Interval Analysis Research Articles

Simulation of exchange processes in multi-component environments with account of data uncertainty

The goal of the work. Proposals for methods of solving systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients that defined in interval form and intended for modeling exchange processes in multicomponent environments. Research subject: systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients defined in interval form. Research method: interval analysis. The obtained results. Systems of linear homogeneous and non-homogeneous differential equations, which are used in modeling exchange processes in multicomponent environments, are considered. Such systems can be considered, for example, in problems of chemical kinetics, materials science, and the theory of Markov processes. To obtain the solution of these equations, specialized calculators of analytical transformations were used and tested. The Matlab system (ode15s solver) was used for numerical analysis of systems of differential equations. It is shown that the application of interval methods of numerical analysis at the initial stage of system modeling has some advantages over probabilistic methods because they do not require knowledge of the laws of distribution of the results of the system state parameter measurements and their errors. It is shown that existing methods of solving systems of linear differential equations can be divided into two groups. Common to these groups is the use of interval expansion of classical methods for solving differential equations given in interval form. The difference between these two groups of methods is as follows. The methods of the first group can be used for all types of differential equations but require the creation of special software. The peculiarity of the methods of the second group is that they can be used to solve equations analytically or using numerical analysis packages. The application of the methods of the second group is shown on the example of solving a system of differential equations, the coefficients of which are determined in interval form. The system of these equations is intended for modeling the processes of exchange with the external environment of the elements of the model of a specific physical system. In the case when the coefficients of these equations are variables, their piecewise-constant approximation is applied and a criterion that determines the possibility of its application is given. The technique proposed in the paper can be applied to solve systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients if they are given by slowly varying functions. In the case when the coefficients of the equations are determined in the interval form, the technique allows obtaining their solution also in the interval form and does not require the creation of special software.

A Novel Slacks-Based Interval DEA Model and Application

This paper proposes a novel slacks-based interval DEA approach that computes interval targets, slacks, and crisp inefficiency scores. It uses interval arithmetic and requires solving a mixed-integer linear program. The corresponding super-efficiency formulation to discriminate among the efficient units is also presented. We also provide a case study of its application to sustainable tourism in the Mediterranean region, assessing the sustainable tourism efficiency of twelve Mediterranean regions to validate the proposed approach. The inputs and outputs cover the three sustainability dimensions and include GHG emissions as an undesirable output. Three regions were found to be inefficient, and the corresponding inputs and output improvements were computed. A total rank of the regions was also obtained using the super-efficiency model.

Detrimental effects of electromagnetic radiation emitted from cell phone on embryo morphokinetics and blastocyst viability in mice.

Electromagnetic radiation (EMR) has deleterious effects on sperm motility and viability, as well as oocyte membrane and organelle structure. The aim was to assess the effects of cell phone radiation on preimplantation embryo morphokinetics and blastocyst viability in mice. For superovulation, 20 female mice were treated with intraperitoneal (IP) injections of 10 IU pregnant mare's serum gonadotropin (Folligon® PMSG), followed by 10 IU of human chorionic gonadotropin (hCG) after 48 h. The zygotes (n = 150) from the control group were incubated for 4 days. The experimental zygotes (n = 150) were exposed to a cell phone emitting EMR with a frequency range 900-1800 MHz for 30 min on day 1. Then, all embryos were cultured in the time-lapse system and annotated based on time points from the 2-cell stage (t2) to hatched blastocyst (tHDyz), as well as abnormal cleavage patterns. Blastocyst viability was assessed using Hoechst and propidium iodide staining. Significant increases (P < 0.05) were observed in the cleavage division time points of t2, t8, t10, and t12 of the experimental group compared with the controls. In terms of blastocyst formation parameters, a delay in embryo development was observed in the experimental group compared with the controls. Data analysis of the time intervals between the two groups showed a significant difference in the s3 time interval (P < 0.05). Also, the rates of fragmentation, reverse cleavage, vacuole formation, and embryo arrest were significantly higher in the experimental group (P < 0.05). Furthermore, the cell survival rate in the experimental group was lower than the control group (P < 0.05). Exposure to EMR has detrimental consequences for preimplantation embryo development in mice. These effects can manifest as defects in the cleavage stage and impaired blastocyst formation, leading to lower cell viability.

In Memoriam: Celebrating the Life and Legacy of Svetoslav Markov

It is with deep sadness and profound appreciation that we reflect on the life and scientific journey of Professor D. Sc. Svetoslav Marinov Markov, who departed this world at the age of 80. Professor Markov (or Sveti, as we, his colleagues and friends called him) has been an integral part of our lives, and together with our grief, we would like to celebrate his remarkable life and outstanding achievements in science. His career, spanning over five decades, has left an indelible mark by marrying biomathematics with interval analysis. Specifically, he focused his research on modeling biological processes under conditions of uncertainty, employing the tools of interval arithmetic. His work extended beyond ensuring the accuracy of input data, incorporating control over computational errors, showcasing a commitment to precision in scientific inquiry.

Scientific Review of the Proarrhythmic Risks of Oligonucleotide Therapeutics: Are Dedicated ICH S7B/E14 Studies Needed for Low-Risk Modalities?

Oligonucleotide therapeutics (ONTs) represent a new modality with unique pharmacological and chemical properties that modulate gene expression with a high degree of target specificity mediated by complementary Watson-Crick base pair hybridization. To date, the proarrhythmic assessment of ONTs has been influenced by International Conference on Harmonization (ICH) E14 and S7B guidance. To document current hERG/QTc evaluation practices, we reviewed US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) Approval Packages (source: PharmaPendium.com) and collated preclinical and clinical studies for 17 marketed ONTs. In addition, clinical QTc data from 12 investigational ONTs were obtained from the literature. Of the marketed ONTs, eight were tested in the hERG assay with no inhibitory effect identified at the top concentration (range: 34-3,000 μM) tested. Fourteen of the ONTs were evaluated in nonhuman primate cardiovascular studies with 11 of them in dedicated telemetry studies. No effect on QTc intervals were observed (at high exposure multiples) in all studies. Clinically, four ONTs were evaluated in TQT studies; an additional six ONTs were assessed by concentration-QTc interval analysis, and six by routine safety electrocardiogram monitoring. None of the clinical studies identified a QTc prolongation risk; the same was true for the 12 investigational ONTs. A search of the FDA Adverse Event Database indicated no association between approved ONTs and proarrhythmias. Overall, the collective weight of evidence from 29 ONTs demonstrate no clinical proarrhythmic risk based on data obtained from ICH S7B/E14 studies. Thus, new ONTs may benefit from reduced testing strategies because they have no proarrhythmic risk, a similar cardiac safety profile as monoclonal antibodies, proteins, and peptides.

Tight interval state estimate for discrete‐time descriptor linear systems

SummaryIn this work, two state estimation methods are proposed for a class of discrete‐time descriptor linear systems subject to bounded uncertainties. First, we propose set‐valued estimator algorithm using symmetric boxes to compute rigorous bounds of the system states. The observer gains are calculated using norm to attenuate the effects of the uncertainties and to improve the accuracy of the proposed estimator. Second, to obtain tighter state enclosures, zonotopic set computation are developed instead of interval analysis to design a new set‐valued state estimation algorithm. The performances of the proposed state estimation approaches are highlighted on different illustrative examples.

Analysis of factors influencing Chinese vocabulary learning level based on big data technology

In recent years, significant progress has been made in the study of Chinese vocabulary acquisition, and the research content and scope have gradually expanded. Chinese vocabulary is the foundation for understanding and using language, and any language has developed on the basis of Chinese vocabulary. Many studies have shown that there are many factors that affect the acquisition of Chinese vocabulary, and the impact on Chinese vocabulary acquisition is also different. Among them, the influencing factors of Chinese vocabulary acquisition have gradually become a research hotspot, and a large number of related empirical studies have emerged. Based on the big data technology and the random effect model, this paper comprehensively analyzes whether the influencing factors of Chinese vocabulary acquisition as a second language are significant and investigates the effects of internal and external influencing factors, delayed post-test, specific factors and experimental interval time on the learning effect. External factors significantly impact Chinese vocabulary acquisition, including learning style, input factors and input methods. Through the subgroup analysis of the experimental interval, we find that the effect of Chinese vocabulary acquisition decreases with the extension of the experimental interval. Therefore, this paper holds an inverse relationship between the experimental interval and the development of Chinese vocabulary acquisition.

A formalization of constraint interval: A precussor to fuzzy interval analysis

This presentation outlines some aspects of interval analysis from the constraint interval point of view, focusing on some issues associated with mathematical analysis, especially those that are fundamental to fuzzy mathematical analysis. We begin by reviewing interval representations from the standard interval point of view and the study of intervals as functions of parameters varying in hypercubes, called constraint interval representation (CI). These concepts are used to define arithmetic operations between intervals, as well, interval-valued and interval functions for rational and transcedental functions. Issues of semantics as they apply to interval analysis and fuzzy interval analysis are discussed. The definition of constrained interval function extension is used to prove that each CI arithmetic operation is an isotonic inclusion.

Importance and Uncertainty of λ-Estimation for Box–Cox Transformations to Compute and Verify Reference Intervals in Laboratory Medicine

Reference intervals play an important role in medicine, for instance, for the interpretation of blood test results. They are defined as the central 95% values of a healthy population and are often stratified by sex and age. In recent years, so-called indirect methods for the computation and validation of reference intervals have gained importance. Indirect methods use all values from a laboratory, including the pathological cases, and try to identify the healthy sub-population in the mixture of values. This is only possible under certain model assumptions, i.e., that the majority of the values represent non-pathological values and that the non-pathological values follow a normal distribution after a suitable transformation, commonly a Box–Cox transformation, rendering the parameter λ of the Box–Cox transformation as a nuisance parameter for the estimation of the reference interval. Although indirect methods put high effort on the estimation of λ, they come to very different estimates for λ, even though the estimated reference intervals are quite coherent. Our theoretical considerations and Monte-Carlo simulations show that overestimating λ can lead to intolerable deviations of the reference interval estimates, whereas λ=0 produces usually acceptable estimates. For λ close to 1, its estimate has limited influence on the estimate for the reference interval, and with reasonable sample sizes, the uncertainty for the λ-estimate remains quite high.

Evaluation of clinical and imaging features for differentiating rhabdomyosarcoma from neuroblastoma in pediatric soft tissue.

In this study, we developed a nomogram predictive model based on clinical, CT, and MRI parameters to differentiate soft tissue rhabdomyosarcoma (RMS) from neuroblastoma (NB) in children preoperatively. A total of 103 children with RMS (n=37) and NB (n=66) were enrolled in the study from December 2012 to July 2023. The clinical and imaging data (assessed by two experienced radiologists) were analyzed using univariate analysis, and significant factors were further analyzed by multivariable logistic regression using the forward LR method to develop the clinical model, radiological model, and integrated nomogram model, respectively. The diagnostic performances, goodness of fit, and clinical utility of the integrated nomogram model were assessed using the area under the curve (AUC) of the receiver operator characteristics curve (ROC) with a 95% confidence interval (95% CI), calibration curve, and decision curve analysis (DCA) curves, respectively. Diagnostic efficacy between the model and radiologists' interpretations was examined. The median age at diagnosis in the RMS group was significantly older than the NB group (36.0 months vs. 14.5 months; P=0.003); the fever rates in RMS patients were significantly lower than in patients with NB (0.0% vs.16.7%; P=0.022), and the incidence of palpable mass was higher in patients with RMS compared with the NB patients (89.2% vs. 34.8%; P<0.001). Compare NB on image features: RMS occurred more frequently in the head and neck and displayed homogeneous density on non-enhanced CT than NB (48.6% vs. 9.1%; 35.3% vs. 13.8%, respectively; all P<0.05), and the occurrence of characteristics such as calcification, encasing vessels, and intraspinal tumor extension was significantly less frequent in RMS children compared to children with NB (18.9% vs. 84.8%; 13.5% vs. 34.8%; 2.7% vs. 50.0%, respectively; all P <0.05). Two, three, and four features were identified as independent parameters by multivariate logistic regression analysis to develop the clinical, radiological, and integrated nomogram models, respectively. The AUC value (0.962), calibration curve, and DCA showed that the integrated nomogram model may provide better diagnostic performance, good agreement, and greater clinical net benefits than the clinical model, radiological model, and radiologists' subjective diagnosis. The clinical and imaging features-based nomogram has potential for helping radiologists distinguish between pediatric soft tissue RMS and NB patients preoperatively, and reduce unnecessary interventions.

Implications of an Altered Gut Microbiome in Rat Experimental Vascularized Composite Transplantation.

Vascularized composite allotransplantation is a reconstructive option after severe injury but is fraught with complications, including transplant rejection due to major histocompatibility complex mismatch in the context of allogeneic transplant, which in turn is due to altered immuno-inflammation secondary to transplant. The immunosuppressant tacrolimus can prevent rejection. Because tacrolimus is metabolized predominantly by the gut, this immunosuppressant alters the gut microbiome in multiple ways, thereby possibly affecting immunoinflammation. We performed either allogeneic or syngeneic transplant with or without tacrolimus in rats. We quantified protein-level inflammatory mediators in the skin, muscle, and plasma and assessed the diversity of the gut microbiome through 16S RNA analysis at several timepoints over 31 days posttransplant. Statistical analysis highlighted a complex interaction between major histocompatibility complex and tacrolimus therapy on the relative diversity of the microbiome. Time-interval principal component analysis indicated numerous significant differences in the tissue characteristics of inflammation and gut microbiome that varied over time and across experimental conditions. Classification and regression tree analysis suggested that both inflammatory mediators in specific tissues and changes in the gut microbiome are useful in characterizing the temporal dynamics of posttransplant inflammation. Dynamic network analysis highlighted unique changes in Methanosphaera that were correlated with Peptococcusin allogeneic transplants with and without tacrolimus versus Prevotella in syngeneic transplant with tacrolimus, suggesting that alterations in Methanosphaera might be a biomarker of vascularized composite allotransplant rejection. Our results suggest a complex interaction among major histocompatibility complex, local and systemic immuno-inflammation, and tacrolimus therapy and highlight the potential for novel insights into vascularized composite allotransplant from computational approaches.

Bioframe: operations on genomic intervals in Pandas dataframes.

Genomic intervals are one of the most prevalent data structures in computational genome biology, and used to represent features ranging from genes, to DNA binding sites, to disease variants. Operations on genomic intervals provide a language for asking questions about relationships between features. While there are excellent interval arithmetic tools for the command line, they are not smoothly integrated into Python, one of the most popular general-purpose computational and visualization environments. Bioframe is a library to enable flexible and performant operations on genomic interval dataframes in Python. Bioframe extends the Python data science stack to use cases for computational genome biology by building directly on top of two of the most commonly-used Python libraries, NumPy and Pandas. The bioframe API enables flexible name and column orders, and decouples operations from data formats to avoid unnecessary conversions, a common scourge for bioinformaticians. Bioframe achieves these goals while maintaining high performance and a rich set of features. Bioframe is open-source under MIT license, cross-platform, and can be installed from the Python Package Index. The source code is maintained by Open2C on GitHub at https://github.com/open2c/bioframe.

Neurological and immunological adverse events after pneumococcal conjugate vaccine in children using national immunization programme registry data.

Despite the general consensus on the safety of pneumococcal conjugate vaccine (PCV), safety concerns unveiled during post-licensure surveillance need to be addressed. We investigated whether there is a transient increased risk following a three-dose series of pneumococcal conjugate vaccine (PCV) administered at 2, 4 and 6 months of age. This was a population-based cohort study using the Korea immunization registry data linked to nationwide administrative claims data. Self-controlled risk interval analysis was conducted for PCV recipients who had an outcome of interest within pre-defined risk and control intervals between 2018 and 2022. The outcomes were anaphylaxis, asthma, encephalopathy, febrile seizure, Kawasaki disease and thrombocytopenia. We used conditional Poisson regression model to estimate the incidence rate ratios (IRRs) and 95% confidence intervals (CIs) comparing the outcomes in the risk and control intervals. Of 1 114 096 PCV recipients, 8661 had outcomes either in the risk or control intervals. Their mean age at Dose 1 was 10.0 weeks, 58.3% were boys, and 85.3% received 13-valent PCV. PCV was not associated with an increased risk of any outcomes except for febrile seizure. There were 408 (56.0%) cases of febrile seizure in the risk interval, corresponding to an IRR of 1.27 (95% CI 1.10-1.47). It is reassuring to note that there was no increased risk of the potential safety concerns following PCV administration. Despite the transient increased risk of febrile seizure, absolute numbers of cases were small. Febrile seizure is generally self-limiting with a good prognosis, and should not discourage parents or caregivers from vaccinating their children.

A study on the epidemiology of brucellosis in bovine population of peri-urban and rural areas of district Multan, southern Punjab, Pakistan

BackgroundBrucellosis is a zoonotic disease caused by a bacterial pathogen belonging to the genus Brucella. It is one of the most frequent bacterial zoonoses globally but unfortunately, it is still considered as a neglected disease in the developing world. Keeping in view, this study was conducted to determine the prevalence and risk determinants of brucellosis in large ruminants of peri-urban and rural areas of district Multan-Pakistan. For this purpose, blood samples (n = 490) were collected from the cattle (n = 245) and buffalo (n = 245) population of the study area and subjected to preliminary screening of brucellosis using local and imported RBPT reagents. All the samples were further analyzed using commercially available multi-specie indirect ELISA kit followed by their confirmation by PCR using genus and species-specific primers. Data obtained from lab analysis and questionnaires were subjected to statistical analysis for Pearson Chi-square, Odds Ratio and Confidence intervals (95%).ResultsThe results showed that the maximum seropositivity was recorded with local RBPT reagent (VRI, Pakistan; 12.45%; 95%CI = 9.72–15.65%) followed by RBPT-IDEXX (12.24%; 95%CI = 9.52–15.45%) and RBPT-ID.vet (11.84%; 95%CI = 9.18–14.95%) however statistical difference was non-significant (P = 0.956). The ELISA results showed an overall seroprevalence rate of 11.22% (95%CI = 8.59–14.33%) with comparatively higher rate in cattle (12.65%; 95%CI = 8.82–17.44%) as compared to buffaloes (9.80%; 95%CI = 6.49–14.15%). The PCR analysis confirmed the presence of genus Brucella in all seropositive samples whereas frequency of B. abortus and B. melitensis in seropositive samples was 80% and 20%, respectively. The co-existence of both species was also observed in 5.45% samples. The statistical analysis showed a significant association of bovine brucellosis with herd size, breed, reproductive disorders, mode of insemination, educational status and farmers’ awareness about brucellosis (P < 0.05). Conversely, locality, age, weight, gender, pregnancy status, parity and puberty status had no associations with brucellosis (P > 0.05).ConclusionIn conclusion, brucellosis is prevalent in large ruminants of district Multan, Pakistan. It is suggested to devise and implement stringent policies for the effective control and prevention of brucellosis in the region. Further, the current situation also warrants the need to strengthen interdisciplinary coordination among veterinarians and physicians in one health perspective to ensure and strengthen the human and animal health care systems in the region.

Uncertainty analysis and updated user guidance for interspecies correlation estimation models and low toxicity compounds.

Interspecies correlation estimation (ICE) models are log-linear relationships of acute sensitivity between two species that estimate the sensitivity of an untested species from the known sensitivity of a surrogate. As ICE model use increases globally, additional user guidance is required to ensure consistent use across chemicals and applications. The present study expands ICE uncertainty analyses and user guidance with a focus on low toxicity compounds whose acute values (i.e., reported as mg/L) can be greater than those used to develop a model. In these cases, surrogate values may be outside the ICE model domain and require additional extrapolations to predict acute toxicity. We use the extensive, standardized acute toxicity database underlying ICE models to broadly summarize inter-test variability of acute toxicity data as a measure by which model prediction accuracy can be evaluated. Using the data and models found on the USEPA Web-ICE (www3.epa.gov/webice), we created a set of "truncated" models from data corresponding to the lower 75th percentile of surrogate toxicity. We predicted toxicity for chemicals in the upper 25th percentile as both μg/L beyond the model domain and converted to mg/L (i.e., "scaled" value) and compared these predictions with those from cross-validation of whole ICE models and to the measured value. For ICE models with slopes in the range 0.66-1.33, prediction accuracy of scaled values did not differ from the accuracy of the models when data were entered as μg/L within or beyond the model domain. An uncertainty analysis of ICE confidence intervals was conducted and an interval range of two orders of magnitude was determined to minimize type I and II errors when accepting or rejecting ICE predictions. We updated the ICE user guidance based on these analyses to advance the state of the science for ICE model application and interpretation. Integr Environ Assess Manag 2024;20:1554-1565. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Solving systems of homogeneous linear algebraic equations in pricing problems of an alternative financial model

Subject. This article focuses on the problems of solving systems of homogeneous linear algebraic equations in the pricing problems of an alternative financial model. Objectives. The article aims to develop a non-trivial approach to solving the problem of pricing from the scratch in the construction of a national alternative financial model, which boils down to solving large homogeneous systems of linear algebraic equations, complicated by the fuzzy setting of initial parameters (initial data) within the framework of creating a State model of a socially oriented economy, guaranteed to ensure economic, military and other types of integrated security of the Russian Federation. Methods. For the study, we used tensorial notations involving the use of elements of linear algebra, including operations with matrices and vectors, the apparatus of the fuzzy set theory (interval arithmetic), as well as methods and principles of scientific research and complex logical analysis and modeling of pricing and technological processes. Results. The article shows that an effective non-zero (different from zero, i.e. classical, trivial) solution of systems of homogeneous algebraic equations in pricing problems of an alternative financial model from the scratch is possible if the initial parameters are specified by fuzzy sets, which significantly simplifies the procedure of the computational process and the volume of arithmetic operations. Conclusions. The results obtained can be used in structures involved in the creation and testing of financial and economic models both at the federal and the Russian Federation constituent entity levels, as well as in governmental and non-governmental organizations dealing with the issues of pricing, planning and logistics support for business entities not only in the Russian Federation, but also in other countries experiencing problems in the field of financing.

Fractional Mercer's Hermite–Hadamard type inequalities in the frame of interval analysis and its applications to matrix

Fractional Mercer's Hermite–Hadamard type inequalities in the frame of interval analysis and its applications to matrix

Tridiagonal Interval Matrix: Exploring New Perspectives and Application

Tridiagonal interval matrices are relevant in diverse applications, especially in dealing with parameter estimation, optimization and circuit analysis uncertainties. This research paper aims to improve the computational efficiency of obtaining the inverse of a general tridiagonal interval matrix. This matrix is pivotal in electric circuit analysis. We achieve this by employing interval arithmetic operations in the LU decomposition process, enabling effective handling of circuit parameter uncertainties. This approach generates an inverse interval matrix that addresses uncertainties in circuit analyses.

The impact of consumers’ preferences for domestic food on dietary sustainability

A sustainable dietary transition requires knowledge of the drivers and barriers of dietary choices. We investigate the role of preferences for domestic food, as well as environmental and health concerns, as drivers for the consumption of red and white meat, fish, ready-made plant-based food products and self-identification as some type of meat reducer (flexitarian, vegetarian, or vegan). A survey of 1102 consumers was conducted in Norway with questions about food attitudes, beliefs and preferences regarding health, the environment and domestic food as well as dietary habits and demographics. The results from interval and logistic regression analyses show that stronger preferences for domestic food are associated with higher consumption of red meat and a lower likelihood of eating plant-based food and identifying as a meat reducer. Health concerns are associated with higher consumption of white meat and fish, and environmental concern is associated with lower consumption of white meat and a higher likelihood of eating plant-based food. The results also confirm previous research results that disbelief regarding the negative health and environmental impacts of meat correlate with higher meat consumption and a lower likelihood of eating plant-based food. In addition, we find that people who believe that Norway is a country primarily suited for livestock production have higher consumption of meat and a lower likelihood of eating plant-based food. We conclude that to make certain consumers transition away from meat, it is important to provide domestically produced, plant-based alternatives and to implement policy measures that will generate positive storylines of improved farmer livelihoods.

An analysis of Pompe newborn screening data: a new prevalence at birth, insight and discussion.

Primary: Establish a new figure for prevalence at birth for Pompe disease by collecting and analyzing the largest relevant dataset to date and using that result to project population prevalence at birth in a novel way. Secondary: Compare these results to previous analyses to offer a framework for evaluating 'frequency' data that can be applied to other rare, genetic diseases, along with methods to assess quality of estimates.