Operational Model Research Articles

Free surgery for CHD through philanthropy—a sustainable model?

AbstractAmong the multiple challenges faced by children from low- and middle-income countries (LMICs) with congenital heart disease (CHD), the economics of care remains foremost, contributing significantly to morbidity and mortality. This paper evaluates the four existing finance models available for healthcare systems and proposes a new model—the GIVE model (government, institutions and individuals, values, and engagements)—as the fifth model for global sustainable healthcare systems. The paper presents an evaluation of a chain of three paediatric cardiac hospitals in India to assess the sustainability of their philanthropy-based operational model, through which surgeries are offered completely free of cost to children with CHD. The three Sri Sathya Sai Sanjeevani Centres for Child Heart Care in India have been proponents of this philanthropic model for over a decade. From February 2013 to January 2024, 19,684 patients with CHD received surgeries at no cost. The average cost of surgery was reported to be USD (United States Dollar) 1800. A case study of one of the Sanjeevani Centres showed that 23.8% of patients were in STAT Category 3 and above, as defined by the Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery, with an in-hospital mortality rate of 2.08%. The evaluation highlighted the institution’s numerous strategies to enable sustainability in key aspects of operations, economics, and social impact. Despite challenges, the Sai Sanjeevani philanthropic model, which encompasses both economic and social impact, is dependable and can be replicated. The proposed GIVE model is recommended for adoption by LMICs as a global way forward to enable free CHD surgeries through sustained philanthropy, strengthened by a shared vision and collaborations.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

Assessment of Urban Resilience to Floods: A Spatial Planning Framework for Cities

Urbanization-led economic growth drives infrastructure investments and population accumulation in cities, hence exploiting natural resources at an extreme rate. In this context, coastal cities have become vulnerable to climate change-induced extreme weather events and human-made disasters in recent history, where effective measures to improve the resilience of cities are pivotal for developing sustainable living environments. This study proposes a framework for assessing urban resilience to natural disasters (floods) using bottom-up spatial interactions among natural, physical, and social systems within cities and regions. It is noted that seminal studies focus on either the mitigation or adaptation strategies within urban environments to assess disaster resilience, where limited multidisciplinary and operational models hinder evaluations at the city scale. Therefore, urban system interactions and quantifiable parameters proposed in this framework are essential for policymakers and disaster management agencies in the timely allocation of resources to optimize the recovery process. Moreover, spatial planning agencies can adopt resilience mapping to identify the potential risk zones and orient sustainable land use management. Urban resilience can be embodied in spatial strategies with the operational framework proposed here, and future urban growth scenarios can be tested in multiple disaster conditions.

Research on Strategies for Digital Empowerment of the Ice and Snow Sports Tourism Industry

The era of the data economy, driven by digital technologies such as big data, blockchain, and cloud computing, has arrived. It is essential to leverage the digital economy to promote the high-quality development of the ice and snow sports tourism industry. This paper employs methods such as literature review and inductive reasoning to argue that the digital economy enhances the high-quality development of the ice and snow sports tourism industry through three main aspects: improving supply efficiency, achieving industrial transformation, and promoting industry integration. However, China’s ice and snow sports tourism industry faces several challenges, including inadequate institutional supply, a lack of high-quality talent, traditional operational models, and a singular investment body. To address these challenges, this paper proposes several strategies: strengthening government leadership to create a suitable policy environment; establishing a talent cultivation system to support the upgrading of the ice and snow sports tourism industry; empowering the industry through technology to promote integration; and fostering a collaborative approach among diverse stakeholders to innovate the industry’s business models.

Rainfall Enhancement Downwind of Hills Due to Stationary Waves on the Melting Level and the Extreme Rainfall of December 2015 in the Lake District of Northwest England

This paper investigates how stationary gravity waves generated by flow over orography enhance rainfall, with particular attention to the role of induced waves in the melting level. The findings reveal a new mechanism by which gravity wave flow focuses precipitation, amplifying rainfall intensity downwind of hills. This mechanism, which depends on the differential velocities of rain and snow, offers fresh insights into how orographic effects can intensify rainfall. A two-dimensional diagnostic model based on linear gravity wave theory is used to investigate the record-breaking rainfall of December 2015 in the Lake District of northwest England. The pattern of ascent is shown to have a qualitatively good fit to that of the Met Office’s operational high-resolution UKV model averaged over 24 h, suggesting that orographically excited stationary waves were the principal cause of the rain. Precipitation trajectories imply that a persistent downstream elevated wave caused by the Isle of Man supported a spray of seeding ice particles directed towards the Lake District, and that these grew whilst suspended in strong upslope flow before being focused by the undulating melting-level into intense shafts of rain.

Operational optimization of a rural multi-energy system supported by a joint biomass-solid-waste-energy conversion system and supply chain

Biomass, as one of the most accessible renewable resources, could reduce dependency on fossil fuels and mitigate associated environmental impacts. With economic development and improved living standards, biomass in rural areas is converted into various forms of energy to meet high energy demands. However, existing studies on rural multi-energy system operations assume energy production from biomass is a given input parameter and consider it to be a fixed constant or subject to upper and lower bounds, independent of the biomass supply chain. Indeed, biomass energy production is significantly influenced by the collection, transportation, storage, and other processes within the supply chain. This work proposes a mixed integer linear program (MILP) for the joint optimization of an integrated rural multi-biomass-solid waste energy conversion system with a source-grid-demand-based biomass-solid waste supply chain (IRMBS-BSC system). The IRMBS-BSC system encompasses three sub-systems: the biomass-solid waste source sub-system, the biomass-solid waste grid sub-system, and the biomass-solid waste demand sub-system. Various biomass-solid wastes are classified, and a source model is established to evaluate their availability. Subsequently, a supply chain model is developed within the biomass-solid waste grid sub-system. Based on the available and transported quantity from the biomass-solid waste source sub-system and grid sub-system, a multi-energy system operation model, considering refined biomass conversion processes, is proposed. A case study involving a multi-energy system that integrates multiple rural community biomass-solid waste supplies is conducted. The results demonstrate that the proposed MILP, which considers the biomass-solid waste supply chain, can reduce energy imports during the load peak periods and can also decrease the overall operational costs of the multi-energy system.

A value chain modeling approach for upscaling the production of fine flavor cocoa in Arauca (Colombia)

AbstractThe fine flavor cocoa (FFC) market offers additional income for small farmers. Despite the potential of several Latin American countries to produce FFC, their lack of standardized postharvest processes hinders compliance with high‐quality standards demanded by the FFC market, as these processes have a direct impact on cocoa's organoleptic characteristics. This work supports the design of a collective postharvest transformation model that coordinates the main steps in the cocoa agri‐food chain: transport of the cocoa harvest, classification, fermentation, drying, storage, and commercialization. Our methodology includes a modeling scheme combining optimization and simulation models, considering variability in the processes, and covering operational (transport and resource allocation), tactical (design and sizing of the processing plant), and strategic (sales dynamics) decisions across the cocoa value chain. We illustrate this methodology in a case study in Arauca, Colombia. Our findings emphasize the importance of combining these operational and strategic models since their inputs and outputs are interconnected influencing the decision‐making within the cocoa value chain. Our study also identifies key determinants for the postharvest design, highlighting the postharvest center capacity, FFC prices, and the market selling mode (export or local).

Integrating an urban fire model into an operational wildland fire model to simulate one dimensional wildland–urban interface fires: a parametric study

Background Wildland fires that occur near communities, in the wildland–urban interface (WUI), can inflict significant damage to urban structures. Although computational models are vital in wildfires, they often focus solely on wildland landscapes. Aim We conducted a computational study to investigate WUI fire spread, encompassing both urban and wildland landscapes. Methods We developed a 1D landscape-scale semi-physical model by integrating a semi-physical urban fire spread model into an Eulerian level set model of wildfire. The model includes ignition and spread through radiation, direct flame contact and ember deposition. Key results Through a parametric study, we compare the relative change of spread rate from various structural properties and landscape layouts represented by model parameters, highlighting the significant impact of fire-resistant structure materials over surface treatments. Layout configurations play a pivotal role in fire spread, with isolated islands of combustibles effective in reducing spread rate, aligning with existing mitigation strategies. Conclusion Despite using a 1D domain and limitations on spatial and temporal variability, our model provides insights into underlying phenomena observed in WUI fires and their mitigation. It offers early-stage development of strategies for managing structure materials and landscape layouts. Implications Our model and findings provide insights into WUI fire dynamics, paving the way for advanced mitigation strategies.

Preventive Preservation of Rammed Earth Historical Heritage Through Continuous Monitoring, Architectural Inspections, and Data Fusion

Rammed earth construction, an ancient and sustainable building technique, faces significant preservation challenges, particularly in historical contexts. This study aims to enhance the preventive preservation of rammed earth historical heritage through a comprehensive methodology combining continuous monitoring, architectural inspections, and data fusion. By integrating nondestructive testing techniques such as ultrasound, thermography, and ground-penetrating radar with operational modal analysis and modeling, the proposed approach allows for early detection and assessment of structural vulnerabilities. This methodology was applied to the Tower of Muhammad in the Alhambra of Granada, Spain, demonstrating its effectiveness in identifying and quantifying damage and predicting structural health. Using multi-source data (documentation, inspections, nondestructive tests, and continuous monitoring), a finite element model was built, calibrated (achieving an avg. error in modal frequencies of 1.28% and a minimum modal assurance criterion value of 0.94), and used to develop a surrogate model able to predict the modal properties of the tower in 0.02 s, becoming compatible with continuous system identification. The presented results highlight the importance of continuous data acquisition and advanced diagnostic tools for safeguarding rammed earth structures against environmental and anthropogenic threats. This study advocates for the adoption of digital twins in historical preservation, facilitating informed decision-making and sustainable management of cultural heritage.

A Platform of Federated Learning Management for Enhanced Mobile Collaboration

Federated learning (FL) has emerged as a crucial technology in today’s data-centric environment, enabling decentralized machine learning while safeguarding user privacy. This study introduces “Federated Learning ML Operations (FedOps) Mobile”, a novel FL framework optimized for the dynamic and heterogeneous ecosystem of mobile devices. FedOps Mobile addresses the inherent challenges of FL—such as system scalability, device heterogeneity, and operational efficiency—through advanced on-device training using TensorFlow Lite and CoreML. The framework’s innovative approach includes sophisticated client selection mechanisms that assess device readiness and capabilities, ensuring equitable and efficient participation across the network. Additionally, FedOps Mobile leverages remote device control for seamless task management and continuous learning, all without compromising the user experience. The main contribution of this study is the demonstration that federated learning across heterogeneous devices, especially those using different operating systems, can be both practical and efficient using the FedOps Mobile framework. This was validated through experiments that evaluated three key areas: operational efficiency, model personalization, and resource optimization in multi-device settings. The results showed that the proposed method excels in client selection, energy consumption, and model optimization, establishing a new benchmark for federated learning in diverse and complex environments.

Exploration of Risk Factors for Cardiovascular Disease in Patients with Rheumatoid Arthritis: A Retrospective Study.

Patients with rheumatoid arthritis (RA) have increased mortality and morbidity rates owing to cardiovascular diseases (CVD). Timely detection of CVD in RA can greatly improve patient prognosis; however, this technique remains challenging. We aimed to investigate the risk factors for CVD incidence in patients with RA. This retrospective study included RA patients without CVD risk factors (n = 402), RA with CVD risk factors (n = 394), and RA with CVD (n = 201). Their data on routine examination indicators, vascular endothelial growth factor (VEGF), and immune cells were obtained from medical records. The characteristic variables between each group were screened using univariate analysis, least absolute shrinkage and selection operator (LASSO), random forest (RF), and logistic regression (LR) models, and individualized nomograms were further established to more conveniently observe the likelihood of CVD in RA. Univariate analysis revealed significantly elevated levels of white blood cells (WBC), blood urea nitrogen (BUN), creatinine, creatine kinase (CK), lactate dehydrogenase (LDH), VEGF, serum total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), apolipoprotein B100 (ApoB100), and apolipoprotein E (ApoE) in RA patients with CVD, whereas apolipoprotein A1 (ApoA1) and high-density lipoprotein/cholesterol (HDL/TC) were decreased. Furthermore, the ratio of regulatory T (Treg) cells exhibiting excellent separation performance in RA patients with CVD was significantly lower than that in other groups, whereas the ratios of Th1/Th2/NK and Treg cells were significantly elevated. The LASSO, RF, and LR models were also used to identify the risk factors for CVD in patients with RA. Through the final selected indicators screened using the three machine learning models and univariate analysis, a convenient nomogram was established to observe the likelihood of CVD in patients with RA. Serum lipids, lipoproteins, and reduction of Treg cells have been identified as risk factors for CVD in patients with RA. Three nomograms combining various risk factors were constructed to predict CVD occurring in patients with RA (RA with/without CVD risk factors).

Sustainable Logistics: Synergizing Passive Design and PV–Battery Systems for Carbon Footprint Reduction

As more companies strive for net-zero emissions, mitigating indirect greenhouse gas emissions embedded in value chains—especially in logistics activities—has become a critical priority. In the European logistics sector, sustainability and energy efficiency are receiving growing attention, given the sector’s intersectional role in both transportation and construction. This transition toward low-carbon logistics design not only reduces carbon emissions but also yields financial benefits, including operational cost savings and new market opportunities. This study examines the impact of passive design strategies and low-carbon technologies in a Swedish logistics center, assessed using the low-carbon design criteria from the BREEAM International standard, version 6. The findings show that passive energy-efficient measures, such as the installation of 47 skylights for natural daylighting, reduced light power density in accordance with AHSHARE 90.1-2019 and the integration of free night flushing, contribute to a 23% reduction in total energy consumption. In addition, the integration of 600 PV panels and 480 batteries with a capacity of 268 ampere-hours and 13.5 kWh storage, operating at 50 volts, delivers a further 56% reduction in carbon emissions. By optimizing the interaction between passive design and active low-carbon technologies, this research presents a comprehensive feasibility analysis that promotes sustainable logistics practices while ensuring a future-proof, low-carbon operational model.

Estimates for Sums of Eigenfunctions of Elliptic Pseudo-differential Operators on Compact Lie Groups

We extend the estimates proved by Donnelly and Fefferman, and by Lebeau, Robbiano and Zuazua, for sums of eigenfunctions of the Laplacian (on a compact manifold) to estimates for sums of eigenfunctions of any positive and elliptic pseudo-differential operator of positive order on a compact Lie group. Our criteria are imposed in terms of the positivity of the corresponding matrix-valued symbol of the operator. As an application of these inequalities in control theory, we obtain the null-controllability for diffusion models for elliptic pseudo-differential operators on compact Lie groups.

An innovative operational model for companies operating bus fleets

The organization and operation of public transport have not escaped the impact of changes taking place worldwide which are driven and influenced by the rapid spread of disruptive technologies and efforts related to climate protection and sustainable development. The challenges posed by the proliferation of electric vehicles (EVs) – including electric buses (BEBs) – require new solutions and new ways of thinking, and a more holistic and innovative operating model for road transport companies using buses. In this model, the authors present a methodology for the delivery of contracted scheduled services that, as part of a systems approach, links a mixed fleet and its appropriate route plan within a data-driven circular operational model, allowing room for innovative energy solutions, the use of renewable energy sources and for satisfaction of increasingly demanding climate and sustainability requirements, all using a logistics science approach.

A novel approach to the cause of death identification—multi-strategy integration of multi-organ FTIR spectroscopy information using machine learning

Identifying the cause of death has always been a major focus and challenge in forensic practice and research. Traditional techniques for determining the causes of death are time-consuming, labor-intensive, have high professional barriers, and are vulnerable to significant subjective bias. Additionally, most current studies on causes of death are limited to specific organs and single causes. To overcome these challenges, this study utilized simple and rapid fourier transform infrared spectroscopy (FTIR) detection technology, integrating data from six organs—heart, liver, spleen, lung, kidney, and brain. The optimum model for identifying seven different causes of death was determined by evaluating the performance of models developed using the model efficiencies of single-organ (SO), single-organ model fusion (SOMF), multi-organ data fusion (MODF), and multi-organ data model fusion (MODMF) modeling methods. Considering factors such as operational costs, model performance, and model complexity, the MODF artificial neural network (ANN) model was found to be the most suitable choice for constructing a cause of death identification model, with a cross-validation mean accuracy of 0.960 and a test set accuracy of 0.952. The heart and kidney contributed more spectral features to the construction of the cause of death identification model compared to other organs. This study not only demonstrated that data fusion and model fusion are effective strategies for improving model performance but also provided a comprehensive data analysis framework and process for modeling with small sample multi-modal data (multiple organ data). In conclusion, by exploring various approaches, this study offers new solutions for identifying the cause of death.

Academentia, management and satire: ‘The good, the bad and the ugly’

Orientation: The argument rethinks current managerial practices in higher education.Research purpose: Satire beckons when normal science cannot reproduce itself or shift to account for different ways of doing things. When reality mimics satire, satire becomes an appropriate methodology. Such literary approaches can reveal what normal science cannot.Motivation for the study: To add to critical management studies and the combatting of academentia as an institutional condition.Research design, approach and method: The general methodology derives from both literary and business principles read through critical management studies and cultural studies. Autoethnography is shown here to be useful within accounting and management studies. The Semler and the Kalahari management practices explicitly specify the need to rethink ‘operational models’.Main findings: No findings are offered in the conventional sense. Rather, relations are analysed and conclusions result from my own internal dialogue as a central player in the case study. The ‘transformation’ objectives of governance were disconnected from educational purposes. The resulting dramatic narrative hopefully appeals to readers in terms of their own lived conditions: can readers insert themselves into the narrative in terms of their own experiences?Practical and/or managerial implications: Institutions to be managed as adhocracies. What has been learned after 20 years of transformation and restructuring, if anything?Contribution and/or value-add: An engagement with critical management studies from the perspective of cultural studies identifies some solutions. Like the former, the latter examines issues of power relations, in this case, relations subsisting within administrative systems rather than the more usual framing as being between classes, races, ethnicities and genders.

A multi-objective dual dynamic genetic algorithm-based approach for thermoelectric optimization of integrated urban energy systems

In order to reduce the loss of energy in the process of conversion and utilization, and to improve the energy utilization effect and economic benefits of urban integrated energy system, a thermoelectricity optimization method of urban integrated energy system based on multi-objective double dynamic genetic algorithm is proposed. The method analyzes the operation characteristics of the urban integrated energy system through its operation model, constructs a multi-objective optimization model for the thermoelectricity of the urban integrated energy system, determines the optimization multi-objective function that minimizes the operation cost, minimizes the carbon emission, minimizes the technical dissatisfaction and the related constraints, and then solves the optimization model by using the dual dynamic genetic algorithm to output the results of the optimization of the thermoelectricity of the urban integrated energy system.The test results show that the algorithm has a crowding distance of 0.75 or above when solving, and the overall unit energy consumption cost reduction ratio is about 22.97 %. The state of charge results of the four energy storage power stations are all below 10 %, and the lowest convergence speed is only 7 seconds, effectively improving energy utilization efficiency.

Semantics of Remote Direct Memory Access: Operational and Declarative Models of RDMA on TSO Architectures

Remote direct memory access (RDMA) is a modern technology enabling networked machines to exchange information without involving the operating system of either side, and thus significantly speeding up data transfer in computer clusters. While RDMA is extensively used in practice and studied in various research papers, a formal underlying model specifying the allowed behaviours of concurrent RDMA programs running in modern multicore architectures is still missing. This paper aims to close this gap and provide semantic foundations of RDMA on x86-TSO machines. We propose three equivalent formal models, two operational models in different levels of abstraction and one declarative model, and prove that the three characterisations are equivalent. To gain confidence in the proposed semantics, the more concrete operational model has been reviewed by NVIDIA experts, a major vendor of RDMA systems, and we have empirically validated the declarative formalisation on various subtle litmus tests by extensive testing. We believe that this work is a necessary initial step for formally addressing RDMA-based systems by proposing language-level models, verifying their mapping to hardware, and developing reasoning techniques for concurrent RDMA programs.

A Digital Twin of a University Campus from an Urban Sustainability Approach: Case Study in Madrid (Spain)

The development of geographic information systems has grown significantly over the past decade. Simultaneously, the concept of smart cities based on the management of large volumes of data has also spread worldwide. The digital twin concept has recently been incorporated into the technological domain of urban management. However, currently, phases such as technological integration, standardization, data and process interconnection, the development of tools and utilities, professional training, and the application of digital urban development in real-world situations are converging. This paper presents the experience developed on a university campus, detailing each of the phases carried out from the initial design to a fully operational pilot phase model. The article highlights the importance of certain aspects to consider in each phase, demonstrating that there are barriers and limitations and at the same time, great strengths and opportunities in applying the digital twin model in urban management, considering aspects such as mobility, accessibility, energy management, and involving students and university administrators in the process.

Innovative financial and operational models for affordable housing: A review of emerging market strategies

Innovative financial and operational models for affordable housing: A review of emerging market strategies