavaScript for frontend development, alongside Python Flask framework for backend functionality, the project aims to deliver a robust and intuitive user experience. Key to the system's operation is the incorporation of PyTorch for machine learning, allowing for the implementation of sophisticated natural language processing (NLP) techniques. Through tokenization and stemming using NLTK (Natural Language Toolkit), the system enhances its understanding of user queries, enabling it to provide relevant and contextually appropriate responses. At the core of the system lies a supervised learning model trained on a custom dataset compiled from user interactions. This model utilizes bag-of-words representations and neural network architectures to predict responses based on input text. By continuously refining its predictions through feedback mechanisms, the system improves its accuracy and responsiveness over time, ensuring a more tailored and effective user experience. Voice input functionality, facilitated by speech recognition technology, further enhances user interaction by allowing users to communicate with the chatbot using spoken commands. This feature expands the accessibility and convenience of the system, catering to a broader range of users with varying preferences and abilities. In addition to its conversational capabilities, the system prioritizes security and privacy, implementing robust user authentication mechanisms and secure data storage practices. By adhering to industry best practices and standards, the project aims to instill trust and confidence in users regarding the confidentiality and integrity of their data.User interface design and usability are also key considerations in the development process, with careful attention given to creating an intuitive and visually appealing interface. Through thoughtful design choices and user feedback, the system seeks to optimize the user experience and streamline the interaction flow, ensuring that users can easily navigate and utilize its features. Overall, the project endeavors to create an intelligent and user-friendly chatbot system that enhances communication and interaction between users and technology. By leveraging advanced technologies and methodologies, the system aims to provide personalized assistance and support to users, ultimately improving productivity, efficiency, and satisfaction in various domains and applications

Techno-economic optimization of a hybrid energy system with limited grid connection in pursuit of net zero carbon emissions for New Zealand

As New Zealand moves towards net zero carbon emissions by 2050, multiple factors must be considered including increased electrical load due to electrification, variability of renewable energy generators, required storage capacity, system economics and limitations on grid transmission capacity. Complex and region-specific interactions between the various design choices involved are likely to require an understanding of a range of optimal and near-optimal designs for proposed micro-grid systems as opposed to a single optimal point. This work develops a novel multiscale optimization algorithm for optimization from a univariate capacity optimization approach to a multivariate one. This enhanced algorithm is applied to a grid-connected hybrid energy system consisting of local wind and solar generation, battery storage, and a limited grid connection for industrial and residential loads. This analysis is repeated for current 2023 and forecasted net zero 2050 grid conditions. Development of local generation allows for a 36.8% reduction of levelized cost of electricity in the 2023 case and a 38.6% reduction in the 2050 case. This results in a projected reduction of 19920 tonnes of CO2/yr. The algorithm and methodology developed are broadly applicable to optimization of next-generation energy grids.

A synthetic indicator for structuring resilient public transport operation

ABSTRACT This study introduces a synthetic resilience indicator as a unified analytical tool to evaluate public transport resilience, assessing performance changes in bus networks against random disruptions. This methodology measures the cumulative impact of disruptions, considering the network’s capacity for maintaining planned functionality (robustness and reliability) and recoverability (i.e., route diversity), factoring in network topology, passenger flow, and resource diversity. This paper analyzes the impact of disruptions at network and station levels by applying an index to six benchmark networks. Our findings reveal: (1) networks with high station connectivity maintain robustness and flow; (2) a clear link between flow reduction and alternate routes highlights route diversity’s value; (3) interplay between topological and flow-related network attributes underlines the need for an aggregated analytical approach. The proposed index can be used as an optimization goal during route network design; informing operators regarding the impact of design choices on future network properties.