Numerous databases exist in the field of cardiac surgery across Europe, but none of them combine the entire captured data of all clinical specialties involved in the care process of patients, including anesthesia and perfusion. To fill this gap, the European Congenital Heart Surgeons Association (ECHSA) and the European Association for Cardiovascular Anesthesia and Intensive Care along with the European Board of Cardiovascular Perfusion propose to build a database module for Anesthesia and Perfusion data collected during congenital cardiac surgery and interventional procedures and coupling these data within the existing largest congenital cardiac surgery database in Europe-the ECHSA Congenital Cardiac Database (ECHSA-CCDB). This report will review the state of the development of this database module to date and propose an initial set of variables for collection. The report also outlines the methodology to be followed for the final selection of variables and sets a course for the start of data collection. The scope of procedures to be captured includes pediatric cardiac surgery operations with or without cardiopulmonary bypass and interventional cardiology procedures. Descriptive study/methods study. Congenital cardiac surgery/cardiology/anesthesiology. Congenital cardiac anesthesiologists, surgeons, and perfusionists. None. We discuss a literature review, survey, and panel meetings for the development of an anesthesia and perfusion variables module being integrated into ECHSA-Congenital Cardiac Database by identifying the first set of variables to be available for outcome research in surgical operations and transcatheter interventional cardiology procedures across Europe. This multisocietal collaboration will lead to the most comprehensive approach to patient outcome research in congenital cardiac surgery and Interventional Cardiology in Europe to date. The article describes the initial concept of the modules and the process steps used to identify candidate variables for anesthesia outcomes in the setting of congenital cardiac surgery.

A carbon-free energy system might use ammonia as a hydrogen carrier owing to its high hydrogen concentration, good liquefaction, and worldwide production and transportation infrastructure. Catalytic ammonia breakdown provides a CO2-free hydrogen-generating method, but high temperatures, catalyst deactivation, and expense restrict its practicality. This study covers important aspects of ammonia decomposition catalysts, including Ru-, Ni-, and Co-based systems supported by carbon, Al2O3, and perovskite oxides. To develop structure-activity connections and identify rate-limiting stages under various operating regimes, thermodynamic, kinetic, and fundamental reaction processes are examined. Ru-based catalysts, especially those supported on conductive carbons and tailored perovskites, are standards for low-temperature activity, but their high cost and stability prevent large-scale application. Combining Ni-based catalysts with Al2O3 or basic perovskite supports offers a good balance between activity, cost, and commercial practicality, making them the dominant non-noble alternatives. Due to strong metal-support interactions and alternate reaction routes, Co-based catalysts in perovskite formulations seem promising despite being inherently less active. Support chemistry, promoters, metal dispersion, and oxygen vacancies are discussed, along with low-temperature operation and scale-up problems and research prospects.

Low-income settings such as Uganda bear a disproportionately high burden of diseases such as HIV/AIDS, but contribute little to knowledge production to address their health challenges. The training of health professionals traditionally focuses on clinical skills with less emphasis on research; yet, in practice, health professionals are expected to do operational research. In this study, we evaluate the impact of a micro-research model on building the capacity of undergraduate health professions students to conduct locally relevant biomedical, behavioral, clinical, and operations research on HIV and its co-morbidities. This study was within the Health Professional Education Partnership Initiative - Transforming Ugandan Institutions Training Against HIV/AIDS (HEPI-TUITAH; 2018-2024), using a sequential explanatory mixed methods design. We collected quantitative data on sociodemographic, number of trainings, concepts and written manuscripts. Thereafter, we conducted in-depth interviews and analyzed them using a thematic content approach. We used the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework to evaluate the impact (number trainings, protocols written, manuscripts written, publications, and change in attitudes) of the HEPI-TUITAH micro-research model. Of the 24 participants that were interviewed, 15 (62.5%) were male, aged 24-63 years (mean age, 35.3 years). Despite the challenges associated with the COVID-19 risk management measures, 41 (57%) out of the expected 72 manuscripts were published. Most participants expressed satisfaction with the recruitment process and the opportunities provided by the program. The process was described as transparent, competitive, and inclusive, allowing participation of multiple disciplines within the health professions training domains. The multi-disciplinary approach to team formation enriched the research process by leveraging diverse perspectives and expertise. Our data show that effective mentorship was a key driver of program success, facilitating skill development, fostering strong relationships, and ultimately contributing to the advancement of both mentees and mentors in their academic and professional pursuits. Training and mentorship sessions encompassed proposal development and manuscript writing. There was a general positive outlook regarding the sustainability at participating institutions. The micro-research model is a successful model for building research capacity for undergraduate health professions students. This model is highly recommended for integration into curricula for health professions programs.

The importance of data clustering is importance in assisting the decision-making process in any given complex system especially in an environment where data is large-scale and high-dimensional and heterogeneous. This paper introduces an evolutionary adaptation of the Chimp Optimization Algorithm (EVCHOA) to enhance the quality and strength of clustering when applied to the real world. The suggested solution implements an adaptive evolutionary update mechanism into the chimp-inspired search process to increase the exploration-exploitation ratio and avoid the early convergence. The effectiveness of EVCHOA in practice can be illustrated with references to intelligent learning systems, where the use of clustering allows analyzing the information about student performance and assisting in the making of individual decisions. Tests are done on real-life datasets implemented in a distributed computing framework and the outcomes are resolved against known methods of clustering, such as K-means, DBSCAN, hierarchical clustering, mean shift, and Gaussian mixture models. The evaluation of performance is based on conventional validity indices including DaviesBouldin Index, Silhouette Score, Adjusted Rand Index (ARI) and CalinskiHarabasz Index. The findings indicate that EVCHOA always achieves better clustering performance resulting in more coherent group structures and better interpretability by decision-makers. Within the framework of intelligent learning environments, the offered approach allows identifying student profiles more accurately, interfering with the target intervention, distributing resources in a more adaptive manner, and may support data-informed instructional planning. It is evident in these results that evolutionary metaheuristics is a useful tool in the operational research field and provides data-driven and scalable solutions to decision support in a variety of application fields.

Ethiopia's unregulated private antibiotic economy spans private hospitals, clinics, pharmacies, drug shops and informal vendors. It drives resistance through three forces: intergenerational self-medication (reservoir habit), a satisfaction imperative linking sales to customer retention, and a corner drugstore calculus prioritising immediate access over cheaper but slow public care. Non-prescribed dispensing becomes the norm and selects resistant bacteria. We propose a six-pillar framework: tiered accreditation, point-of-service education, supply chain engagement, mystery client support, technology enabled consultation and operational research. This stewardship without walls is an urgent priority for Ethiopia and similar settings.

Integrated Management of Childhood Illness (IMCI) guidelines, used alone, fail to reliably identify severe hypoxaemia (SpO2<90%), a predictor of mortality in children under five. The AIRE (Améliorer l'Identification des Détresses Respiratoires chez l'Enfant; in English, Improving Identification of Respiratory Distress in Children) operational research project introduced routine use of pulse oximetry (PO) within IMCI consultations in Burkina Faso, Guinea, Mali and Niger. We estimated the added value of incorporating PO within IMCI (IMCI+PO) for improving the diagnosis and subsequent management of severe hypoxaemia in primary healthcare centres (PHCs). All children aged 0-59 months attending IMCI consultations were eligible for SpO2 measurement, except those 2-59 months classified as simple non-respiratory cases using IMCI. Monthly aggregated data were collected from 202 AIRE PHC's through a cross-sectional study to estimate the added value of PO within IMCI in diagnosing severe cases (SCs) which should be referred. The added value was defined as the number of additional IMCI SCs with severe hypoxaemia, diagnosed using IMCI+PO, divided by the number of SCs diagnosed using IMCI alone. In a subset of 16 PHCs, we conducted a 14-day cohort follow-up for SCs. We analysed their management and mortality according to hypoxaemia status. Of the 514 901 IMCI consultations between June 2021 and December 2022, 74.2% were eligible for PO use. Of those, 5.4% were SCs diagnosed using IMCI+PO. The added value of PO was +4.9% (+962 SCs; 95% CI 4.6% to 5.2%). This was similar for all countries except Guinea (+0.9%). Healthcare workers' referral decisions were significantly higher for SCs with severe hypoxaemia (74.5%) than for those without (22.2%), p value <0.0001. In the research PHCs, the 142 SCs with severe hypoxaemia were significantly more likely to be referred and admitted to hospital, although their survival rates were similar. However, oxygen therapy remained suboptimal. At PHC level, PO improves the diagnosis of SCs with severe hypoxaemia and is associated with improved management. However, subsequent care in these settings remains challenging. PanAfrican Clinical Trial Registry (PACTR202206525204526).

Kidney exchange is a transplant modality that has provided new opportunities for living kidney donation in many countries around the world since 1991. It has been extensively studied from an Operational Research (OR) perspective since 2004. This article provides a comprehensive literature survey on OR approaches to fundamental computational problems associated with kidney exchange over the last two decades. We also summarise the key integer linear programming (ILP) models for kidney exchange, showing how to model optimisation problems involving only cycles and chains separately. This allows new combined ILP models, not previously presented, to be obtained by amalgamating cycle and chain models. We present a comprehensive empirical evaluation involving all combined models from this paper in addition to bespoke software packages from the literature involving advanced techniques. This focuses primarily on computation times for 49 methods applied to 4,320 problem instances of varying sizes that reflect the characteristics of real kidney exchange datasets, corresponding to over 200,000 algorithm executions. We have made our implementations of all cycle and chain models described in this paper, together with all instances used for the experiments, and a web application to visualise our experimental results, publicly available.

Residential heating system based on wind-solar complementary in northeast China: Operation research

Corrigendum to “Approximating G(t)/GI/1 queues with deep learning” [European Journal of Operational Research, Volume 322, Issue 3, 1 May 2025, Pages 889-907

<sec><title>BACKGROUND</title>TB disease control programmes routinely report on basic performance indicators, such as case detection or treatment success. Structures, processes, and quality of care are also important. We assessed published TB programme evaluations with respect to these basic and wider system factors.</sec><sec><title>METHODS</title>We conducted a scoping review of English peer-reviewed literature. We included articles if they presented TB programme evaluations in high-TB-incidence settings. We extracted data across domains and indicators to describe scope and methodology across the TB programme wider system.</sec><sec><title>RESULTS</title>The search yielded 885 articles, of which 57 were included. The majority (73.7%) did not use a systematic approach to indicator selection and 71.9% had unclear indicator definitions. Reports tended to be narrow in scope and focused on quality of care (89.5%), infrastructure and supplies (70.2%), human resources (70.2%), and outcomes/cascade of care (59.6%). Access to care (42.1%), engagement with community and patients (31.6%), knowledge and information (29.8%), leadership and governance (26.3%), and finances (26.3%) were infrequently evaluated.</sec><sec><title>CONCLUSION</title>TB programme evaluations vary in methodology and reporting, and are limited in scope. A more strategic and systematic combined approach to operational research, external evaluation, and internal diagnostic control systems would be beneficial for TB programmes.</sec>.

The transportation problem is an important problem that has received significant attention in operations research. Transportation problems are widely applied in supply chain management and logistics to minimize costs. Several methods have been developed to solve transportation problems when the cost coefficients and the quantities of supply and demand are precisely known. However, in real-world situations, the cost coefficients as well as the supply and demand quantities are often uncertain and may be represented as fuzzy quantities. A fuzzy transportation problem is a transportation problem in which the transportation costs, supply, and demand quantities are expressed as fuzzy quantities.

ABSTRACT Introduction Mathematical and optimisation models are frequently used to improve hospital planning and capacity management. However, the resulting model-derived solutions are rarely evaluated for their adoption within the real-world context of a hospital. Objectives In this study, we share our experience of an interdisciplinary collaboration between operations research/management science and implementation science, as one way of bridging the gap between technically sound solutions and their practical, sustainable use in healthcare. Methodology We applied implementation science prospectively to anticipate adoption implications at the design stage of a scheduling tool. Specifically, we used the Consolidated Framework for Implementation Research (CFIR) to identify anticipated barriers and facilitators for adopting a mathematically optimised surgery blueprint schedule within a children’s hospital. Results Identified anticipated facilitators included strong staff motivation to improve schedules, as well as positive perceptions of an objectively designed mathematical scheduling tool. Barriers included resistance to change among some staff and the demand for more evidence of the schedule’s benefits prior to implementation. We identified a strong culture of retaining autonomy in scheduling decisions, as well as operational adjustments made to current scheduling tools. Practical implications Applying CFIR prospectively demonstrated how implementation science frameworks could provide a structured way to anticipate adoption challenges and align technical solutions with organisational realities.

Social risks (adverse social and economic conditions) exert greater impact on health than clinical care. The Veterans Health Administration (VA) conducts annual clinical screenings for food insecurity, housing insecurity, and intimate partner violence nationally, but less is known about the prevalence of a wider array of social risks among VA patients. To quantify the prevalence of 19 social risks among VA patients. A cross-sectional survey, designed with VA operational leaders, clinicians, researchers, and Veterans, was distributed via web and mail March 21-June 25, 2024. National sample of 13,510 Veterans with ≥ 1 prior-year visit to VA primary and/or mental health care, stratified by health record-based age, race/ethnicity, and sex with oversampling for underrepresented groups. MAIN MEASURES: Prevalence of 19 social risks across material (e.g., food insecurity), social (e.g., social support), and personal (e.g., health literacy) circumstances, weighted for sampling and non-response probabilities. KEY RESULTS: Among 3430 respondents (response rate 25.4%), 53.1% (95% confidence interval (CI) 51.4-54.7%) were above age 65years; 84.8% (95% CI 83.5-86.0%) were cisgender men; 18.2% (95% CI 16.9-19.5%) were Black or African American; and 9.3% (95% CI 8.3-10.3%) were Hispanic, Latino, or Spanish. Nearly all (93.8%, 95% CI 92.6-94.9%) reported ≥1 social risk. The five most prevalent experiences were social isolation (54.9%, 95% CI 52.7-57.1%), financial strain (49.4%, 95% CI 47.1-51.7%), discrimination (47.8%, 95% CI 45.6-49.9%), caregiver responsibilities (39.8%, 95% CI 37.5-42.1%), and low digital literacy (37.9%, 95% CI 35.8-40.2%). Nearly all VA patients experience one or more social risks, which can negatively impact health and pose barriers to care. None of the five most prevalent social risks is included in VA's current annual screenings. These findings can inform VA's ongoing efforts to identify social risks, tailor care, and offer social and financial services to improve Veterans' health and well-being.

Effects of bovine and lamb blood-feeding sources on the fecundity and fertility of Wolbachia-carrying Aedes aegypti females.

The need to explore and/or optimize expensive simulators with many qualitative factors arises in broad scientific and engineering problems. Our motivating application lies in path planning – the exploration of feasible paths for navigation – which plays an important role in robotics and assembly planning. For complex settings, the parameter space for path exploration can be discrete and high-dimensional, and the evaluation of path feasibility requires expensive virtual simulations. A carefully selected experimental design is thus essential for timely decision-making. We propose here a novel framework called QuIP, for experimental design of a Gaussian process (GP) surrogate with Qualitative factors via Integer Programming. QuIP leverages a GP surrogate with an exchangeable covariance function. For initial design, we show that its maximin design can be formulated as an assignment problem from operations research, which can be efficiently and globally optimized via state-of-the-art integer programming solvers. For sequential design (specifically, for active learning or black-box optimization), we show that its design problem can similarly be formulated as an assignment problem, which facilitates efficient and reliable optimization with state-of-the-art solvers. We demonstrate the effectiveness of QuIP over existing design methods in a suite of path planning experiments and an application to rover trajectory optimization.

Formulating mathematical models from real-world decision problems is a core task in Operations Research, yet it typically requires considerable human expertise and effort, limiting practical application. Recent advances in large language models (LLMs) have sparked interest in automating this process from natural language descriptions. However, challenges including limited modelling expertise, dependence on large-scale training data, and hallucination affect the reliable application of LLMs in optimisation modelling. To address these challenges, we propose SMILO, an expert-knowledge-driven framework that integrates optimisation modelling expertise with LLMs to generate mixed-integer linear programming models. SMILO uses a three-stage architecture built on reusable modelling graphs and associated resources: identifying relevant modelling components, extracting instance-specific information using LLMs, and constructing models through expert-defined templates. This modular architecture separates information extraction from formula generation, enhancing modelling accuracy, transparency, and reproducibility. We demonstrate the implementation of our problem-type-specific modelling framework using workforce scheduling problems spanning manufacturing, logistics, and service operations as illustrative cases. Experiments show that SMILO consistently generates correct models in 93.33% of test instances across five trials, outperforming the baselines by at least 40%. This work offers a generalisable paradigm for integrating LLMs with expert knowledge across diverse decision-making contexts, advancing automation in optimisation modelling.

Background The United Kingdom has a diverse, professionalised dental workforce. Dental caries remains a prevalent disease, with significant inequalities. New ways of using our dental care professionals need to be considered in delivering preventive care to all patients.Methods Data sources for an operational research optimisation model included child (under 18 years) population demography; National Health Service (NHS) data; epidemiological data on dental caries experience from three national surveys; and preventive guidance identified from Delivering Better Oral Health V4. Data for care delivery included timings were informed by previous research and NHS working patterns. The Linear Programming model was developed for six different dental skill mix scenarios.Results Skill mix utilisation reduces time and workforce numbers, when compared with a single member of the dental team providing the preventive treatment plan. The workforce capacity ranged from 409-7,991 for dentists, 6,002-17,500 for dental therapists and 979-4,083 for extended duties dental nurses. Modelling suggests that a combination of all three dental team members provides the most efficient skill mix for this care delivery.Conclusion The development of this operational research model suggests the potential for skill mix in delivering evidence-informed, risk-based prevention of oral and dental disease in children in England in dental settings and beyond.

Illustrative overview of CNG engine performance, emissions behavior, and operation challenges. The graphic highlights key trends identified in the systematic review, including typical 10–20% power and torque losses; reductions in CO, HC, PM, and CO 2 emissions; methane-slip escalation with mileage; and the dual effect of cleaner combustion but accelerated lubricant oxidation. The figure synthesizes evidence from 22 studies to show how engine architecture, retrofit quality, and accumulated mileage shape real-world CNG outcomes • Provides the first systematic, strata-based synthesis of CNG performance, emissions, and durability in aged and retrofitted SI and CI engines. • Demonstrates that performance and emissions outcomes under CNG are governed by engine design, retrofit quality, calibration strategy, and accumulated degradation rather than fuel properties alone. • Shows that retrofitted SI fleet engines commonly experience power loss and methane slip, while optimized dedicated SI engines achieve higher efficiency through compression ratio and combustion phasing control. • Identifies dual-fuel CI engines as offering strong particulate reduction but requiring careful pilot-injection and EGR management to avoid CO and HC drawbacks at low load. • Highlights durability and lubrication trade-offs under CNG, with reduced soot contamination but increased thermo-oxidative oil stress, emphasizing the need for robust maintenance and calibration practices. Compressed natural gas (CNG) offers significant emissions advantages over gasoline and diesel, yet most literature focuses on new or laboratory-optimized engines rather than the aged, retrofitted vehicles common in developing countries. With addition of other studies, the review followed PRISMA 2020 guidelines and a prospectively registered protocol (OSF) − https://osf.io/c8u7f/ . Searches across Scopus, IEEE Xplore, and Google Scholar identified 816 records, of which 26 studies met inclusion criteria. CNG consistently lowered CO, HC, PM, and CO 2 emissions, but retrofitted SI engines experienced 10–20% losses in power and torque due to methane’s low volumetric energy density and age-related declines in efficiency. High-mileage fleets showed methane-slip increases, catalyst deterioration, and lubricant oxidation, whereas optimized or dedicated CNG engines demonstrated improved thermal efficiency and fuel economy. Retrofit quality and calibration accuracy proved decisive in determining real-world outcomes. The findings highlight that CNG’s environmental and efficiency benefits are achievable but depend on proper engine design, maintenance, and regulatory support, especially in regions dominated by older vehicle fleets. This review provides the first systematic synthesis focused on aged, high-mileage, and retrofitted spark ignition (SI) and compression ignition (CI) engines operating on CNG, integrating evidence on performance, emissions, combustion behavior, methane slip, lubricant degradation, and catalyst aging. By comparing retrofitted and dedicated CNG engines against real-world aged engine across diverse regions, it reveals how engine architecture, retrofit quality, and accumulated mileage shape CNG outcomes and identifies the operational challenges and research priorities needed for durable, efficient, and low-emission operation.

Coral reef is an endangered species among the coastal ecosystems. It can act as a rainforest; forms an ecosystem; provide habitat; protect coast from waves; contribute in carbon sinking and attract tourists along the coast. The fragile coral reefs are highly sensitive to environmental variations (temperature, salinity, turbidity, etc.) and anthropogenic activities. Among the above, temperature is the major factor that can affect corals to a large extent. Therefore, it is important to assess the coral bleaching due to an abnormal rise in Sea Surface Temperature (SST). Current study is an attempt to quantify the intensity of coral bleaching using Marine Heat Wave (MHW) based on persistent SST over 90 percentile in the Andaman environs during April to May, 2010. The contiguous degree of MHW is a dictator of thermal stress, which is non-conducive to corals can lead to bleaching. In this study the relationship was established between MHW and In-Situ observations to estimate the Percentage of Coral Bleaching (PCB) on a synoptic scale. Extent and PCB can be calculated only based on MHW by using the estimated slope value 1.36 and intercept value -13.957. Further, the relation between the ratio index value derived based on the ratio of Remote Sensing Reflectance (Rrs) values of bands 412 &amp; 531 nm and PCB to estimate the coral bleaching threshold (2.5). Significant correlation between Rrsratio index and PCB recorded is 0.82. The extracted threshold limits 2.5 ratio index can be used to confirm bleaching and values above this can categorize the coral bleaching intensity. The approach used in the current study enhances the capabilities of the operational coral reef monitoring programs and researchers.

The aim of this article is to present the methodological and theoretical foundations for examining the defense potential of the Polish diaspora and Polish citizens residing outside the territory of the Republic of Poland. In the context of a radical transformation of the security architecture in Central and Eastern Europe and the emergence of hybrid threats, the authors argue for the need to redefine the perception of the Polish diaspora and Poles living abroad – from a cultural and sentimental perspective toward understanding them as a strategic resource contributing to state resilience. The article introduces an original concept for empirical research on the Polish diaspora and Poles Residing Outside the Republic of Poland, based on methodological triangulation and purposive sampling, encompassing an analysis of their potential across seven countries of the greatest demographic and strategic importance to Poland. Thus, the article provides both a ready-to-use operational research framework and a substantive justification for conducting systematic studies within the problem area under discussion.