Infrastructure Systems Research Articles

Enhancing System Efficiency through AI, Edge Computing, and Resource Optimization in Modern Infrastructure

This paper explores innovative strategies for enhancing system efficiency in modern infrastructure by integrating artificial intelligence (AI), edge computing, and resource optimization techniques. As the complexity of infrastructure systems increases, traditional methods often fall short in addressing the evolving demands of operational efficiency and reliability. By leveraging AI algorithms for predictive analytics and resource allocation, and utilizing edge computing for real-time data processing, organizations can significantly improve performance and responsiveness. The study examines case studies that highlight successful implementations of these technologies across various sectors, including infrastructure monitoring, and grid maintenance. Insights from this research provide a framework for practitioners to adopt these advanced methodologies, ultimately leading to more resilient and efficient infrastructure systems.

Improvement of the Surveillance Protocols After Experiencing a Pseudo-Epidemic After 2018 Central Sulawesi Earthquake in Indonesia.

Following a disaster, a pseudo-epidemic can occur due to redundant and duplicated data caused by infrastructure and information system disruptions. This study aims to investigate whether there have been improvements the post-disaster surveillance system in the comparison of diarrhea incidents between Central Sulawesi, Indonesia, and Cianjur, West Java, Indonesia. We conducted an analysis of the epidemic-prone disease diarrhea before and during disasters, comparing the data with secondary data from the Early Warning Alert and Response System (EWARS) and the District Health Information System V.02 (DHIS-2). In central Sulawesi in 2018 and Cianjur in 2022, we observed an upsurge in diarrhea cases in the first week after the disaster. Although diarrhea cases increased after the disaster, they remained within acceptable outbreak criteria. Multiplication and redundant data were detected in the DHIS-2 system in Central Sulawesi, likely leading to erroneous overreporting. Changes in surveillance officers and their personal experiences during the disaster contributed to data inconsistencies. As compared to Central Sulawesi, the DHIS-2 reporting form in Cianjur was simplified as an individual form to enhance efficiency and accuracy. Enhancing valid assessment and conducting thorough investigations are essential to improve surveillance protocols for epidemic-prone diseases following disasters.

Challenges And Strategies For Implementing Performance-Based Capitation In Indonesian Community Health Center : A Scoping Review

This study examines the implementation and challenges of performance-based capitation (KBK) at Community Health Centers (Puskesmas) in Indonesia, as a part of the National Health Insurance Program, aiming to improve the efficiency and quality of primary health services. Despite its potential, the implementation of KBK faces various challenges including inadequate human resources, limited infrastructure, and insufficient monitoring systems. The study uses a scoping review framework based on the Public Health Care Monitoring Conceptual Framework by WHO, analyzing 11 selected studies from 2021 to 2024. Findings indicate that internal factors such as management capacity, health worker competence, and organizational culture significantly impact the effectiveness of KBK. External factors like local government support, funding policies, and infrastructure availability also play crucial roles. Technological barriers, particularly in remote areas, hinder optimal policy implementation. The study emphasizes the need for clear regulations, consistent policy dissemination, and effective monitoring to ensure successful KBK implementation. Recommendations include strengthening leadership, training health workers, improving infrastructure, and enhancing digital health technology utilization. These strategies aim to optimize resource allocation, enhance service access, and improve patient satisfaction in Indonesia’s primary health care system.

GNSS interference detection: Methodology utilising ADS-B NACp indicator and GPS almanac data

Abstract The threat of GNSS interference poses a great danger to many critical infrastructure systems including air navigation. With a focus on mitigating this threat, this paper proposes a methodology for detecting GNSS interference. The methodology utilises the quality indicator NACp transmitted in ADS-B messages and GPS almanac data for interference detection. The NACp indicator enables estimation of the position error derived from GPS, which is compared with the HDOP value of the GPS satellite constellation. Based on this comparison, the developed detection algorithm determines whether the aircraft is affected by jamming. The detection methodology is evaluated on datasets obtained during deliberate experiments with GPS jamming. The proposed methodology provides a way to detect GNSS interference, facilitating mitigation of its impact on air traffic operation.

Challenges to implementing AI in health care

Abstract Germany, renowned for its robust healthcare system, finds itself at the early stages of AI integration. This is a reality for many European countries, emphasizing that, despite general technological prowess, AI adoption remains in its absolute infancy. However, in Germany there are areas where AI shows promise, notably in fields like radiology. Beyond such niche applications though, Germany grapples with a significant challenge - the scarcity of robust data essential for meaningful AI utilization, particularly in the realm of public health. While these insights are specific to the German context, understanding the intricate hurdles impeding widespread AI implementation is a meaningful takeaway for countries around the world. In order to effectively leverage new technologies, there is an acute need for advancements in data infrastructure and governance to unlock the full potential of AI in healthcare. Overcoming barriers and charting a path towards a future where AI serves as a transformative force in German healthcare, could one day lead to enhanced system efficiencies and patient experiences, but for now we have to be thoughtful about the realities of implementing AI within current existing health system infrastructure.

Life cycle sustainability assessments of critical civil infrastructure systems: additional guidance for future research and practical application

ABSTRACT Sustainable development of critical civil infrastructure systems should include economic, environmental, and social considerations, as well as considerations that are of high value to stakeholders and decision-makers. Life cycle economic and environmental impacts of critical civil infrastructure systems are commonly quantified by well-established methodologies such as life cycle assessment and life cycle costing. However, social and other related impacts of critical civil infrastructure systems can be difficult to quantify and are therefore less commonly and thoroughly addressed. A mini-literature review was conducted to explore how sustainability is currently evaluated for critical civil infrastructure systems. The review found that, while there are life cycle assessment frameworks available, there is vague guidance available for addressing factors that are not directly related to environmental, economic, or social (i.e., difficult to quantify) impacts. In response, the authors build upon currently available methodologies by discussing a supplemental approach for collecting additional data for critical civil infrastructure systems, which includes the use of stakeholder discussions and semistructured interviews. Including a range of factors early on in life cycle sustainability assessments of critical civil infrastructure can improve study robustness and allow for further integration of decision-making factors in life cycle sustainability assessments.

Strengthening anthrax outbreak response and preparedness: simulation and stakeholder education in Namisindwa district, Uganda

BackgroundAnthrax is a zoonotic disease caused by Bacillus anthracis that poses a significant threat to both human health and livestock. Effective preparedness and response to anthrax outbreak at the district level is essential to mitigate the devastating impact of the disease to humans and animals. The current diseaae surveillance in animals and humans uses two different infrastructure systems with online platform supported by established diagnostic facilities. The differences in surveillance systems affect timely outbreak response especially for zoonotic diseases like anthrax. We therefore aimed to assess the feasibility of implementing a simulation exercise for a potential anthrax outbreak in a local government setting and to raise the suspicion index of different district stakeholders for a potential anthrax outbreak in Namisindwa District, Uganda.MethodsWe conducted a field-based simulation exercise and a health education intervention using quantitative data collection methods. The study participants mainly members of the District Taskforce (DTF) were purposively selected given their role(s) in disease surveillance and response at the sub-national level. We combined 26 variables (all dichotomized) assessing knowledge on anthrax and knowledge on appropriate outbreak response measures into an additive composite index. We then dichotomized overall score based on the 80% blooms cutoff i.e. we considered those scoring at least 80% to have high knowledge, otherwise low. We then assessed the factors associated with knowledge using binary logistic regression with time as a proxy for the intervention effect. Odds ratios (ORs) and 95% Confidence intervals (95%CI) have been reported.ResultsThe overall district readiness score was 35.0% (24/69) and was deficient in the following domains: coordination and resource mobilization (5/16), surveillance (5/11), laboratory capacity (3/10), case management (4/7), risk communications (4/12), and control measures (4/13). The overall community readiness score was 7 out of 32 (22.0%). We noted poor scores of readiness in all domains except for case management (2/2). The knowledge training did not have an effect on the overall readiness score, but improved specific domains such as control measures. Instead tertiary education was the only independent predictor of higher knowledge on anthrax and how to respond to it (OR = 1.57, 95% CI = 1.07–2.31). Training did not have a significant association with overall knowledge improvement but had an effect on several individual knowledge aspects.ConclusionWe found that the district’s preparedness to respond to a potential anthrax outbreak was inadequate, especially in coordination and mobilisation, surveillance, case management, risk communication and control measures. The health education training intervention showed increased knowledge levels compared to the pre-test and post-test an indicator that the health education sessions could increase the index of suspicion. The low preparedness underscores the urgency to strengthen anthrax preparedness in the district and could have implications for other districts. We deduce that trainings of a similar nature conducted regularly and extensively would have better effects. This study’s insights are valuable for improving anthrax readiness and safeguarding public and animal health in similar settings.

Multiple points of system failure underpin continuous subcutaneous infusion safety incidents in palliative care: A mixed methods analysis.

About 25% of palliative medication incidents involve continuous subcutaneous infusions. Complex structural and human factor issues make these risk-prone interventions. Detailed analysis of how this safety-critical care can be improved has not been undertaken. Understanding context, contributory factors and events leading to incidents is essential. (1) Understand continuous subcutaneous infusion safety incidents and their impact on patients and families; (2) Identify targets for system improvements by learning from recurrent events and contributory factors. Following systematic identification and stratification by degree of harm, a mixed methods analysis of palliative medication incidents involving continuous subcutaneous infusions comprising quantitative descriptive analysis using the PatIent SAfety (PISA) classification system and qualitative narrative analysis of free-text reports. Palliative medication incidents (n = 7506) reported to the National Reporting and Learning System, England and Wales (2016-2021). About 1317/7506 incidents involved continuous subcutaneous infusions with 943 (72%) detailing harms. Primary incidents (most proximal to patient outcomes) leading to inappropriate medication use (including not using medication when it was needed) were underpinned by breakdowns in three major medication processes: monitoring and supply (405, 31%), administration (383, 29%) and prescribing (268, 20%). Recurring contributory factors included discontinuity of care within and between settings, inadequate time, inadequate staffing and unfamiliarity with protocols. Psychological harms for patients and families were identified. System infrastructure is needed to enable timely supply of medication and equipment, effective coordinated use of continuous subcutaneous infusions, communication and continuity of care. Training is needed to improve incident descriptions so these pinpoint precise targets for safer care.

Perencanaan Green Infrastructure pada Kawasan Transit Oriented Development (TOD) Lebak Bulus, Jakarta Selatan

Transit Oriented Development (TOD) approach is one of the solutions adapted by DKI Jakarta’s Government in order to create a walkable and mass transportation friendly environment in Jakarta. Lebak Bulus TOD in its development will use environmental sustainability approach in order to reduce negative impact from urban development. However, Lebak Bulus TOD is currently lacked of green open space and dominated with impermeable surface that affect the increase of the stormwater runoff. Implementing green infrastructure concept in planning Lebak Bulus TOD site is one of the solution to overcome the problems. This research is aimed to reduce stormwater runoff and pollution in Pasar Jumat Street, Lebak Bulus Raya Street, and RA Kartini Street located in Lebak Bulus TOD. The methods used in this research are descriptive, quantitative analysis, and spatial analysis. Quantitative analysis is used to calculate water quality volume for each location, the results for each location respectively are 832,56 m3, 205,44 m3, dan 205,7 m3. Green infrastructure system used are stormwater tree trench, stormwater planter, green gutter, permeable pavement, and green wall. 285,95 m3 of stormwater runoff can be reduced by the implementation of green infrastructure. Spatial analysis using CITYGreen shows that the planning can reduce pollution by 48,83 kg/year with economic value of $263,49 (Rp3.751.834,11) and reduce runoff volume by 51% with economic value of $2.408,67 (Rp34.348.838).

Cyber-Physical Systems and IoT: Transforming Smart Cities for Sustainable Development

Smart cities CPS/ IoT are impacting sustainable development in modern cities. This research explore how CPS and IoT integration into infrastructure systems in cities can improve sustainability in aspects such as energy, waste, and environment. Finally, taking quantitative data on energy and pollution saving and resource utilization from the smart cities around the world, the paper quantifies the case analysis. The research method entails retrieving data from existing smart city projects and then using statistical tools to evaluate the differences in sustainability targets with and without CPS and IoT integration. The main discoveries indicate that places implementing CPS and IoT technologies have recorded reduction in energy use by up to 25 % and enhanced waste management by up to 30% thus exhibiting a reduced carbon footprint. The originality of this research is based on the application of CPS- IoT in analysing their practical implications for urban sustainability with relevant recommendations for policymakers, urban planners and technologists. This study not only serves as the research for the academic literature but also advises cities into how they would be able to build upon their sustainable initiatives through the adoption of advanced technology. The outcomes underscore the possibility of the strategic application of CPS and IoT to enhance the creation of smarter urban contexts.

Application of intelligent self-organizing algorithms in UAV cooperative inspection of power distribution networks

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies.

Sustainable Development in Rural Underserved Communities through Improved Responsible Management of Decentralized Wastewater Infrastructure: A Focus on the Alabama Black Belt.

Despite global efforts on meeting sustainable development goals by 2030, persistent and widespread sanitation deficits in rural, underserved communities in high-income countries─including the United States (US)─challenge achieving this target. The recent US federal infrastructure funding, coupled with research efforts to explore innovative, alternative decentralized wastewater systems, are unprecedented opportunities for addressing basic sanitation gaps in these communities. Yet, understanding how to best manage these systems for sustainable operations and maintenance (O&M) is still a national need. Here, we develop an integrated management approach for achieving such sustainable systems, taking into account the utility structure, operational aspects, and possible barriers impeding effective management of decentralized wastewater infrastructure. We demonstrate this approach through a binomial logistic regression of survey responses from 114 public and private management entities (e.g., water and sewer utilities) operating in 27 states in the US, targeting the rural Alabama Black Belt wastewater issues. Our assessment introduces policy areas that support sustainable decentralized wastewater systems management and operations, including privatizing water-wastewater infrastructure systems, incentivizing/mandating the consolidation of utility management of these systems, federally funding the O&M, and developing and retaining water-wastewater workforce in rural, underserved communities. Our discussions give rise to a holistic empirical understanding of effective management of decentralized wastewater infrastructure for rural, underserved communities in the US, thereby contributing to global conversations on sustainable development.

A secure and efficient data aggregation scheme for cloud–edge collaborative smart meters

Smart meters are part of the Advanced Measurement Infrastructure (AMI) system in the smart grid. It facilitates data transfer between consumers and electricity suppliers (ES). However, the mass deployment of smart meters (SM) brings heavy overhead to grid operation and poses serious privacy threats. To this end, this paper proposes a secure and efficient data aggregation scheme of cloud–edge collaboration smart meters. At first, we standardize the users’ historical electricity load features and use the improved K-Means clustering algorithm to calculate the Euclidean distance between feature vectors to obtain the classification results of users’ load features. On this basis, ES generates relevant parameters to encrypt meter data and protect users’ data privacy based on classification results. The aggregator (Ag) performs the data aggregation, generates the overall signature using the Schnorr aggregation signature method, and sends it to the cloud server (CS). The ES queries the CS to obtain data and parses it to realize the customer billing service. Meanwhile, this paper executes a series of experiments, and the results show that the proposed scheme exhibits significant advantages in privacy protection and system operation efficiency.

Localization of underground pipeline intrusion sources using cross-correlation CNN: application in pile-driving model test

Preserving the structural integrity of critical infrastructure systems necessitates a heightened focus on fortifying the protection of underground pipelines. To this end, this paper presents an innovative approach, namely the Multi-Sample Joint Localization Method (MSJLM) utilizing Cross-Correlation Convolutional Neural Networks (CC-CNN), aimed at precisely localizing intrusion sources in the vicinity of underground pipelines. Traditional techniques for detecting and pinpointing pipeline intrusions primarily rely on a single sensor monitoring point, which is susceptible to inherent errors and constraints. In contrast, the MSJLM proposed in this study leverages data from multiple samples, integrating diverse data sources through correlation analyses to elevate precision and reliability. The utilization of the CC-CNN framework for processing aggregated data has proven highly successful in extracting spatial features and identifying patterns. Furthermore, the effectiveness of this method is corroborated through validation via a pile-driving model test.

Process Approach in a Mining Company: LW Bogdanka S.A. Case Study

This article concerns the management of the Polish mining company Lubelski Węgiel (LW) Bogdanka S.A. The main aim of this paper was to describe the whole process of implementing a process approach to management in one of the most important mining companies in Poland. The objective of the research presented was the incorporation of the process approach in LW Bogdanka S.A. to enhance its management and to improve the effectiveness of its mining process. This paper uses a case study method and presents a practical incorporation of the process approach and its results. The first phase of the proposed solution included building a map of business processes. Then, the research consisted of an analysis of existing processes; the introduction of new solutions, mainly in the field of infrastructure and IT systems; and changing the awareness of key process owners. The motivation for applying this approach was to achieve greater efficiency and continuous improvement in LW Bogdanka S.A. The main results and achievements were not merely limited to the development of a map of processes and subprocesses, but also led to an improvement in organizational structure, changes in approach to company management, a diagnosis of problems in the most important company departments, and the proposal of solutions to them.

Multi-Scale Risk-Informed Comprehensive Assessment Methodology for Lithium-Ion Battery Energy Storage System

Lithium-ion batteries (LIB) are prone to thermal runaway, which can potentially result in serious incidents. These challenges are more prominent in large-scale lithium-ion battery energy storage system (Li-BESS) infrastructures. The conventional risk assessment method has a limited perspective, resulting in inadequately comprehensive evaluation outcomes, which impedes the provision of dependable technical support for the scientific appraisal of intricate large-scale Li-BESS systems. This study presents a novel Li-BESS-oriented multi-scale risk-informed comprehensive assessment framework, realizing the seamless transmission of assessment information across various scales. The findings from a previous smaller-scale analysis serve as inputs for a larger scale. The evaluation process of this method is more scientifically rigorous and yields more comprehensive results compared to assessment technologies just relying on a single perspective. By utilizing the proposed comprehensive assessment methodology, this study utilized the emergency power supply of nuclear power plants (NPPs) as an application scenario, demonstrating the complete implementation process of the framework and conducting a comprehensive assessment of Li-BESS feasibility as an emergency power source for NPPs. Our findings propose a novel paradigm for the comprehensive assessment of Li-BESS, which is expected to serve as a scientific foundation for decision-making and technical guidance in practical applications.

A comprehensive framework for circular product-service systems in infrastructure: Enhancing customer-contractor collaboration

Product-service systems (PSS) represent a business model that increases material decoupling and decreases environmental risks while providing customer value. PSS can help realize a more sustainable construction industry, which remains among the largest polluting and waste-generating sectors. Systemic change in the infrastructure sector requires client involvement, which is represented by the government. However, establishing circular PSS is challenging due to the complexities, which need to combine the lifecycle approach, customer-client aspects, new contracting, knowledge transfer, technology, and PSS-specific aspects. It requires ex-ante evaluation of real case studies to increase knowledge and understanding. This article presents a first-of-its-kind framework for infrastructure based on the current literature and analysis of five infrastructure pilots in the Netherlands (bridge deck, digital road lights, guide rails, roads). The final multistakeholder integrated circular PSS framework includes i) lifecycle perspective and circularity, a) materials and b) management, ii) customer-contractor relationship including a) customer-perspective b) co-creation aspects, c) client-customer hierarchy, iii) technology perspective (functions and resources) iv) business aspects such as a) network and b) value creation and retention. The framework allows customer-contractor communication and can serve as decision support. It is applicable to circular PSS where the customer has more involvement in the formulation of PSS.

An in-depth exploration of e-learning experiences at the University of Venda amid the pandemic

The COVID-19 pandemic necessitated significant adjustments across various sectors globally, and the education sector was no exception. To adhere to social distancing requirements and ensure continuity in education, E-learning emerged as a viable solution, offering a glimmer of hope in these challenging times. However, this strategy brought its own set of challenges. This study aimed to explore the experiences of students with E-learning, particularly focusing on the obstacles and benefits encountered. A qualitative approach was employed, utilizing an exploratory research design to gather comprehensive insights. Participants were selected through purposive sampling, focusing on five University of Venda students registered for the 2021 academic year. Data collection was conducted via one-to-one semi-structured interviews, which allowed for detailed and nuanced responses. Thematic analysis was used to analyse the collected data, identifying key themes and patterns in the students' experiences. The findings highlighted various challenges, such as limited access to reliable internet, lack of digital devices, and difficulties in maintaining motivation and engagement in a virtual learning environment. Despite these challenges, some students appreciated the flexibility and convenience offered by E-learning, highlighting its potential. The study underscores the need for improved infrastructure and support systems to enhance the E-learning experience. These insights can inform policymakers and educational institutions in developing more effective strategies to support students in a digital learning landscape, ensuring that the benefits of E-learning are maximized while minimizing its drawbacks. The findings contribute to the broader discourse on the adaptation of education systems in response to global crises, emphasizing the importance of resilience and adaptability in educational practices.

Management and Performance Program Chatbot: A Use Case of Large Language Model in the Federal Public Sector in Brazil

Governments are facing the benefits and perils of recent developments in artificial intelligence. One key objective is using AI to enhance the efficiency and effectiveness of their public service provision. While academic research has examined these advances, there is a lack of empirical evidence regarding user experiences. This paper explores the initial results of using a chatbot based on a Large Language Model (LLM) to address user queries about the Management and Performance Program (PGD). Survey analysis demonstrated that ChatPGD is likely to reduce program-related response time and increase effectiveness. However, the initial tests indicated a need to refine response algorithms to detect and mitigate potential biases and to strengthen the system infrastructure to support new enhancements. This paper examines the chatbot's application in harnessing government databases, highlighting the potential benefits, challenges, and perspectives of embedding AI and LLMs in public service delivery. The insights can support the development of other public service LLMs.

Place-based and sectoral patterns in urban experimentation: Implications for deep transitions research

Transforming critical infrastructure systems, such as water and energy, is crucial to achieving global sustainability and climate change targets in many cities. Whilst experimentation has been studied extensively in urban sustainability scholarships, there have been no large-N cross-sector comparative studies. Existing research is potentially blind to different patterns of urban experiments across multiple sectors. This is particularly relevant to advancing deep transitions thinking, which has increasingly foregrounded the notion of multi-system alignment across socio-technical domains. Our research aims to fill this knowledge gap using a database to characterise urban experiments across water and energy domains while integrating sectoral and place-based perspectives. We analysed 40 experiments across Melbourne and Adelaide, Australia. Our results show that on a collective level, these experiments skew towards technological interventions, while their transfer and impact trajectories are underpinned by distinct territorial and sectoral logics. We show that cross-sectoral analysis can reveal plurality in urban experiments across multiple systems and places while offering a more refined understanding of multi-system alignment requirements for deep transitions.