BackgroundRecurrent events of infectious diseases are common and the subject of analyses in many clinical studies. A proper understanding of disease occurrence over time within a cohort provides a basis for study planning and sample size estimation. This study mathematically describes the recurrence of malaria in a malaria-naïve cohort and highlights the necessary assumptions to inform study planning.MethodsTo represent different disease transmission scenarios, five mathematical models with different levels of complexity were constructed to mimic possible real-life scenarios. Model A represents the simplest model with constant infection risk, Model B includes protection due to treatment and reduced individual susceptibility after each infection, Model C shows preventive effects from a vaccination, Model D explores heterogeneous transmission with varying levels of infection risks, and Model E captures temporal dynamics through seasonal variation in infection risk. The models were implemented as compartmental models using a system of ordinary differential equations.ResultsThe different transmission scenarios strongly affected the pattern of recurrent infections. Models A and B had the same number of cases with infections; however, due to treatment effects and immunity development, the number of recurrent events was lower in Model B. Compared to Model B, Model C showed a substantial reduction in both first and recurring infections. In Model D, the subpopulation with a high transmission risk had a higher proportion of recurrent infections, with nearly 100% of this group experiencing more than one infection. Model E demonstrated how seasonal transmission risk leads to temporal dynamics with strong fluctuations in the occurrence of infections. Based on these models, we provide examples of how final cohort sizes can be estimated for different transmission settings.ConclusionsRecurrent infections in longitudinal studies cannot be estimated directly from disease frequency data. However, this study provides a simple set of equations to calculate the number of expected recurrent events. These models can be easily adapted to represent additional transmission and infection dynamics or to model other recurrent diseases like influenza.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12936-025-05594-1.

Abstract Testing is essential for successful delivery of software development and maintenance cycles and is performed by specialist testers, developers, and other team members. Our earlier studies of testers found a wide diversity in the participants. The intention for this study was to understand more about the characteristics, backgrounds, and work experiences of testers, as well as the approaches they take to the activities of testing, with the research question ‘Who is testing?’ In a qualitative survey of over 70 industry participants, covering testers from multiple countries and domains, the study uncovered information about their wide range of backgrounds, hobbies, roles, and characteristics, with differing work styles and problem-solving preferences. The people contributing to testing during software projects have varied backgrounds, academic qualifications, hobbies, and interests. Examination of their job titles and aspirations showed the actual and potential scope of the role. Their responsibilities, approaches to testing activities, and the problems they described showed their work requires a high cognitive skill level. We contribute findings that testers do not meet the stereotypes for IT workers, and the role does not meet the stereotype of boring, repetitive work. This matters for tester recruitment, retention, and career paths. There are also implications for the representation within IT teams of people using software, and potentially for society. Breaking the stereotyping and supporting diversity in testers’ backgrounds and characteristics might be supported by using tester personas to support aspects of testers’ work life. We demonstrate that a simple set of personas would not reflect the rich heterogeneity of the software tester community and instead introduce our current work to build a framework of heuristics that aids test tool designers and those acquiring tools to address building personas for their context.

Abstract Effective municipal solid waste management is currently facing a global problem. The increasing accumulation of these waste contents, resulting in improper waste management, has led to various environmental issues, including frequent greenhouse gas emissions and a lack of space for garbage disposal. Solid waste management is a serious problem in the current era. For this type of problematic issue, the concept of intuitionistic fuzzy sets (IFS) is a promising tool for assessing uncertain and fuzzy information. In this article, we propose the preference ranking organization method for enrichment evaluation (PROMETHEE) in combination with the analytical hierarchy process (AHP). The AHP is used for calculating weight vectors of attributes using IFS information. We present the multi-criteria decision-making (MCDM) algorithm based on AHP to investigate the weightage of attributes and the PROMETHEE approach for ranking the alternatives. We solve the real-life numerical example of finding the best solid waste management company for utilizing solid waste material. To investigate the applicability of the proposed theory, we comprehensively compare it with other fuzzy data aggregation models, such as intuitionistic fuzzy Hamacher weighted averaging and intuitionistic fuzzy Dombi weighted averaging operators. On the other hand, many MCDM approaches based on simple fuzzy sets fail to investigate IFS-based information. Hence, our proposed model offers more computational efficiency and is a suitable tool for investigating complicated MCDM problems.

Abstract Present-day simulations of supercell thunderstorms have the high resolution and incorporate the physical processes known to be conducive to simulating tornado-like vortices (TLVs). In such supercell simulations, TLVs are identified by the strength, duration and location of the vertical vorticity in the simulated supercell. To bring the analysis of these supercell-produced TLVs a step closer to observations and theory, the TLV in an advanced supercell simulation is identified as the tornado center, the Cartesian model velocities are transformed to cylindrical coordinates and azimuthally averaged. The azimuthally averaged TLV exhibits many of the observed and theoretically modeled features of tornadoes, including an end-wall vortex with strong maximum vertical and azimuthal velocities (wmax and vmax) close to the ground with transition to the weaker core velocities (wc and vc) aloft through vortex breakdown. A theory for the rotating-flow boundary layer with radial and azimuthal inflow velocities modeled on the axisymmetric analysis is shown to produce good qualitative agreement with the analyzed axisymmetric TLVs; however, the theoretical wmax and vmax are too large as the theory does not account for vortex breakdown. Estimation of the corner-flow swirl ratio suggest a limit of vmax/vc slightly greater than unity; since vc is a feature of the mesocyclone there is a limit on the extent to which the amplified velocities of the end-wall velocities can be realized. To augment the diversity of cases, the present analysis is applied to a simplified set of supercell simulations; the present theory explains several features of the axisymmetric vortices.

Assessing physical education (PE) classroom teaching enhancement through modern technologies remains a difficult task in the present era. The evaluation system contains four fundamental dimensions: student engagement, skill development effectiveness, cognitive impact, and feedback and assessment capability. Multi-attribute decision-making (MADM) is one of the most trending systems for ranking alternatives based on their attributes. The interval-valued circular intuitionistic fuzzy set is an advanced approach for assessing MADM problems, rather than the existing simple circular intuitionistic fuzzy set. The ordinary circular intuitionistic fuzzy set lacks a concept of intervals in membership degree (MD), non-membership degree (NMD), and circular degree (CD), resulting in a significant amount of information being lost. Dombi operations are a valuable approach to improving the precision of aggregated results. The interval-valued circular intuitionistic fuzzy set-based analytic hierarchy process (AHP) provides a structured and objective framework for evaluating various innovative teaching approaches, which can be complex. The evaluation process utilizes interval-valued circular intuitionistic fuzzy set-based AHP to assess three innovative PE teaching methods that help educators and policymakers maximize the effectiveness of their instruction. In the past, various approaches were defined within different fuzzy set-based frameworks; however, they lacked a proper method for evaluating the weightage of alternatives. There is a need to define new concepts using interval-valued circular intuitionistic fuzzy set-based information under AHP for the assessment of vague and uncertain MADM problems. By applying the concept of AHP, Dombi operations, and interval-valued circular intuitionistic fuzzy set, this study develops new theories, including interval-valued circular intuitionistic fuzzy Dombi weighted averaging (IV-CIFDWA) and interval-valued circular intuitionistic fuzzy Dombi weighted geometric (IV-CIFDWG) aggregation operators (AOs). The presence of AHP in the proposed approach makes it unique from other existing MADM approaches. We also investigate some desirable axioms of AOs. We offer an MADM algorithm based on a theory developed for precisely investigating fuzzy information. Solve the numerical problem of selecting the best PE platform using the MADM approach. We are ranking the set of considered alternatives like use of wearable fitness technology; game-based learning technology; video Feedback and performance analysis; task-based cooperative Learning. We noticed that use of wearable fitness technology is the best alternative is achieved by using the IV-CIFDWA and IV-CIFDWG operators. We compare our proposed methods with existing methods to verify their authenticity and accuracy. Lastly, we offer a conclusion.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-18080-0.

Ultrasound scatteromics: A multimodal QUS-based solution for detecting ambulatory function deterioration in Duchenne muscular dystrophy.

Optimising fibromyalgia criteria: evidence from the UK Biobank.

Introduction. The BPMN standard system (notation) is widely used in business process modeling. However, it is not expressive enough to represent technical and production mechanisms. BPMN poorly describes parallel flows with strict resource constraints, insufficiently supports modeling of physical parameters and technological conditions. These and other shortcomings worsen the analysis of performance and reliability, reduce the applicability of models for optimization and verification. The objective of the presented work is to create a method that uses an alternative notation and thus limits the impact of the listed shortcomings of BPMN in modeling production processes.Materials and Methods. The basis of the new solution was a comparison of BPMN and the notation for the system “node – function – object” (NFO). The elements of the diagrams were the intersections of some connections (nodes). They contained functional elements (functions, processes), which in some cases also had the characteristics of a substance (objects). A comparative analysis of the normative systems of BPMN and NFO showed the possibility of mutual transformation of diagrams. The processes were visualized using the CASE (Computer Aided Software Engineering) tool NFO-toolkit and the Stormbpmn program according to the BPMN rules. The NFO diagram was described in the XPDL2 language.Results. Six sequential operations have been developed for converting a NFO diagram into BPMN, and four — for the reverse transformation. The scheme of component production is shown in the context and decomposition, from the requirement for the development of the workflow to the issuance of products. Decompositions of the NFO elements “Injection Molding Machine”, “Master” and “Development Department” are presented, each of which corresponds to a decomposition of the same-name track of the BPMN notation pool. It has been proven that converting a BPMN diagram to a NFO improves the description of the process as a whole and to any degree of detail. The NFO approach does not refer to the graphical notation system of BPMN, which increases labor costs and the risk of simulation errors. The XPDL language describes processes, connectors, splitters, relationships, external entities, and other elements of NFO diagrams.Discussion. The main advantages of NFO notation over the BPMN approach are: easier procedure for creating models and their better visualization. A simple graphic set of NFO reduces simulation time and increases its accuracy. The NFO approach is focused on taking into account information and material connections. This means that it is possible to conduct functional cost CASE analysis, which is impossible using the BPMN method. The XPDL language is suitable for describing elements of NFO diagrams, and the solution can be Russified.Conclusion. Content redundancy and other shortcomings of the BPMN notation are eliminated through using a more universal and convenient notation — NFO. The research results will contribute to the development of the theory and practice of graphanalytic modeling of production processes, and simplify the procedure for their development and automation.

Standards play a crucial role in ensuring consistency, interoperability, and efficiency of communication across various disciplines. In the field of synthetic biology, the Synthetic Biology Open Language (SBOL) Visual standard was introduced in 2013 to establish a structured framework for visually representing genetic designs. Over the past decade, SBOL Visual has evolved from a simple set of 21 glyphs into a comprehensive diagrammatic language for biological designs. This perspective reflects on the first ten years of SBOL Visual, tracing its evolution from inception to version 3.0. We examine the standard's adoption over time, highlighting its growing use in scientific publications, the development of supporting visualization tools, and ongoing efforts to enhance clarity and accessibility in communicating genetic design information. While trends in adoption show steady increases, achieving full compliance and use of best practices will require additional efforts. Looking ahead, the continued refinement of SBOL Visual and broader community engagement will be essential to ensuring its long-term value as the field of synthetic biology develops.

This paper introduces a stochastic degradation model for Electronic Security Systems (ESS) within Cyber-Physical Protection Systems (CPPS). The model demonstrates how CPPS effectiveness is influenced by three major groups of factors: component aging processes, accumulation of adversary knowledge and skills, and technological advancements in attack tools, along with random vulnerability events and periodic maintenance actions. Each factor is represented by an appropriate distribution such as normal, logarithmic, or Bernoulli, providing a simplified yet representative set of degradation and recovery processes. System effectiveness SEi in month i is defined as max(100 − Xi, 0), where Xi aggregates the cumulative declines and improvements. Monte Carlo simulations (using hypothetical data and assumptions) reveal a characteristic degradation curve: high initial performance followed by accelerated decline, punctuated by minor recoveries due to maintenance. A case study indicates that the traditional EASI approach, which neglects degradation, substantially overestimates CPPS resilience over long time horizons. Consequently, we advocate incorporating the SEi metric into risk assessments and adopting adaptive maintenance schedules better aligned with real-world wear and evolving threats. Although based on hypothetical parameters, this model provides a foundation for calibration with operational data and for developing dynamic modernization and upkeep policies.

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This paper presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.

Ice crystallization in supercooled water is a complex phenomenon with far-reaching implications across scientific disciplines, including cloud formation physics and cryopreservation. Experimentally studying such complexity can be a highly data-driven and data-hungry endeavor because of the need to record rare events that cannot be triggered on demand. Here, we describe such an experiment comprising 561 million images of X-ray free-electron laser (XFEL) diffraction patterns (2.3 PB raw data) spanning the disorder-to-order transition in micrometer-sized supercooled water droplets. To effectively analyze these patterns, we propose a data reduction (i.e., coarse-graining) and dimensionality reduction (i.e., principal component analysis) strategy. We show that a simple set of criteria on this reduced dataset can efficiently classify these patterns in the absence of reference diffraction signatures, which we validated using more precise but computationally expensive unsupervised machine learning techniques. For hit-finding, our strategy attained 98% agreement with our cross-validation. We speculate that these strategies may be generalized to other types of large high-dimensional datasets generated at high-throughput XFEL facilities.

Carbohydrate‐protein interactions are important in cell‐cell communication, signal transduction, cancer, or infection. Chemists have designed glycosylated multivalent systems to mimic these recognition phenomena and produce potent ligands of lectins with therapeutic applications. Dynamic combinatorial chemistry (DCC) provides access to libraries of glycosylated macrocycles equilibrating through reversible covalent bonds. This strategy can be applied to the rapid and efficient identification of multivalent glycoclusters by introducing a protein into the equilibrating library. This strategy allowed the identification of the best ligands for more than one lectin in a single experimental set up by using two simple 1,4‐dithiophenol building blocks. Selection of the best binder by each lectin (ConA, LecA, and LecB) was accompanied by the amplification of glyco‐dyn[3]arenes and glyco‐dyn[4]arenes. These macrocycles could be synthesized, isolated, and displayed nanomolar dissociation constants. Furthermore, while no toxicity could be detected against human cells or bacteria, their anti‐adhesive properties against Pseudomonas aeruginosa were confirmed through a virulence assay on human cells. Altogether, extremely simple 1,4‐dithiophenol building blocks provided access to a large diversity of glycoconjugates that could be selected by a lectin in a simple experimental set up to identify glycoconjugates with potential anti‐infectious applications, thus speeding up the discovery of potential new antibacterial treatments.

Abstract This study presents a semi‐supervised machine learning method for predicting the occurrence of specific surface lithologies over a 330 km × 115 km area in Victoria, Australia. The study area is a geologically complex region within the Lachlan Fold Belt, characterized by orogenic events and surface lithologies that include deep‐marine sedimentary turbidites, granitic intrusions, volcanic formations and metamorphic complexes. The approach used a modified Self‐Organizing Map algorithm that was enhanced by an iterative multi‐step clustering process that used geophysical surveys (magnetic, radiometric, and gravity) with varying signal enhancements as inputs. The clustering results were refined through validation with a lithological database, allowing the algorithm to associate clusters of characteristics in the geophysical survey data with lithological categories. The lithological database comprised both natural rock samples, and synthetic samples derived from published geological maps in order to compensate for severe spatial heterogeneity in the locations of natural samples. It divided the observed and synthetic samples into 11 manually chosen categories that were expected to show distinctive fingerprints in the geophysical survey data: psammitic sedimentary, pelitic sedimentary, chert (quartz‐dominant) sedimentary, felsic intrusive, intermediate/mafic/ultramafic intrusive, felsic volcanic, intermediate/mafic/ultramafic volcanic, and regional metamorphic units. Within this simplified set of lithological categories, the output of the algorithm agreed well with a published geological map. The algorithm's performance demonstrates potential for broader applications to spatial lithological prediction, provided that the target rock types are characterized within existing global databases of rock samples and geophysical observations.

Hyponatremia is a potential serious adverse drug reaction to treatment with thiazide diuretics. This study aimed to determine whether individuals at high risk of developing thiazide-induced hyponatremia can be identified before treatment initiation. A population-based cohort study was conducted in Denmark among individuals aged ≥40 years from 2014 to 2020. Moderate-to-severe hyponatremia (plasma sodium <130 mmol/L) within 120 days of treatment was compared in new users of thiazide or non-thiazide antihypertensive drugs. Using the causal forest method, models to predict individual-level risk of thiazide-induced hyponatremia were trained in a development cohort (n=185,699; 2014-2018) and validated in a separate cohort (n=75,030; 2019-2020). Individual-level excess risk could be parsimoniously described by a four-covariate model that included information on age and baseline plasma sodium, hemoglobin, and C-reactive protein levels with good calibration and concordance-for-benefit (0.66; 95% CI, 0.66-0.67) in the validation cohort. The average 120-day excess risk of hyponatremia among thiazide-treated patients was 1.8% (95% CI, 1.3% to 2.2%), with individual-level heterogeneity ranging from -1.6% to 15.9%. For the 10% of thiazide-treated with the highest excess risk of hyponatremia the average excess risk was 7.4% (95% CI, 4.4% to 10.5%). Reassigning this high-risk group to non-thiazide drugs would reduce the excess risk within the thiazide-treated population by 0.7% (95% CI, 0.4%-1.0%), corresponding to a 42% relative reduction. The population-level burden of thiazide-induced hyponatremia can potentially be markedly reduced by identifying and prescribing alternative antihypertensive drugs to high-risk patient groups using a simple set of baseline information.

The Cas9 nuclease has become central to modern methods and technologies in synthetic biology, largely due to the ease in which it can be targeted to specific DNA loci via guide RNAs (gRNAs). Reports vary widely on the actual specificity of this targeting, with some studies observing 60% of gRNAs possessing no activity against the genome, while there is a general assumption in the E. coli community that inactive gRNAs are rare. To resolve these contradictions, we evaluated the activity of nearly 500,000 unique gRNAs in the E. coli K12 MG1655 genome. We show that the overwhelming majority of unique gRNAs are functional (at least 93%) while only 0.3% are nonfunctional. These nonfunctional gRNAs tend to exhibit strong spacer self-interaction, leading to the development of a simple set of gRNA design rules for bacteria. Finally, this work provides the greater microbial synthetic biology community a set of nearly half a million sgRNA spacers that have been empirically evaluated in vivo which will expedite future biological engineering projects.

Abstract We consider numerical aspects of finding classical J. Nash’s equilibrium in concave n-persons game, nonlinear equilibrium (NE), as an alternative to primal and dual linear programming (LP) problems, and recently introduced nonlinear production-consumption equilibrium (NPCE). The problems are particular cases of a general nonlinear equilibrium problem, which is equivalent to a variational inequality (VI). The corresponding VIs have simple feasible sets, that the projection on them is a low cost operation. Therefore, we apply two projection methods for finding the equilibrium: pseudo-gradient projection (PGP) and extra pseudo-gradient (EPG). We present and analyze results obtained on random generated sets of these three classes of problems. The obtained results show expected advantages of the EPG over PGP. What is most important: the number of iterations requited by EPG method to find an approximation for the equilibrium with a given accuracy grows linearly with the number of products in case of NE and NPCE, or with the number of active strategies in case of J. Nash’s equilibrium. The number of operations, or solution time grows as a cube of the corresponding parameters. These results corroborate the complexity bounds established in [18–20] under reasonable assumptions on the input data.

Image segmentation is a critical yet fundamental aspect of interpreting the spatio-spectral information contained in complex, high-dimensional mass spectrometry imaging (MSI) data. Clustering methods are commonly employed to group similar pixels to form segmentation maps that describe different spatial features or regions of interest found in material samples. In this study, we initially assessed three clustering methods, k-means, DBSCAN, and SOM-RPM, for their ability to identify both intercluster and intracluster variability on a simple bivariate synthetic data set. The results showed that k-means struggles with nonconvex data, while DBSCAN requires arduous parameter tuning. SOM-RPM was found to be the most suitable for resolving complex data structures due to its topology preserving property. The application of SOM-RPM was further expanded to a specific data treatment task in MSI-background data removal. We used a complex microarray time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data set as the exemplar, to investigate the impact of background removal on SOM-RPM models. Conspicuously, SOM-RPM produces robust segmentation maps, especially when the background was removed, that provide very useful insights into spectral diversity. We also incorporated a dimensionality reduction workflow by principal component analysis (PCA) and gained insights into the practicality of the method within ML pipelines. This study highlights the advantages of SOM-RPM in revealing underlying material properties and pipelining inefficiencies and measurement discrepancies to demonstrate its utility for applications such as forensic screening and analytical investigations.

As global momentum builds for universal health coverage (UHC), it is unclear whether orthodontic care should be included in UHC packages. The concept of medically necessary orthodontic care (MNOC) and its criteria thus have far-reaching implications for priority setting and resource allocation in public and private oral health care programs. To identify factors that contribute to the determination of MNOC based on perspectives from leaders of dental professional organizations, academics, clinicians, funders, patient advocates, and patients from 7 countries: Canada, United States, Germany, Greece, United Kingdom, Switzerland, and Australia. A qualitative description design was used with semi-structured virtual interviews conducted via Zoom from November 2021 to August 2022. Interviews were transcribed verbatim, coded, and analyzed for themes. Sixteen interviews were conducted. Participants described their concept of MNOC through 4 interrelated categories: (1) dental factors including dental health, the goals of treatment, and methods of needs assessment; (2) medical factors including the meaning of medical necessity, systemic health considerations, and treatment of craniofacial anomalies; (3) psychosocial factors including societal standards of beauty, social functioning, and mental health; and (4) funding factors including resource allocation considerations and the goals of funding. The diversity of factors identified highlights the complex interplay between the dental profession, funders of care, society, and individual patients in understanding MNOC. Given this complexity, MNOC is arguably not amenable to a concise definition or list of criteria. Instead, a decision-making process that incorporates key actor perspectives can enhance transparency, fairness, and accountability in priority setting and resource allocation as related to MNOC and medically necessary oral health care more broadly. This approach would ensure coverage for those with demonstrated need in the context of health, well-being, and quality of life.Knowledge Transfer Statement:This study provides critical insights into the dental, medical, psychosocial, and funding factors that influence the meaning of medically necessary orthodontic care (MNOC) from the perspectives of key actors in 7 high-income countries. The findings reveal that MNOC cannot be defined by a simple set of criteria. Instead, determinations of MNOC should be made through a decision-making process that incorporates a wide array of viewpoints. This approach ensures transparent and fair resource allocation, improving access to essential orthodontic services, thereby enhancing patient health and well-being.

Abstract In this work, we couple the functional–structural plant model CPlantBox to the Unreal Engine by exploiting the implemented raytracing pipeline to evaluate light influx on the plant surface. There are many approaches for photosynthesis computation and light evaluation, though they typically are limited by versatility, compute speed, or operate on much coarser resolutions. This work specifically addresses the concern that data generation pipelines tend to be unresponsive and do not include model-based knowledge as part of the generation pipeline. Using established photosynthesis solvers, we model the interaction between the Unreal Engine and the FSPM to measure physical properties in the virtual world. This is successful if we are able to reproduce experimental results using an in silico model. As part of the pipeline, we generate a surface geometry and utilize material shaders that are designed to establish a baseline surface model for light interception and transmission, based on simple parameter sets that can be calibrated. Using a Selhausen field experiment as baseline, we reproduce the photosynthesis effectiveness of the plants in the 2016 winter wheat experiments. Our pipeline is deeply intertwined with data generation and has been proven to perform well at scale. In this work, we build on our previous work by showcasing both a simulation study of a light evaluation as well as quantifying how well our system performs on high-performance computing systems.