Distribution Of Intervals Research Articles

CARL: Compiler Assigned Reference Leasing

Data movement is a common performance bottleneck, and its chief remedy is caching. Traditional cache management is transparent to the workload: data that should be kept in cache are determined by the recency information only, while the program information, i.e., future data reuses, is not communicated to the cache. This has changed in a new cache design named Lease Cache . The program control is passed to the lease cache by a compiler technique called Compiler Assigned Reference Lease (CARL). This technique collects the reuse interval distribution for each reference and uses it to compute and assign the lease value to each reference. In this article, we prove that CARL is optimal under certain statistical assumptions. Based on this optimality, we prove miss curve convexity, which is useful for optimizing shared cache, and sub-partitioning monotonicity, which simplifies lease compilation. We evaluate the potential using scientific kernels from PolyBench and show that compiler insertions of up to 34 leases in program code achieve similar or better cache utilization (in variable size cache) than the optimal fixed-size caching policy, which has been unattainable with automatic caching but now within the potential of cache programming for all tested programs and most cache sizes.

Dynamics of a semiconductor laser with feedback and modulation: experiments and model comparison.

We study experimentally and numerically the dynamics of a semiconductor laser near threshold, subject to optical feedback and sinusoidal current modulation. The laser operates in the low frequency fluctuation (LFF) regime where, without modulation, the intensity shows sudden spikes at irregular times. Under particular modulation conditions the spikes lock to the modulation and their timing becomes highly regular. While the modulated LFF dynamics has received a lot of attention, an in-depth comparison with the predictions of the Lang-Kobayashi (LK) model has not yet been performed. Here we use the LK model to simulate the laser dynamics and use the Fano factor to quantify the regularity of the timing of the spikes. The Fano factor is calculated by counting the number of spikes in successive segments of the intensity time-series and keeps information about temporal order in the spike sequence that is lost when the analysis is based on the distribution of inter-spike intervals. Here we compare the spike timing regularity in experimental and in simulated spike sequences as a function of the modulation amplitude and frequency and find a good qualitative agreement. We find that in both experiments and simulation for appropriate conditions the spike timing can be highly regular, as revealed by very small values of the Fano factor.

Estimation of Serial Interval and Reproduction Number to Quantify the Transmissibility of SARS-CoV-2 Omicron Variant in South Korea.

The omicron variant (B.1.1.529) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was the predominant variant in South Korea from late January 2022. In this study, we aimed to report the early estimates of the serial interval distribution and reproduction number to quantify the transmissibility of the omicron variant in South Korea between 25 November 2021 and 31 December 2021. We analyzed 427 local omicron cases and reconstructed 73 transmission pairs. We used a maximum likelihood estimation to assess serial interval distribution from transmission pair data and reproduction numbers from 74 local cases in the first local outbreak. We estimated that the mean serial interval was 3.78 (standard deviation, 0.76) days, which was significantly shorter in child infectors (3.0 days) compared to adult infectors (5.0 days) (p < 0.01). We estimated the mean reproduction number was 1.72 (95% CrI, 1.60–1.85) for the omicron variant during the first local outbreak. Strict adherence to public health measures, particularly in children, should be in place to reduce the transmission risk of the highly transmissible omicron variant in the community.

On the conditional noncentral beta distribution

The beta family owes its privileged status within unit interval distributions to several relevant features such as, for example, easiness of interpretation and versatility in modeling different types of data. However, the flexibility of its density at the endpoints of the support is poor enough to prevent from properly modeling the data portions having values next to zero and one. Such a drawback can be overcome by resorting to the class of the noncentral beta distributions. Indeed, the latter allows the density to take on arbitrary positive and finite limits which have a really simple form. Nevertheless, the analytical and mathematical complexity of this distribution poses strong limitations on its use as a model for data on the real interval (0, 1). That said, an in‐depth study of a newly found analogue of the noncentral beta distribution is carried out in this article. The latter preserves the applicative potential of the standard noncentral beta class but with the advantage of showing a more straightforward and easily handleable density.

Estimation of R(t) based on illness onset data: An analysis of 1907–1908 smallpox epidemic in Tokyo

R(t), the actual average number of secondary cases per primary case at calendar time t, is epidemiologically useful in assessing transmission dynamics in a population with varying susceptibility levels. However, a technical limitation of existing methods of estimating R(t) is the reliance on the daily number of cases with illness onset and the distribution of the serial interval, although the estimator of R(t) should be calculated as the ratio of newly infected cases at time t to the total number of potentially infectious people at the same time. Using historical data of a smallpox outbreak in Tokyo City, Japan, approximately 100 years ago, we propose a new method to compute R(t) that can be estimated using information on illness onset. Our method decomposes the mechanism of transmission into two distinct pieces of information: the frequency of secondary transmission relative to disease age and the probability density function of the incubation period. Employing a piecewise constant model, our maximum likelihood estimates of R(t) dropped below unity (0.6; 95% confidence interval: 0.5–0.7) for the period from Day 64 to Day 79, indicating that the epidemic was under control in this period. R(t) was continuously below one through the remaining days. The model prediction captured the overall observed pattern of the epidemic well. Our method is appropriate for acute infectious diseases other than smallpox for which variations in infectivity relative to disease age should be considered to correctly estimate the transmission potential, such as the ongoing global epidemic of coronavirus disease 2019 (COVID-19).

Uncovering Characteristic Response Paths of a Population

We propose an approach for uncovering characteristic response paths of a population from an individual-level multivariate time series data set. The approach is based on a model that accommodates a set of arbitrary distributions for endogenous variables and interstep intervals and variables. The model enables reliable estimation of individual-level parameters by uncovering and statistically pooling clusters of similar individuals. We show that using such a model one can distribute the response of an outcome variable to an impulse over all possible preceding activity sequences. When a few such sequences explain most of the response, they describe the population’s characteristic response paths from the impulse to the outcome. We apply the proposed approach to a customer touchpoint data set from a large multichannel specialty retailer. This application uncovers six customer segments, each with unique characteristic paths to purchase. These paths provide insights into the behavior of customers and the optimal over-time communication strategy for different customer segments. Summary of Contribution: Uncovering users’ paths through physical and virtual spaces has been of considerable interest in the computing and operations research domain. The existing research suggests a demand for visualizing the primary paths of agents through geographic, online, and activity spaces. Thus far, most of the research have developed approaches that are unique to specific domains providing insight into the domain in the process. There is a need for a general statistically robust approach that can be applied to a broad range of domains to uncover variable sequences that lead to outcomes of interest. We propose a computational approach to uncover characteristic response paths of a population from an individual-level multivariate time series data set. The approach is based on a statistical model that accommodates arbitrary and mixed set of distributions for the endogenous variables, accommodates intersession intervals and variables, and reliably estimates individuals’ parameters through statistical pooling by uncovering clusters of similar members. These features make the proposed model suitable for a large variety of real-world datasets. We show that using such a model one can extract characteristic paths over possible activity sequences starting from an impulse leading up to a target variable of interest.

An Extended SORA Method for Hybrid Reliability-Based Design Optimization

In many practical engineering problems, distributions of some random variables may not be precisely known or even only the variation ranges can be given, due to which the Reliability-Based Design Optimization (RBDO) methods cannot be applied directly. In this paper, a hybrid RBDO model is established to deal with hybrid uncertainties including random variables with interval distribution parameters and coexistence of random and interval variables. The hybrid uncertainties lead to an interval of reliability, thus giving rise to a triple-loop optimization problem, which burdens the computational effort significantly. Therefore, an extended Sequential Optimization and Reliability Assessment (SORA) method is proposed, where the worst-case point over the bounded domain of interval uncertainty is employed to replace reliability constraint with deterministic optimization. Therefore, the hybrid RBDO problem can be efficiently solved by a series of deterministic optimization procedures. The efficiency and accuracy of the proposed method are illustrated through several numerical examples.

Robust minimum cost consensus models with aggregation operators under individual opinion uncertainty

Individual opinion is one of the vital factors influencing the consensus in group decision-making, and is often uncertain. The previous studies mostly used probability distribution, interval distribution or uncertainty distribution function to describe the uncertainty of individual opinions. However, this requires an accurate understanding of the individual opinions distribution, which is often difficult to satisfy in real life. In order to overcome this shortcoming, this paper uses a robust optimization method to construct three uncertain sets to better characterize the uncertainty of individual initial opinions. In addition, we used three different aggregation operators to obtain collective opinions instead of using fixed values. Furthermore, we applied the numerical simulations on flood disaster assessment in south China so as to evaluate the robustness of the solutions obtained by the robust consensus models that we proposed. The results showed that the proposed models are more robust than the previous models. Finally, the sensitivity analysis of uncertain parameters was discussed and compared, and the characteristics of the proposed models were revealed.

Analysis of Al adatoms random hopping on Si(100)-c(4 [formula omitted] 2) surface observed by STM at low temperature: Determination of the hopping barriers

Mobility of single aluminium atoms adsorbed on Si(100)-c(4×2) surface was studied by means of scanning tunneling microscopy (STM) at a temperature of 115 K. Due to a strongly anisotropic surface migration along silicon dimer rows the hopping in one-dimensional (1-D) bounded areas of various sizes was observed. Distributions of distances traversed in a time between acquisition of two subsequent STM images were obtained. The experimental conditions were interpreted by a model of 1-D random walk of Al adatoms in areas of limited size with reflective boundaries. Kinetic Monte Carlo (KMC) simulations were used for analysing dependence of a calculated activation energy for a single hop on a size of the 1-D bounded area. The developed method allowed restoration of a real value of the activation energy for the Al adatom hopping in a direction parallel to the silicon dimer rows unaffected by the limited size of the bounded area. The activation energy for hops in a direction perpendicular to the dimer rows was estimated from a distribution of time intervals between individual perpendicular hops.

The Spatiotemporal Link of Temporal Expectations: Contextual Temporal Expectation Is Independent of Spatial Attention.

Temporal expectation is the ability to construct predictions regarding the timing of events, based on previously experienced temporal regularities of different types. For example, cue-based expectations are constructed when a cue validly indicates when a target is expected to occur. However, in the absence of such cues, expectations can be constructed based on contextual temporal information, including the onset distribution of the event and recent prior experiences, both providing implicit probabilistic information regarding the timing of the event. It was previously suggested that cue-based temporal expectation is exerted via synchronization of spatially specific neural activity at a predictable time of a target, within receptive fields corresponding to the expected location of the target. Here, we tested whether the same theoretical model holds for contextual temporal effects. Participants (n = 40, 25 females) performed a speeded spatial-cuing detection task with two-thirds valid spatial cues. The hazard-rate function of the target was modulated by varying the foreperiod-the interval between the spatial cue and the target-among trials and was manipulated between groups by changing the interval distribution. Reaction times were analyzed using both frequentist and Bayesian generalized linear mixed models, accounting for hazard and sequential effects. Results showed that the effects of contextual temporal structures on reaction times were independent of spatial attention. This suggests that the spatiotemporal mechanisms, thought to account for cue-based expectation, cannot explain other sources of temporal expectations. We conclude that expectations based on contextual structures have different characteristics than cue-based temporal expectation, suggesting reliance on distinct neural mechanisms.SIGNIFICANCE STATEMENT Temporal expectation is the ability to predict an event onset based on temporal regularities. A neurophysiological model suggested that temporal expectation relies on the synchronization of spatially specific neurons whose receptive fields represent the attended location. This model predicts that temporal expectation would be evident solely within the locus of spatial attention. Existing evidence supported this model for expectation based on associations between a temporal cue and a target, but here we show that it cannot account for temporal expectation that is based on contextual information, that is, the distribution of intervals and recent priors. These findings reveal the existence of different predictive mechanisms for cued and contextual temporal predictions, with the former depending on spatial attention and the latter nonspatially specific.

FORECASTING AND ON THE INFLUENCE OF CLIMATIC FACTORS ON RISING DENGUE INCIDENCE IN BAGUIO CITY, PHILIPPINES

Dengue fever or dengue has been a concern for individuals living in Baguio City, Philippines. Every year, incidence counts rise during rainy seasons experienced from June to October. Several researches suggest that meteorological factors have great influence on the life, growth, and reproduction of dengue-carrying mosquitoes, resulting in higher dengue incidence in the area. With the continuing rise of dengue incidence in Baguio City, we aim to forecast dengue incidence in the area for the year 2019, starting from January until the end of the outbreak period in the area. Here, we use the projections package of R as it involves the serial interval distribution and 𝑅𝑡 value of dengue incidence. We also aim to use multiple linear regression analysis to determine if meteorological factors have significant effects in the rise of dengue incidence in the city. With the inclusion of time-varying reproduction number and serial interval distribution of dengue, we projected that dengue incidence may reach up to 101 cases by June 16, 2021, and without further actions, cases may rise up to 529 cases by August 29, 2021. Based on the average two-year period, such increase is attributed to relative humidity and average temperature as these are the most significant factors associated with dengue incidence based on the MLR analysis. The highest and mean maximum temperatures remain as key meteorological variables that influence dengue incidence in the city. As early as possible, local officials are recommended to uphold proper safety and health procedures in preventing the spread of dengue in Baguio City.

A single-loop method for reliability-based design optimization with interval distribution parameters

The Reliability-Based Design Optimization (RBDO) provides an effective way to obtain the optimum design in the presence of random uncertainties which follow the precise probability distribution function in the structural optimization design. However, in many practical engineering problems, the probability distribution which describes the stochastic nature of the uncertainties cannot be precisely obtained due to limited information. To quantify this kind of imprecise uncertainties, a probability-interval hybrid model emerged, where all uncertain variables are treated as random variables while some distribution parameters can only be given variation intervals. For such kind of uncertainties, this paper establishes a hybrid reliability-based design optimization model and proposes a single-loop solution algorithm. The interval parameters lead to an interval of reliability for each constraint function, thus giving rise to a triple-loop optimization problem for the hybrid RBDO, which is difficult to solve due to the unaffordable computational effort and the hinder of convergency. In this paper, the Karush–Kuhn–Tucker (KKT) optimality conditions of the inner loops are imposed as equivalent deterministic equality constraints. The original triple-loop optimization is thus converted into an equivalent single-loop problem, which alleviates the computational demand significantly. The efficiency and accuracy of the proposed Single-Loop Method (SLM) is verified through several practical engineering problems.

Novel Epidemic Metrics to Communicate Outbreak Risk at the Municipality Level: Dengue and Zika in the Dominican Republic.

Arboviruses remain a significant cause of morbidity, mortality and economic cost across the global human population. Epidemics of arboviral disease, such as Zika and dengue, also cause significant disruption to health services at local and national levels. This study examined 2014–2016 Zika and dengue epidemic data at the sub-national level to characterise transmission across the Dominican Republic. For each municipality, spatio-temporal mapping was used to characterise disease burden, while data were age and sex standardised to quantify burden distributions among the population. In separate analyses, time-ordered data were combined with the underlying disease migration interval distribution to produce a network of likely transmission chain events, displayed using transmission chain likelihood matrices. Finally, municipal-specific reproduction numbers (Rm) were established using a Wallinga–Teunis matrix. Dengue and Zika epidemics peaked during weeks 39–52 of 2015 and weeks 14–27 of 2016, respectively. At the provincial level, dengue attack rates were high in Hermanas Mirabal and San José de Ocoa (58.1 and 49.2 cases per 10,000 population, respectively), compared with the Zika burden, which was highest in Independencia and San José de Ocoa (21.2 and 13.4 cases per 10,000 population, respectively). Across municipalities, high disease burden was observed in Cotuí (622 dengue cases per 10,000 population) and Jimani (32 Zika cases per 10,000 population). Municipal infector–infectee transmission likelihood matrices identified seven 0% likelihood transmission events throughout the dengue epidemic and two 0% likelihood transmission events during the Zika epidemic. Municipality reproduction numbers (Rm) were consistently higher, and persisted for a greater duration, during the Zika epidemic (Rm = 1.0) than during the dengue epidemic (Rm < 1.0). This research highlights the importance of disease surveillance in land border municipalities as an early warning for infectious disease transmission. It also demonstrates that a high number of importation events are required to sustain transmission in endemic settings, and vice versa for newly emerged diseases. The inception of a novel epidemiological metric, Rm, reports transmission risk using standardised spatial units, and can be used to identify high transmission risk municipalities to better focus public health interventions for dengue, Zika and other infectious diseases.

Using Simulation Optimization to Solve Patient Appointment Scheduling and Examination Room Assignment Problems for Patients Undergoing Ultrasound Examination.

This study investigates patient appointment scheduling and examination room assignment problems involving patients who undergo ultrasound examination with considerations of multiple examination rooms, multiple types of patients, multiple body parts to be examined, and special restrictions. Following are the recommended time intervals based on the findings of three scenarios in this study: In Scenario 1, the time interval recommended for patients’ arrival at the radiology department on the day of the examination is 18 min. In Scenario 2, it is best to assign patients to examination rooms based on weighted cumulative examination points. In Scenario 3, we recommend that three outpatients come to the radiology department every 18 min to undergo ultrasound examinations; the number of inpatients and emergency patients arriving for ultrasound examination is consistent with the original time interval distribution. Simulation optimization may provide solutions to the problems of appointment scheduling and examination room assignment problems to balance the workload of radiological technologists, maintain high equipment utilization rates, and reduce waiting times for patients undergoing ultrasound examination.

Enhanced Beetle Antennae Algorithm for Chemical Dynamic Optimization Problems’ Non-Fixed Points Discrete Solution

Dynamic optimization is an important research topic in chemical process control. A dynamic optimization method with good performance can reduce energy consumption and prompt production efficiency. However, the method of solving the problem is complicated in the establishment of the model, and the process of solving the optimal value has a certain degree of difficulty. Based on this, we proposed a non-fixed points discrete method of an enhanced beetle antennae optimization algorithm (EBSO) to solve this kind of problem. Firstly, we converted individual beetles into groups of beetles to search for the best and increase the diversity of the population. Secondly, we introduced a balanced direction strategy, which explored extreme values in new directions before the beetles updated their positions. Finally, a spiral flight mechanism was introduced to change the situation of the beetles flying straight toward the tentacles to prevent the traditional algorithm from easily falling into a certain local range and not being able to jump out. We applied the enhanced algorithm to four classic chemical problems. Meanwhile, we changed the equal time division method or unequal time division method commonly used to solve chemical dynamic optimization problems, and proposed a new interval distribution method—the non-fixed points discrete method, which can more accurately represent the optimal control trajectory. The comparison and analysis of the simulation test results with other algorithms for solving chemical dynamic optimization problems show that the EBSO algorithm has good performance to a certain extent, which further proves the effectiveness of the EBSO algorithm and has a better optimization ability.

Effect of Bottom Blowing Mode on Fluid Flow and Mixing Behavior in Converter

Bottom blowing agitation plays a crucial role in improving the reaction kinetics condition of molten bath during the steelmaking process. Herein, the influence of bottom blowing mode on the flow and mixing characteristics of molten bath and the abrasion characteristics of refractory lining in a 6:1 scaled-down model of a 100 t converter were investigated using physical and numerical simulations together. Eight bottom blowing modes were designed (uniform, three-point linear co-direction, three-point linear unco-direction, two-point linear, circumferential linear, A-type, V-type, and triangle alternating). The results indicated that bottom blowing mode has a significant effect on the local flow field at the inner ring of bottom tuyeres, the velocity interval distribution, and the turbulent kinetic energy, which in turn determines the tracer diffusion path and rate as well the mixing time of molten bath. Reasonable non-uniform bottom blowing modes promote the interaction between the various stirring sub-zones of the molten bath. Among them, the three-point linear co-direction mode and A-type mode have the highest mixing efficiency under the conditions of bottom blowing and combined blowing, respectively, which is superior to the uniform mode. In addition, the bottom blowing mode changed the location and degree of abrasion of the refractory lining, and the total abrasion of the non-uniform mode was reduced. The average value and fluctuation degree of integral wall shear stress for the A-type mode were minimal.

Clogging and avalanches in quasi-two-dimensional emulsion hopper flow.

We experimentally and computationally study the flow of a quasi-two-dimensional emulsion through a constricting hopper shape. Our area fractions are above jamming such that the droplets are always in contact with one another and are in many cases highly deformed. At the lowest flow rates, the droplets often clog and thus exit the hopper via intermittent avalanches. At the highest flow rates, the droplets exit continuously. The transition between these two types of behaviors is a fairly smooth function of the mean strain rate. The avalanches are characterized by a power-law distribution of the time interval between droplets exiting the hopper, with long intervals between the avalanches. Our computational studies reproduce the experimental observations by adding a flexible compliance to the system (in other words, a finite stiffness of the sample chamber). The compliance results in continuous flow at high flow rates, and allows the system to clog at low flow rates leading to avalanches. The computational results suggest that the interplay of the flow rate and compliance controls the presence or absence of the avalanches.

Transmission and containment of the SARS-CoV-2 Delta variant of concern in Guangzhou, China: A population-based study.

BackgroundThe first community transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant of concern (VOC) in Guangzhou, China occurred between May and June 2021. Herein, we describe the epidemiological characteristics of this outbreak and evaluate the implemented containment measures against this outbreak.Methodology/Principal findingsGuangzhou Center for Disease Control and Prevention provided the data on SARS-CoV-2 infections reported between 21 May and 24 June 2021. We estimated the incubation period distribution by fitting a gamma distribution to the data, while the serial interval distribution was estimated by fitting a normal distribution. The instantaneous effective reproductive number (Rt) was estimated to reflect the transmissibility of SARS-CoV-2. Clinical severity was compared for cases with different vaccination statuses using an ordinal regression model after controlling for age. Of the reported local cases, 7/153 (4.6%) were asymptomatic. The median incubation period was 6.02 (95% confidence interval [CI]: 5.42–6.71) days and the means of serial intervals decreased from 5.19 (95% CI: 4.29–6.11) to 3.78 (95% CI: 2.74–4.81) days. The incubation period increased with age (P<0.001). A hierarchical prevention and control strategy against COVID-19 was implemented in Guangzhou, with Rt decreasing from 6.83 (95% credible interval [CrI]: 3.98–10.44) for the 7-day time window ending on 27 May 2021 to below 1 for the time window ending on 8 June and thereafter. Individuals with partial or full vaccination schedules with BBIBP-CorV or CoronaVac accounted for 15.3% of the COVID-19 cases. Clinical symptoms were milder in partially or fully vaccinated cases than in unvaccinated cases (odds ratio [OR] = 0.26 [95% CI: 0.07–0.94]).Conclusions/SignificanceThe hierarchical prevention and control strategy against COVID-19 in Guangzhou was timely and effective. Authorised inactivated vaccines are likely to contribute to reducing the probability of developing severe disease. Our findings have important implications for the containment of COVID-19.

Quantification and prediction of urban sprawl and surface temperature and assessment of their impacts on the environment: Case El Jadida (Morocco)

In this study, we aim to evaluate urban sprawl dynamics and surface temperature changes between 1985 and 2019, and to predict a future scenario of their evolution in 2027 and 2038 in great El Jadida city using Landsat images. We compared the resulting maps using spatio-temporal and statistical analysis. The results reveal that the built-up area increased by 5,121 ha (247%) between 1985 and 2019 (34 years). During the same period, the surface temperature was mutated in the distribution of different temperature intervals, marked essentially by an increase in the areas where the surface temperature was between 25°C and 30°C and a decrease in areas where the temperature was above 30°C. The overlapping of urban extension evolution and the surface temperature maps reveals the impact of urban extension on surface temperature spatial distribution. Finally, the Markov cellular automata model application allowed prediction in the map forms of future changes in 2027 and 2037.

Burst-pause criterion derivation for drinkometer measurements of ingestive behavior

The drinkometer is a promising device for the study of ingestive behavior of liquid meals in humans. It can be used to investigate behavior in different target populations. However, ingestive behavior has a great variability across study participants. Therefore, a new analytical approach is required for the extraction and analysis of drinkometer-derived data that could account for this variability. We developed an optimized protocol to predict an optimal burst-pause criterion (PC) for the extraction of PC-dependent microstructural parameters of ingestive behavior. These describe the microstructure of bursts, while PC-independent parameters describe the microstructure of sucks. Therefore, a PC is required to analyze separately two physiologically different parts of behavior. To accomplish this burst-pause criterion derivation (BPCD), a Gaussian Mixture Model (GMM) was built for estimation of two probability density functions (PDFs). These model the distribution of inter-suck intervals (ISIs) and inter-burst intervals (IBIs), respectively. The PC is defined at the intersection point of the two density functions. A Kaplan-Meier (KM) survival analysis was performed for post-hoc verification of the fit of the predicted optimal PC to the ISI distribution. In this protocol paper, we present a walkthrough of the data analysis of drinkometer-derived data for the measurement of microstructure of ingestive behavior based on previous results published by our group [1].•Standardization of the burst-pause criterion derivation for drinkometer measurements of ingestive behavior.•All codes are publicly available in a repository.•The method can be easily adapted to studies with larger sample size or more than one study stimulus.