Systems Methodology Research Articles

Modeling and scenario building for climate change adaptation planning: The case of large mining in Chile

The mining sector in Chile is a strategic industrial sector for the country. Existing evidence shows that it faces several serious climate change threats; precipitation and flooding, droughts, heatwaves, among others. This scene put pressure on the government and the industry to develop efficient adaptation plans. To date, adaptation plans are designed under the rather linear paradigm of predict-then-act or the impact-lea approach as they are characterized in the IPCC Assessment Report 5 (AR5). Literature and the IPCC reports have identified the limits of those planning paradigms showing the relevance of adaptation barriers or enabling conditions, which should be considered as intrinsic part of the planning problem.In methodological terms, planning for climate change risks implies an enriched adaptation plan problem, previously characterized only by an operative climate risk management, that must be described at the beginning of the decision-making process. In this study, our objective is to contribute to the climate change adaptation planning of large-scale mining in Chile. The study is based on a climate change adaptation planning approach that overcomes the limitations of the current paradigm. In doing so, we start from the understanding that what emerges as an object of analysis from adding to the climate risks their enabling managing conditions is a social system. The social system whose function is that social climate risk management takes place. Therefore, we call it the Social Management System for Adaptation to Climate Change (SMSACC). As such the SMSACC should be the adaptation planning key object.In the first place we modeled that system applying a qualitative system methodology and then we developed it into a mathematical model based on graph theory, in particular the signed digraphs. This allows us to simulate two types of intervention on the enlarged object of analysis of the large mining adaptation plan. On the one hand, we carried out a future scenario analysis based on prospective tools which enables us to understand the system's answer to different future behavior of its environment, including climate change. On the other hand, we simulate different strategic interventions options on the system, which facilitates understanding the system's reaction under different public policy approaches.The modeling and simulation results provided an insightful understanding of the dilemma of social adaptation management of large mining in Chile, and as such they are useful input for the planning process.

7234 Creation Of The Minimum Dataset Required For Monitoring The Safety And Efficacy of Growth Hormone Therapy in Children with Noonan Syndrome - A GloBE-Reg Initiative

Abstract Disclosure: S. Chen: Research Investigator; Self; Pfizer, Inc.. J. Bryce: None. M. Chen: None. G. Binder: Advisory Board Member; Self; Ascendis, Ferring Pharmaceuticals, Ipsen, Novo Nordisk, Pfizer, Inc., Sandoz. Grant Recipient; Self; Novo Nordisk. Speaker; Self; Ferring Pharmaceuticals, Ipsen, Lilly USA, LLC, Merck, Novo Nordisk, Pfizer, Inc., Sandoz. C.S. Choong: None. T. Edouard: Advisory Board Member; Self; Biomarin, Novo Nordisk. Speaker; Self; Biomarin, Novo Nordisk. A. Fu: None. R. Horikawa: None. A. Hubbard: None. J. Lebl: Speaker; Self; Pfizer, Inc., Novo Nordisk, Merck. L. Mazzanti: Speaker; Self; Novo Nordisk, Pfizer, Inc., Sandoz. A. Romano: Consulting Fee; Self; Novo Nordisk. Speaker; Self; Novo Nordisk. M. Shaikh: Consulting Fee; Self; Novo Nordisk, Pfizer, Inc.. F. Tamburrino: None. S. Tamponi: None. P. Yger: None. S. Ahmed: Grant Recipient; Self; Novo Nordisk, Pfizer, Inc., GenSci. Speaker; Self; Ipsen. A. Jorge: Grant Recipient; Self; Biomarin. Research Investigator; Self; Novo Nordisk. Speaker; Self; Novo Nordisk, Pfizer, Inc. Introduction: Recombinant human growth hormone (rhGH) is approved internationally for use in Noonan syndrome (NS); initially by the FDA in 2007 and most recently in Europe in 2020. Most studies have shown benefit in linear growth and adult height associated with rhGH therapy, although response to treatment often varies, and longer-term safety data is limited. Moreover, comparisons between studies are compromised by the lack of consistency in data collection. The objective of the current study was to identify the minimum dataset (MDS) that could be monitored and reported globally in a routine clinical setting. Methods: This study was undertaken by the NS Expert Working Group in GloBE-Reg, the Global Registry for Novel Therapies in Rare Bone and Endocrine Conditions (https://globe-reg.net/). Ten international clinical experts in NS from ten different countries and three patient representatives of patient organisations collaborated to develop this recommendation, based on a previously described and published methodology for the grading system to determine if an item fulfils the criteria for the MDS rhGH in childhood onset GH deficiency (GHD) (doi: 10.1159/000533763). Data fields that achieved >70% consensus in terms of importance qualified for the MDS, provided <50% of the group deemed the item difficult to collect. Results: Preparation for the development of the finalised MDS occurred over 4 months, including two teleconference meetings between the group members and a final consensus meeting also held virtually. A total of 256 items were compiled and 9 removed for redundancies, with 247 items subjected to the grading system. Of these, 169 items achieved at least 70% consensus as important data to collect and a total of 169, albeit different items, were deemed easy to collect when monitoring children with NS on rhGH. Combining the criteria of importance and ease of data collection, 124 fulfilled the criteria for the MDS. Six items were designated as core data, 2 were computed fields, 10 items were removed following detailed discussion and 48 items were merged, to produce the final MDS recommendation of 57 items which consisted of 20 items that were only required to be completed once, and the rest at least once annually. Importantly, the exercise also showed that the MDS for monitoring of safety and efficacy of rhGH therapy differs for the different licensed indications for rhGH use, as the need for growth hormone stimulation tests and information on reinitiation after final adult height was not included in the NS MDS in contrast to the MDS for GHD. Conclusion: In summary, this exercise performed through the GloBE-Reg initiative provides a recommendation of the MDS requirement that should be collected as part of routine clinical practice, for the reporting of core outcomes that relate to safety and effectiveness of all forms of rhGH in children with NS. Presentation: 6/3/2024

Automotive powertrain electrification: system methodology to guarantee acoustic comfort

For the past few years, electrification has disrupted the automotive industry. Surprisingly, Noise Vibration and Harshness (NVH) became a major issue as it is located in high frequencies (1 to 5 kHz). The noise has 2 origins: the electromagnetic forces in the electric motor and the gear mesh forces in the reducer. The first part focuses on the methodology developed in collaboration with car-makers in order to integrate the powertrain in a satisfying way in the vehicle. First, this process is focused on the structure-borne noise. Attention is paid on rubber mounts and on their fixation points. Second, the airborne transfer may become an issue. Despite the acoustic power requirement provided, the final vehicle performance is not guaranteed. Examples will illustrate these important topics. The second part pays attention to the prediction of reducer noise. Automotive stakeholders are approaching the gear mesh source by approximate linear tools. Recent works have shown that this methodology has limitations and leads to inaccurate results. This may be a major concern. A few examples will illustrate this risk and show that further simulation developments are necessary.

Numerical validation of a simplified source-receiver mobility approach for estimating building floor velocity responses to railway vibration

Prediction methods are required for designing buildings to protect inhabitants against ground-borne (structure-borne) sound from railway lines or other outdoor ground vibration sources. There is no European standard for such predictions yet, but a European task group (CEN/TC126/WG2/TG1) is now doing preliminary work on this subject. A promising approach, based on the source-receiver vibration system methodology (the building foundations being the source and the building upper-structure the receiver) and using a mobility approach, has been identified and presented at Forum Acusticum in 2023. In the present paper, the approach is numerically validated in a simple 2.5 D configuration (3D but infinite in one direction) of a building along a railway line. The floor velocity responses are estimated first using a numerical BEM model taken as reference model and then using a simplified mobility approach, where railway excitation is expressed as a line of uncorrelated forces applied to ground and the source-receiver mobility system expressed using effective mobilities (velocity response to a line of uncorrelated forces). The two floor velocity responses obtained are compared and the assumptions required for easily solving the systems discussed.

Deep learning-powered visual place recognition for enhanced mobile multimedia communication in autonomous transport systems

The progress of autonomous transport systems (ATS) involves efficient multimedia communication for real-time data tradeoffs and environmental issues. Deep learning (DL) powered visual place recognition (VPR) was developed as an effective tool to improve mobile multimedia communication in ATS. VPR relates to the capability of a method or device to recognize and identify particular places or locations from the visual scene. This procedure involves inspecting visual data, like images or video frames, to control the unique features or features connected with diverse locations. By leveraging camera sensors, VPR allows vehicles to detect their surroundings, enabling context-aware communication and enhancing the entire system's performance. DL-empowered VPR offers a transformative manner to improve mobile multimedia communication in ATS. By identifying and understanding their situation, autonomous vehicles can communicate most effectively and operate reliably and safely, paving the way for a future characterized by seamless and intelligent transportation. This article develops a novel Deep Learning-Powered Visual Place Recognition for Enhanced Multimedia Communication in Autonomous Transport Systems (DLVPR-MCATS) methodology. The main aim of the DLVPR-MCATS methodology is to recognize visual places or not utilize optimal DL approaches. For this purpose, the DLVPR-MCATS approach utilizes a bilateral filtering (BF) based preprocessing model. For the feature fusion model, the DLVPR-MCATS approach follows three models: residual network (ResNet), EfficientNet, and MobileNetv2. Moreover, the hyperparameter tuning method uses the Harris Hawks Optimization (HHO) model. Finally, the bidirectional long short-term memory (BiLSTM) technique is implemented to recognize visual places. A wide range of simulations is executed to validate the solution of the DLVPR-MCATS method. The experimental validation of the DLVPR-MCATS method portrayed a superior performance over other models concerning various aspects.

Managing Organizational Culture Integration Post-Acquisition: Lessons from PT 'X' in the Oil and Gas Sector

In the upstream oil and gas industry, mergers and acquisitions (M&A) are often used to enhance access to limited resources and improve competitiveness in the global market. However, a major challenge arises in post-acquisition organizational cultural integration, which can impede the success of this process. At PT "X," the acquisition of PT "Y" and PT "Z" highlighted a cultural gap between a company oriented towards familial values and companies with more professional and structured cultures. Although numerous studies have addressed the importance of cultural integration in M&A, few have delved deeply into cases in Indonesia's oil and gas sector. This research employs a qualitative descriptive methodology with a Soft System Methodology (SSM) approach. The findings reveal that cultural differences, particularly in communication styles and work systems, led to tensions and decreased performance. The study further indicates that the lack of cultural assessment in the early stages of the acquisition exacerbated these issues. The research implications underscore the need for a more systematic and planned cultural integration strategy to ensure post-acquisition success.

Towards Integrated Safety Assurance Methodology for Autonomous Vessel Navigation Systems

Abstract Safety assurance remains paramount for the realization of autonomous vessels. A robust assurance methodology that can provide traceability throughout the design and verification process is necessary to demonstrate safety equivalence to that of conventional ships. However, there are few references that propose a holistic safety assurance scheme for autonomous ships, using actual engineering processes of ships as examples. This study proposes a design and development method for an autonomous navigation system that integrates Model-Based Systems Engineering (MBSE), System Theoretic Process Analysis (STPA), Failure Modes and Effects Analysis (FMEA), and several verification methods including simulation-based tests to develop a comprehensive safety assurance scheme. This safety assurance scheme is being conducted as a case study for a newly constructed autonomous vessel. First, STPA can be performed on the conceptual design established by MBSE to extract additional safety requirements from the identified loss scenarios. Focusing on the process model in the loss scenario description leads to a deeper understanding of the system behavior. FMEA in addition identify potential component failure modes and their impact on system safety. The multi-level requirements that emerge from these activities are validated in test scenarios. Simulators are used to evaluate whether the autonomous navigation system’s safety can meet predefined pass criteria in some of the scenarios. These activities ensure traceability from safety goals to design decisions, allowing design changes and their impact on safety to be evaluated throughout the development lifecycle and allowing for more systematic ongoing monitoring during operations.

Hydraulic valve design methodology for hydro turbine control system

The control of the turbine and its equipment in a hydroelectric power plant requires the HPU (hydraulic power unit) to deliver large volumes of working fluid in a short time at specific optimum control parameters. The use of typical proportional flow control valves created by manufacturers of hydraulic components in low-pressure control systems is disadvantageous due to high pressure losses in the control chambers. This paper presents the methodology and test results while designing a hydraulic proportional valve for low pressure and high flow rate operation. CFD (computational fluid dynamics) theory was analyzed and characteristic values were determined. The validation of CFD tests through those on the test bench was carried out, correction factor was determined. A new proprietary solution was developed and a series of simulation studies were carried out to determine the flow characteristics depending on the degree of the opening of the proportional flow control valve.

Revolutionizing efficiency: The path to sustainable oilfield services in Indonesia's energy landscape – The case of Pertamina drilling services

PurposeThis study explores strategic and technological approaches for sustainable oil production in Indonesia, focusing on policy frameworks, eco-innovation, and supply chain resilience to align with national and global standards. It also analyzes the organizational transition of Pertamina Drilling Service Indonesia (PDSI) to better navigate industry challenges with the outcome of a framework sustainable practices. Design/methodology/approachUtilizing Soft Systems Methodology (SSM) to analyze primary qualitative data, the research examines PDSI's adaptation to depict the current business environment and challenges both domestically and globally, addressing industry demands. It focuses on operational objectives, strategic asset management, regulatory compliance, and market positioning. SSM offers a comprehensive framework for understanding and modeling the organizational changes within PDSI. FindingsThe transition to a functional organizational structure has led to enhanced operational efficiency, clearer role definition, and improved employee retention and development strategies at PDSI. The study also highlights the industry's challenges, including high risks, capital intensity, environmental concerns, and fluctuating oil prices, and how PDSI is strategically responding to these challenges. Research limitations/implicationsThe study faces limitations due to reliance on specific data sources and the inherent complexity of the oil and gas sector. However, it offers valuable insights into the dynamics of organizational change in a high-risk industry. Practical implicationsThe findings provide a roadmap for PDSI in addressing operational efficiency, human resource management, and organizational agility. This includes aligning business expansion with environmental sustainability and enhancing competitiveness through technological innovation. Social implicationsBy focusing on strategic employee development and retention, the study underscores the importance of human capital in the oil and gas industry, impacting social aspects of business operations. Originality/valueThis research contributes to the understanding of organizational transitions in the oil and gas sector. It offers a unique perspective on how PDSI can achieve sustainable growth and industry leadership through a functional organizational structure, underlined by a comprehensive framework emphasizing efficiency, adaptability, and continuous improvement.

A novel advanced block assembly method with 3D printed channel systems

Modular construction offers economic and environmental advantages over conventional construction technologies. Although several researchers have introduced various modular construction systems, the methods for creating channel structures within block modules and injecting epoxy resin to connect blocks efficiently have not been fully explored. This study proposes a new assembly channel system for modular construction using 3D printing. Four different channel types are fabricated using 3D printing, and epoxy resin is injected for block connections. The relationships between channel geometries and structural resistance are thoroughly analyzed with the uniaxial tensile and three-point bending tests. Both experiments are also numerically simulated using the finite element method to validate the modeling methodology for assembly channel systems. The validated methodology is useful for evaluating assembly channel systems and enhancing the design of such systems. Overall, this study can foster the practical application of modularization across various industrial fields.

Comprehensive groundwater potential mapping in Nagaland, India using integrated geo-spatial techniques

Groundwater is essential for a sustainable future, yet identifying potential groundwater zones remains challenging in regions with varied hydro-geological conditions. Nagaland is a state in north-eastern India characterized by diverse terrain, complex geological formations, and seasonal variations in rainfall. The objective of the present study is to evaluate groundwater potential zones in Nagaland using an integrated Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) methodology, resulting in the first comprehensive map addressing the region's diverse hydro-geological conditions. Several attributes like geomorphology, geology, slope, soil, rainfall, drainage density, and Land use/Land cover (LU/LC) were generated from available data resources like Landsat 8 and Sentinel-2 satellite imageries, the Geological Survey of India (GSI), Environmental Systems Research Institute (ESRI), the Food and Agriculture Organization (FAO), and the Climatic Research Unit (CRU). These datasets are extracted using the Arc-GIS (Arc-GIS Desktop 10.8.1), with weights assigned according to Saaty's scale, and subsequently normalized through the AHP. The findings reveal that 14% (2240km2) of the state has very good groundwater potential, 42% (6720 km2) is classified as good, 9% (1440 km2) as moderate, 27% (4320 km2) as poor, and 8% (1280 km2) as very poor. These insights are significant for regions like Mokokchung, Longleng, and Wokha, which require urgent intervention. Conversely, the study identifies Dimapur and Chumoukedima as areas with significant groundwater potential, capable of meeting rising water demands. The validation of these results using well discharge data from 12 monitoring wells supports these findings.

Алгоритмизация управленческих процессов медицинской организации для создания цифровой модели функционального прототипа регионального проекта в отрасли здравоохранения.

Currently, the issues of algorithmization of various systems, objects, phenomena and processes for their subsequent digitalization are relevant. The essence of this approach is associated with the active development of information technology. The purpose of the study is to identify processes (algorithms) for preparing and creating a digital model of a project prototype, starting with an examination of the object (managerial processes of the project activity of a medical organization) in all functional states under the influence of external factors and the existing communication environment of the system. Materials and methods. The theoretical basis of the review was the scientific works of Russian and foreign researchers devoted to the study of the processes of algorithmization, prototyping and mathematical modeling. More than 30 literature sources were analyzed. The research methods were: system analysis, comparison, systemic and integrated approaches, as well as the method of problem modeling and abstraction. Results. The project-oriented orientation of the healthcare industry dictates the need to develop a practical system methodology and a holistic approach to the creation of digital prototypes of medical organization projects, which will rationalize the constant search for new management models to optimize project processes. The authors made the assumption that in order to visualize design processes by selecting optimal variants of prototype models, it is possible to create a digital prototype by algorithmizing management processes. To visualize the process algorithms (mechanisms) of the prototype(s) of project management processes, researchers proposed using a matrix that allows one to correlate the formalized processes of the project model with the processes of system functioning through the stages of the project life cycle. Conclusions. The transfer of management activities into a digital format implies clear formalization and codification using pre-formed prototypes of standard management processes for further digitization and development of digital (native) prototypes. In the next article, the authors plan to analyze in detail the matrix output data obtained from the formalization of project management processes in order to obtain information that complements the results of full-scale tests of a real system, and to obtain forecasts for the evolution of the project system over time.

A Cross-Layer Approach to Analyzing Energy Consumption and Lifetime of a Wireless Sensor Node

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error.

The Art of Soft Systems Inquiry: Retracing the Footsteps of Churchman and Checkland

AbstractIn this paper I reflect upon soft-systems inquiry. To do this I revisit Churchman’s discourse on the philosophical ideas underpinning inquiring systems and then discuss the practical application of soft inquiry pioneered by Checkland. This body of work suggested ontological models of organisational behavior were inappropriate and only by adopting an epistemological perspective would provide the basis for understanding complex systems that characterise human society. These ideas motivated a new way of addressing complexity. This work created a difficulty for action research since to be faithful to this discovery meant that any method of inquiry should be one that could cope with such complexity. One outcome was Soft Systems Methodology (SSM). Paradoxically the success of SSM has restrained further discussion of these ideas. The purpose of this paper is to revisit the ideas of Churchman and Checkland as a means of returning to first principles. In this paper I reassess their ideas and those of Husserl and Gadamer and suggest there is more to be gained from this rich seam of systems thinking.

One Health Approach Necessity in the Control of Highly Pathogenic Avian Influenza: An Integrated Analysis of Decision Makers’ Perspectives in Ivory Coast, Using Peter Checkland’s Soft System Methodology

Abstract The control of highly pathogenic avian influenza (HPAI) requires an integrated strategy. In Ivory Coast, it is decentralized and included in the health policy (as all tools, including SOPs, for managing disease prevention and control) but would lack a common vision and stable bridges for intersectoral exchanges and major health challenges joint planning. Thus the need to identify levers for action through the key sectoral decision makers’ perspectives in integrated HPAI control. A cross-sectional retrospective qualitative study, respecting ethical considerations, was carried out in May 2023 with national decision makers from the human, animal, environmental and community health sectors in Ivory Coast. Purposive sampling was guided by seven respondents based on the saturation principle. An open-ended, semi-structured interview grid based on the CATWOE components ( C : Clients, A : Actors, T : Transformation process, W : Weltranschauung, O : Owners, E : Environment; Smyth D. and Checkland P. en 1975) were used to analyse the surveyed decision makers’ perspectives, using QDA Miner4Lite software. Findings showed four emerging perspectives, depending on the decision makers’ profile: (i) Veterinarians advocated a world better prepared to deal with HPAI, with simulation exercises (as one of the four components of the International Health Regulations monitoring framework to enable the lessons learned from a simulated public health event that can be capitalized on while strengthening the response system), joint investigations and training, creation of reactive and autonomous regional centres with enhanced technical facilities; (ii) Poultry industry (private sector) is also moving in the same direction, with the idea of common funds pre-mobilized through poultry farmer contributions to facilitate future compensation; (iii) Physicians prioritized the cross-sector sharing of digitized information in real time, the mutualization of resources and the integration of existing surveillance networks; and (iv) Environmentalists were keen to integrate aspects of other key sectors into their sectoral monitoring tools in order to be more alert and proactive in an ecological dynamic. Information © The Authors 2024

Evaluation of advanced control strategies for PMSG in wind turbines: backstepping fuzzy logic control and adaptive neural network control approaches

This study presents a comparative analysis of two advanced control strategies for Permanent Magnet Synchronous Generators (PMSGs) in wind turbines: Backstepping Fuzzy Logic Control and Adaptive Neural Network Control. The aim is to evaluate their effectiveness in handling structured and unstructured uncertainties and enhancing system performance. Backstepping Fuzzy Logic Control integrates backstepping techniques with fuzzy logic regulation to improve flexibility and robustness. This method addresses the limitations of traditional PI controllers by enhancing adaptability to parameter variations and external disturbances. In contrast, Adaptive Neural Network Control employs neural networks with dynamic compensators to address high uncertainties and nonlinearities, aiming to surpass the performance of conventional PI controllers. The study involves simulations to compare these control strategies based on several criteria: robustness in managing parameter changes and external disturbances, flexibility in adapting to dynamic conditions, and overall performance including efficiency and stability. Simulation results indicate that both strategies offer significant improvements over traditional PI controllers. Backstepping Fuzzy Logic Control demonstrates strong adaptability and robustness, while Adaptive Neural Network Control shows superior efficiency and optimality, particularly in high-uncertainty environments. This comparative analysis provides insights into the strengths and limitations of each approach, offering guidance for selecting the most suitable control strategy based on specific operational requirements. The findings contribute to advancing control methodologies in wind energy systems and suggest directions for future research and practical implementation.

Combating COVID-19 health disparities in Black, Indigenous, and People of Colour Communities-A call for critical systems thinking.

The Black, Indigenous, and People of Colour populations in the United States are disproportionately affected by the emerging health threat SARS-CoV-2, which causes COVID-19. This paper aims to demonstrate the usefulness of critical systems thinking by using scenario planning based on epidemiological data and tying epidemiology with soft systems methodology to investigate COVID-19 disparities among disproportionately affected Black, Indigenous, and People of Colour populations. Using a review of the COVID-19 literature and publicly available US COVID-19 data, critical systems thinking is applied in a scenario planning example and a call to link soft systems methodology with epidemiology. According to the four plausible Endgame scenarios, levels of community transmission as well as the current state transmission are based on the driving forces of the scenarios. In addition, soft systems methodology explores the effect on stakeholders and strengthens the picture of disease burden beyond sole reliance on traditional data sources. This analysis underscores employing critical systems thinking to critically assess diverse methods appropriate for the ongoing complexity of global crises. It is argued that critically engaged subjectivity should be given space alongside data-dependent objectivity. COVID-19 disparities are reliant on the social determinants of health's effects as driving forces on disease transmission in Black, Indigenous, and People of Colour populations. It is moreover argued that critical systems thinking is demonstrated by linking epidemiological evidence with scenario planning and soft systems methodology. This in turn supports a critical systems thinking approach to uncover the state of health disparities among minoritized communities under COVID-19.

Enhancing industrial sustainability in complex production systems through energy hotspot identification: A multi-task learning with layer-wise relevance propagation approach

With growing concerns about global warming and environmental degradation, the petrochemical industry has been urged to reduce energy consumption and emissions. However, making operational adjustments in multi-sectional production systems, which involve multiple production stages, is challenging without identifying the areas within the process that are intensive energy consumers. Therefore, this paper proposes a novel hotspot identification methodology in a multi-sectional production system using layer-wise relevance propagation of a multi-task learning model. The proposed model tracks overall energy efficiency as the final output to determine the energy gap, while the activation result identifies areas of production where energy is ineffectively consumed using sectional energy efficiency. The performance of the proposed method is validated using a case study of the vinyl chloride monomer process, which can be divided into five sections. The results show that the proposed methodology effectively captures the relationship between process variables and both tasks (overall and sectional energy efficiency) with an average R2 of 0.9818 and 0.9858 in prediction with unseen data, respectively. The layer-wise relevance propagation result demonstrates the contribution of unit operations to overall energy efficiency values by tracing the regression trajectories. Furthermore, operational adjustment using the overall energy gap as a benchmark condition offers a significant energy consumption reduction of around 15.87 %, reduces operating expenses for utilities by 147,000 USD, and decreases direct carbon dioxide emissions by 4700 tons, helping production to become more energy-efficient and sustainable.

Digital Economy Reformulation and Economic Competitiveness of Smart Cities: An Application of Soft Systems Methodology

Smart cities have become a new trend in urban development worldwide, and digital economy plays a crucial role. Therefore, this study aimed to create a novel formulation of digital economy that contributes to improving competitiveness of smart cities and provides practical guidance on digital economy strategies. A Soft System Methodology (SSM)–based action that utilizes the concept of systems was used to investigate conceptual models from different perspectives, from conceptual thinking to real-world–based action. Moreover, Indonesia was selected as a case study due to its extensive implementation of smart cities over the past five years, with over 250 cities participating in the program. The study successfully identified five scopes of digital economy, namely digital technology, digital service, shared economy and platform, digitalized economy, and financial services. It also identified six crucial criteria of economic competitiveness, namely human resources, product offerings, innovation capability, economic productivity, ease of business and investment, as well as financial capital. In conclusion, local cities and regencies' governments need to accelerate digital economy by creating supporting policies, making collaboration, effective digital marketing, improving digital literacy, guaranteeing cyber security, as well as protecting consumers and data privacy.

A novel instrumentation approach for achieving in-situ temperature sensing of a Li-ion cylindrical cell under immersion cooling

Battery thermal management systems are critical for high-performance electric vehicles, where the ability to remove heat and homogenize temperature distributions in single cells and packs are key considerations. Immersion cooling, which immerses the battery in a dielectric fluid, can improve cooling efficiency compared to air cooling. However, stringent instrumentation standards make in-situ monitoring under immersion cooling a difficult challenge. This study proposes a novel cell instrumentation method based on the integrated combination of three O-rings, which for the first time achieves in-situ temperature sensing of batteries under oil-immersed cooling conditions. Reliability of the sealing system and instrumentation methodology is ensured by leakage tests and oil corrosion assessment. Data such as cell impedance and discharge capacity also support the success and reliability of the instrumentation process without affecting the battery performance. Compared with natural air cooling, immersion cooling can reduce the temperature rise during battery operation by >10 °C under high C-rate discharge conditions. However, this is accompanied by an increase in radial temperature gradient. The temperature gradient of the cell with oil immersed reaches a maximum of >7 °C, while the temperature gradient under natural cooling under the same test conditions is only 3 to 4 °C. This leads to intensified temperature inhomogeneity of the cell, which may accelerate the local ageing and degradation. Our instrumentation approach contributes to a deeper understanding of the thermal behaviour of batteries under direct immersion cooling, aiding the development of more advanced cooling strategies and associated battery thermal management models.