Zero Values Research Articles

Calibration of the Ueno’s Shadow Rate Model of Interest Rates

Shadow rate models of interest rates are based on the assumption that the interest rates are determined by an unobservable shadow rate. This idea dates back to Fischer Black, who understood the interest rate as an option that cannot become negative. Its possible zero values are consequences of negative values of the shadow rate. In recent years, however, the negative interest rates have become a reality. To capture this behavior, shadow rate models need to be adjusted. In this paper, we study Ueno’s model, which uses the Vasicek process for the shadow rate and adjusts its negative values when constructing the short rate. We derive the probability properties of the short rate in this model and apply the maximum likelihood estimation method to obtain the parameters from the real data. The other interest rates are—after a specification of the market price of risk—solutions to a parabolic partial differential equation. We solve the equation numerically and use the long-term rates to fit the market price of risk.

Influence of rope fastening on the spectrum of its natural transverse vibrations

According to the previously developed method, which takes into account the bending stiffness of steel ropes during their transverse oscillation, the ropes of the pedestrian bridge ‘Lovers’ in the city of Tyumen were investigated. The obtained values of tension forces and bending stiffness were within the expected range. The high variation of bending stiffness values forced to pay attention to the accuracy of the initial values of rope lengths, which were obtained from the photograph. To measure the rope lengths, a method of nodal harmonic registration was proposed, but the results obtained by this method were higher than the values obtained from the photograph. The evaluation of measurement errors did not explain this overestimation, which led to the necessity to revise the solution of the differential equation of transverse oscillations. It turned out that previously only its partial solution was considered, assuming a hinged rope attachment, whereas it is cantilevered and has a bending reaction. Taking this feature into account by introducing an additional boundary condition allowed to improve the calculation model and explain the overestimation of the rope length in the method of nodal harmonics.Taking into account the nature of the rope attachment allowed us to introduce a generalised parameter s, which takes zero value for rigid cantilever attachment, and is equal to one for articulated attachment. Then the stiffness weakening inside the anchorage will be manifested by the growth of the parameter s and can be detected by the results of the oscillation spectrum registration.

Enabling Electric Field Model of Microscopically Realistic Brain.

Modeling brain stimulation at the microscopic scale may reveal new paradigms for a variety of stimulation modalities. We present the largest map of distributions of the extracellular electric field to date within a layer L2/L3 mouse primary visual cortex brain sample, which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250×140×90 μm 3 volume). We used the map to identify microscopic perturbations of the extracellular electric field and their effect on the activating thresholds of individual neurons. Previous relevant studies modeled a macroscopically homogeneous cortical volume. Result: Our immediate result is a reduction of the predicted stimulation field strength necessary for neuronal activation by a factor of approximately 0.7 (or by 30%) on average, due to microscopic perturbations of the extracellular electric field-an electric field "spatial noise" with a mean value of zero. Although this result is largely sample-specific, it aligns with experimental data indicating that existing macroscopic theories substantially overestimate the electric fields necessary for brain stimulation. Currently, there is a discrepancy between macroscopic volumetric brain modeling for brain stimulation and experimental results: experiments typically reveal lower electric intensities required for brain stimulation. This study is arguably the first attempt to model brain stimulation at the microscopic scale, enabled by automated analysis of modern scanning electron microscopy images of the brain. The immediate result is a prediction of lower electric field intensities necessary for brain stimulation, with an average reduction factor of 0.7.

The genetic architecture of exercise-induced ventricular ectopy

Abstract Background Previously, we have shown that the frequency of exercise-induced premature ventricular contractions (PVCs) during and after exercise are associated with increased risk of major adverse cardiovascular events in asymptomatic individuals from the UK Biobank study1. The underlying mechanisms are, however, unclear. Several electrocardiographic markers associated with arrhythmogenesis have a known hereditary component, but the genetic underpinnings of exercise-induced ventricular ectopy have not been investigated. Purpose Our aim was to explore the genetic basis of premature ventricular ectopy during and after exercise and unveil plausible candidate genes and biological mechanisms. Methods We conducted a genome-wide association study (GWAS) for PVC frequency during exercise and recovery in >44,000 asymptomatic individuals of European ancestry without known cardiovascular disease from the UK Biobank study. As the distribution of PVC frequency is highly skewed (e.g. absence of negative values and excess of zero values), we first applied Gaussian transformation. Simulation studies were performed to justify that this approach would lead to valid inference. In the GWAS model, we included sex, age, UK Biobank genotyping array, and the first 10 genetic principal components as covariates. Statistical power was boosted by joint analysis of exercise and recovery PVC traits using multi-trait analysis of GWAS (MTAG)2. Genetic risk scores were constructed in independent samples from UK Biobank (N=341,948) and were tested for association with heart failure and life-threatening ventricular arrhythmias. Results We identified 4 loci for PVC frequency during exercise, and 1 locus for PVC frequency during recovery (Picture 1). Candidate genes included CRIM1, FLNC, and BAG3 for which mutations have been linked in the pathogenesis of (arrhythmogenic) cardiomyopathy. Subjects in the top 10% of the genetic risk scores had a significantly higher risk of heart failure and life-threatening ventricular arrhythmias compared to the bottom 90%, (odds ratio (OR) (95% confidence interval): 1.14 (1.08 – 1.20), p < 0.001; and 1.13 (1.02 – 1.24), p < 0.001, respectively). Conclusions Ventricular ectopic burdens during exercise and recovery have a significant heritable component. Candidate genes highlight a possible shared genetic background with inherited cardiomyopathy and a genetic risk score was associated with heart failure and ventricular arrhythmias. These findings substantial advance our understanding of the genetic basis of exercise-induced ventricular ectopy in a population-based cohort without cardiovascular disease.Miami plot of ventricular ectopic burden

Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction.

Spatiotemporal dengue forecasting using machine learning (ML) can contribute to the development of prevention and control strategies for impending dengue outbreaks. However, training data for dengue incidence may be inflated with frequent zero values because of the rarity of cases, which lowers the prediction accuracy. This study aimed to understand the influence of spatiotemporal resolutions of data on the accuracy of dengue incidence prediction using ML models, to understand how the influence of spatiotemporal resolution differs between quantitative and qualitative predictions of dengue incidence, and to improve the accuracy of dengue incidence prediction with zero-inflated data. We predicted dengue incidence at six spatiotemporal resolutions and compared their prediction accuracy. Six ML algorithms were compared: generalized additive models, random forests, conditional inference forest, artificial neural networks, support vector machines and regression, and extreme gradient boosting. Data from 2009 to 2012 were used for training, and data from 2013 were used for model validation with quantitative and qualitative dengue variables. To address the inaccuracy in the quantitative prediction of dengue incidence due to zero-inflated data at fine spatiotemporal scales, we developed a hybrid approach in which the second-stage quantitative prediction is performed only when/where the first-stage qualitative model predicts the occurrence of dengue cases. At higher resolutions, the dengue incidence data were zero-inflated, which was insufficient for quantitative pattern extraction of relationships between dengue incidence and environmental variables by ML. Qualitative models, used as binary variables, eased the effect of data distribution. Our novel hybrid approach of combining qualitative and quantitative predictions demonstrated high potential for predicting zero-inflated or rare phenomena, such as dengue. Our research contributes valuable insights to the field of spatiotemporal dengue prediction and provides an alternative solution to enhance prediction accuracy in zero-inflated data where hurdle or zero-inflated models cannot be applied.

Extreme enthalpy‒entropy compensation in the dimerization of small solutes in aqueous solution.

This communication summarizes findings from the earliest encounters with extreme enthalpy‒entropy compensation, a phenomenon first detected in the 1950s by a reappraisal of isopiestic and calorimetric measurements on aqueous urea solutions in terms of solute self-association. Because concurrent studies of carboxylic acid association were confined to measurement of the equilibrium constant by conductance, IR spectrophotometry or potentiometric titration measurements, temperature-independence of the dimerization constant was mistakenly taken to signify a value of zero for Δ instead of (Δ ‒TΔ ). In those studies of small-solute self-association the extreme enthalpy‒entropy compensation was reflecting the action of water as a reactant whose hydroxyl groups were competing for the solute carbonyl involved in self-association. Such action gives rise to a positive temperature dependence of Δ that could well be operating in concert with that responsible for the commonly observed negative dependence for protein‒ligand interactions exhibiting extreme enthalpy‒entropy compensation, where the solvent contribution to the energetics reflects changes in the extent of ordered water structure in hydrophobic environments.

Adaptive Measurement and Parameter Estimation for Low-SNR PRBC-PAM Signal Based on Adjusting Zero Value and Chaotic State Ratio

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator’s behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB.

Distortion of Magnetic Fields Near a Steel Construction. Modeling

Natural variations of the Earth's magnetic field and its man-made distortions are considered. The rules and regulations in force in the Russian Federation for staying and living in places with a distortion of the Earth's magnetic field are analyzed. Existing methods for eliminating distorted magnetic fields inside residential and work premises are considered. Modeling of distortions of the Earth's magnetic field by building materials was carried out and the influence of their location in space on the overall picture of the resulting magnetic field was shown. To modeling the influence of reinforcement of reinforced concrete constructions on the resulting magnetic field, 6 models of the location of reinforcing bars and their groups were used: one reinforcing bar magnetized against the Earth’s magnetic field and along the Earth’s field; 4 reinforcing bars each, magnetized against the Earth’s magnetic field and magnetized in an alternating manner; the simplest model of a residential structure or public building with 36 reinforcing bars magnetized against the Earth’s magnetic field and with 36 magnetized bars in different directions. Near the model of a rod with magnetization along the Earth’s magnetic field, there are hypogeomagnetic fields. Near the models of a reinforcing bar and four reinforcing bars magnetized against the Earth’s magnetic field, there are no hypogeomagnetic zones and changes in the direction of the magnetic field strength vectors, but strong increases in the resulting magnetic field can be observed - 500 times or more. Near models with 4 reinforcing bars magnetized in an alternating manner, there are the most dangerous zero values of magnetic field strength at a distance of 1 m. Inside the model with 36 reinforcing bars there is a hypogeomagnetic field in the very center of the model and a change in the direction of the magnetic field strength vectors. Near the model with 36 magnetized rods in different directions, a more even resulting magnetic field is observed without sharp changes in its direction and absolute value. Methods have been proposed for eliminating magnetic pathogenic zones at the stages of installation and construction of building that have ferromagnetic materials in their design.

A-220 Method Intercomparison Study between Atellica Solution® and CI® Analyzers for HDL and LDL Methods

Abstract Background After validating a new Siemens equipment, the Atellica CI®, it is essential to ensure result interchangeability in our laboratory accredited by ISO 15189:2012. Objectives verify if the results of the HDL and LDL methods measured by Atellica Solution® and CI® analyzers are interchangeable and could be used interchangeably on both instruments Methods 40 patient samples were selected for each assay, with results distributed across the entire measurement range and analyzed by both instruments. Result evaluation was conducted using the statistical program Method Validator v.19. To determine the degree of agreement between the instruments, a Bland-Altman mean difference analysis and a Passing-Bablok regression were performed. Statistics are expressed with their 95% confidence intervals (CI). Results HDL: After analyzing the Bland-Altman differences, a systematic error was found as the mean difference between the results obtained by Atellica Solution® and CI® was 0.686, with higher results in CI 1900®. Regarding the Passing Bablok analysis, constant systematic differences were observed as the confidence interval of the intercept does not include the zero value, and statistically significant proportional differences based on the slope whose 95% CI does not include the value 1.LDL: After analyzing the Bland-Altman differences, a systematic error was found as the mean difference between the results obtained by Atellica and CI® was -3.16, with higher results in Atellica Solution®. Regarding the Passing Bablok analysis, there were no systematic differences of constant type as the confidence interval of the intercept includes the zero value, but there were proportional differences based on the slope whose 95% CI does not include the value 1. Conclusions After evaluating the results, it is concluded that Atellica Solution® and CI® analyzers are interchangeable for HDL and LDL assays despite the presence of a systematic error, as the observed difference is not clinically significant.

B-011 Intermethod Comparison Verification between Atellica Solution® and CI® Analyzers for Amylase and Glucose Methods

Abstract Background The objective of this study is to verify if the results of the Amylase and Glucose methods, measured by two different analyzers: Atellica Solution® and CI® (Siemens Healthineers), are interchangeable. Methods To carry out the intermethod comparison study of the Amylase and Glucose methods, 40 patient samples were selected for each assay, with concentrations across the entire measurement range. Statistical analysis was performed using Method Validator v.19 software, through Bland-Altman mean difference analysis and Passing-Bablok regression. Statistics are expressed with their 95% confidence intervals (CI). Results AMYLASE: After analyzing the Bland-Altman differences, a statistically significant systematic error was found, as the mean difference between the results obtained by Atellica Solution® and CI® was 28.1 with a 95% CI that does not include the null value, with higher results in CI®. Regarding the Passing Bablok analysis, systematic differences, both proportional and constant, were observed as the confidence interval of the intercept does not include the zero value, and that of the slope does not include the value 1.GLUCOSE: After Bland-Altman analysis, a systematic error was found, as the mean difference between the results obtained by Atellica Solution® and CI® was 2.1, with slightly higher results in CI®. Regarding the Passing Bablok analysis, there were no systematic differences of constant type since the confidence interval of the intercept includes the zero value, and a slight proportional systematic error based on the slope as its 95% CI does not include the value 1. Conclusions After evaluating the results, it is concluded that the Atellica Solution® and CI® analyzers are not interchangeable for the Amylase assay, so new reference values should be established. For the Glucose assay, both analyzers are interchangeable as the observed difference does not exceed our quality specification.

A-219 Method Intercomparison Study between Atellica Solution® and CI® Analyzers for Total Cholesterol and Triglycerides

Abstract Background Method intercomparison is an essential requirement that every Laboratory must perform when considering a method or instrument change for a specific analyte. Objective verify if the results of two analytical techniques (Total Cholesterol and Triglycerides) measured by Atellica Solution® and CI® analyzers (Siemens Healthineers) are interchangeable. Methods 40 patient samples were selected for each assay, with results distributed across the entire measurement range and analyzed by both instruments. Estatistical program Method Validator was used. To determine the degree of agreement between both instruments, Bland-Altman difference analysis and Passing-Bablok regression were performed. Statistics are expressed with their 95% confidence intervals. Results Total Cholesterol: After analyzing Bland-Altman differences, a systematic error was found as the mean difference between the results obtained by Atellica Solution® and CI 1900® was 13.8 with a 95% CI that does not include the null value. Regarding Passing Bablok analysis, a constant systematic difference was observed as the confidence interval of the intercept does not include the zero value, and also a proportional difference based on the 95% CI of the slope that does not include the value 1.• Triglycerides: After analyzing the Bland-Altman differences, a systematic error was found as the mean difference between the results obtained by Atellica Solution® and CI 1900® was -15.6, with higher results in Atellica Solution®. Regarding the Passing Bablok analysis, constant systematic differences were observed as the confidence interval of the intercept does not include the zero value, and also proportional differences based on the slope, whose 95% CI does not include the value 1 Conclusions It´s concluded that Atellica Solution® and CI® analyzers are not interchangeable for Total Cholesterol and Triglycerides assays due to the presence of a constant systematic error. To use them on both instruments, it would be advisable to modify the reference values for both methods.

Global COVID-19 under-reporting: A Tobit model

During the COVID-19 pandemic, the precision in reporting infectious cases and fatalities presents significant challenges, exacerbated by rapid transmission rates and overburdened healthcare infrastructures. Officially reported cases occasionally exhibit zero increments, which is likely to be under-reported. Some models exclude zero values from the sample, creating a sample selectivity problem. In contrast, alternative models substitute zero values with a constant to enable logarithmic transformations. Since both modelling approaches are wrong, in this study, we address this issue by extending the Tobit model to account for both under-reporting and random noise. The standard Tobit model accommodates zero values but not under-reporting. Analyzing data from 61 countries between January 1, 2020, and November 3, 2020, we explore external factors that explain country-specific under-reporting. Our findings confirm the existence of under-reporting across countries and reveal that cases reported with zero increments actually involve non-zero infectious instances. This novel methodology enriches future under-reporting analyses.

Electronic Properties of Monolayer Graphene with Doping of Nitrogen Atom: A Density Functional Theory Study

Abstract As a material that has been widely researched, graphene is certainly interesting because it has many unique features. In its development, graphene is very promising as an optoelectronic material. However, in theory, graphene has obstacles in its development because its band gap position is zero. With various modifications in opening the band gap so that it does not have a value of zero. In the current research, structural modifications were carried out on graphene layers by doping with a number of Nitrogen atoms. The modeling was carried out using graphene with a supercell size of 4 x 4 x 1. Our calculations are based on Density Functional Theory using the PBE – GGA function for potential exchange correlation. The result is the Fermi level shifting and emergence of magnetic moment through doping with Nitrogen atoms. The results obtained certainly open up opportunities for the development of graphene.

Mathematical model for the exergy of the system ammonium nitrate–water

The exergy balance has recently been used more frequently for the energy analysis of technological processes and represents a considerable improvement over the evaluation according to the First law of thermodynamics, in which completely convertible and incompletely convertible forms of energy are taken into account equally. In order to carry out the exergy balance, the exergy values of the material flows involved in the process must be known. This article presents a mathematical model for calculating the exergy of ammonium nitrate in aqueous solution in the production of fertilizers. When carrying out the exergy model, it was assumed that the conditions are: ambient temperature 20 °C, pressure 101.325 kPa and that it consists of liquid water and crystalline ammonium nitrate, which has an exergy value of zero under these conditions. The model is based on Pitzer’s ion-interaction type equations for the dependence of the activity coefficient on the composition of the solution at a given temperature and pressure. This mathematical model is valid for aqueous solutions of ammonium nitrate up to saturation in the temperature range from 20 °C to 170 °C and offers the possibility to estimate the NH4NO3(aq) enthalpies at different temperatures and different compositions.

Modelling the heterogeneity of tourist spending in a mature destination: An approach through infinite mixture

Identifying tourists' preferences is essential for stakeholders to provide better products and services. Among the tools to classify such choices, expenditure segmentation is valuable for separating tourist groups with shared interests. The underlying idea of the (infinite) mixture model is that tourists spend on a specific activity depending on their preferences. However, the propensity to consume may be based on or influenced by the group to which the tourist belongs. Thus, such a tendency could increase depending on homogeneity or heterogeneity. This paper uses a compound distribution mixture to model the expenditure heterogeneity. The resulting mixture model derives from a multivariate Pareto (Lomax) distribution that is easy to implement and includes zero value in its support since it is empirically proven that a tourist's expenditure on some activity can be zero. Results show that once the spending on transport has been carried out, tourists prefer to spend more on food than other activities. Conditioned to the expense carried out on food, the mean expenditure on leisure activities is more significant than on transport. Finally, tourists would prefer to spend more on food than on transportation once they decide to spend on other activities.

Cluster Analysis and Forecasting on Local Shoe Products: Case study for Ventela in Indonesia

Shoes are a secondary need that is in demand by all age groups. Each shoe brand has many models, which means a store must provide complete stock to meet consumer needs. The available models have different purchasing power or demand, which creates difficulties for stores in determining shoe products that are often sold and shoe products that are not in demand by customers. The data used in cluster formation includes three variables recorded and collected from e-commerce transactions, namely Number of Visitors, Number of Buyers, and Total Sales. Based on these variables, Ventela shoe models are grouped into three clusters, namely low-selling, normal and best-selling. Next, the variable number of transactions for Ventela shoe models in the best-selling cluster is taken to be predicted using the Exponential Smoothing method. The forecasts obtained are used to determine future demand to maximize profits. Based on the results of the clustering analysis, it was found that the number of shoe models included in the best-selling cluster was six, including (1) Ventela Ethnic Low All Black, (2) Ventela Ethnic Low Black Natural, (3) Ventela Public Low Black Natural, (4) Ventela Public Low Cream, (5) Ventela Republic Low Black Natural, and (6) Ventela Republic Low White. Referring to the sample of this study, which only spanned less than three years, several shoe models produced a forecast value of zero. It means, based on the forecast results for the next 12 months, there may be no sales.

Estimation of residual fatigue life of high-strength steel welded joints based on diffuse ultrasound

Fatigue significantly affects the durability and capacity of high-strength steel welded joints (WJs) in engineering. Despite the capability of ultrasonic-based methods to issue early warnings for fatigue crack initiation and effectively monitor crack expansion, estimating residual life before surface cracks poses a considerable challenge. This study introduces a novel method that relies on the correlation coefficient of the ultrasonic energy density, α, to estimate the residual life of WJs before surface cracks become evident. The fatigue damage of 20 butt- and cross-WJs of Q690qENH and Q960E, subjected to different fatigue stresses, was monitored using α. The results revealed a cubic polynomial relationship between α and the number of fatigue cycles when a sample experienced early fatigue damage. Consequently, we introduced an α-N early fatigue damage monitoring curve. The zero value of its second derivative functioned as an early warning indicator, and the corresponding life of the sample was recorded as ζ. Finally, a warning of 20%–50% of the fatigue life for WJs made of two high-strength steels was attainable. A linear relationship between ζ and fatigue stress, denoted as the ζ-S curve, facilitated residual life estimation before surface cracks developed. The blind test produced an approximately ±10% error with 4 samples.

The Relative Importance of the Contrast and Assimilation Effects in Decisions Under Risk

ABSTRACTPast research on decisions under risk has documented two contradictory context effects: the contrast effect, where risk preferences in “target” tasks diverge from those in previous “surrounding” tasks, and the assimilation effect that implies the opposite bias. We present four web experiments (three preregistered) that clarify the conditions determining the relative prominence of these opposing effects. Our experiments focus on choice patterns in “target” tasks where participants choose between the status quo and a risky mixed gamble with an expected value of zero. Study 1 examines the impact of surroundings that differ from the target task with respect to the expected benefit from risk‐taking. The findings reveal a strong contrast effect: Decreasing the attractiveness of risk‐taking in the surrounding tasks increased the risk‐taking rate in the target tasks from 53.2% to 79.7%. Study 2 investigates the impact of surroundings that differ in the payoff domain. The findings indicate a strong assimilation effect: Decreasing the attractiveness of risk‐taking in the surrounding tasks decreased the risk‐taking rate in the target tasks from 74.7% to 36.5%. Additionally, the results revealed unpredicted and robust reversed loss aversion patterns which Studies 3 and 4 further clarify. Our findings (1) suggest that the isolated within‐task computations assumed by leading descriptive models overlook substantial contextual considerations, (2) clarify the factors determining the impact of the contrast and assimilation effects in decisions under risk, and (3) provide a theoretical framework for making useful predictions in various scenarios.

ScIDPMs: Single-Cell RNA-Seq Imputation Using Diffusion Probabilistic Models.

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling the sequencing of mRNA in individual cells, thereby providing valuable insights into cellular gene expression and functions. However, scRNA-seq data often contain false zero values known as dropout events, which can obscure true gene expression levels and compromise downstream analysis accuracy. To address this issue, several computational approaches have been proposed for imputing missing gene expression values. Nevertheless, these methods struggle to capture dropout value distributions due to the sparsity of scRNA-seq data and complex gene expression patterns. In this study, we present a novel method called scIDPMs that utilizes conditional diffusion probabilistic models to impute scRNA-seq data. Firstly, scIDPMs identifies dropout sites based on gene expression characteristics and subsequently infers the missing values by considering available gene expression information. To effectively capture global gene expression features, scIDPMs employs a deep neural network with an attention mechanism to optimize the imputation process. We evaluated the performance of scIDPMs using simulated and real scRNA-seq datasets and compared it with ten other imputation methods. The results indicate that scIDPMs outperform other methods in restoring biologically meaningful gene expression values and improving downstream analysis.

Hybrid Model for Multistep-Ahead Rainfall Forecast in Northeast India: A Comparative Study

Abstract Among all hydrometeorological parameters, rainfall strongly correlates with hydrometeorological disasters. The rainfall forecast process remains challenging due to the nonlinear, nonstationary nature and multiscale variability of rainfall. Moreover, the unique microclimate in different regions further complicates the forecasting process. This study proposes a hybrid model employing multivariate singular spectrum analysis (MSSA) and long short-term memory (LSTM) for multistep-ahead hourly rainfall forecasting in urban areas of northeast India. The model is trained and evaluated using high-resolution (12 km) hourly meteorological data from the Indian Monsoon Data Assimilation and Analysis (IMDAA) dataset for Guwahati (plain) and Aizawl (hilly) regions from 2015 to 2019. The hybrid model outperforms the single LSTM model in both plain and hilly regions, with an average percentage gain of 47.99% and 43.88% for symmetric mean absolute percentage error (SMAPE) and root-mean-square error (RMSE) in the case of the Guwahati dataset and 84.59% and 82.27% in the case of the Aizawl dataset, respectively. The performance of the LSTM model significantly improves as the zero values in the observed data are eliminated after reconstruction by MSSA. This enables the model to discern essential patterns and relationships in the data, which leads to more accurate forecasts. However, the hybrid model underestimates the rainfall, which can be tackled by hypertuning the parameters. The study highlights the importance of considering the interplay between rainfall and meteorological parameters for accurate rainfall forecasting in urban areas. The proposed MSSA–LSTM model can be used as a decision support tool for urban planning and disaster management. Significance Statement This study addresses a critical need in multistep-ahead hourly rainfall forecasting in urban areas, with a focus on the unique conditions of northeast India. Our research has identified the presence of red noise in the rainfall data, shedding light on the complexities of the underlying rainfall patterns. Furthermore, we delve into the intricate interplay between rainfall and meteorological parameters, providing valuable insights into the factors influencing rainfall dynamics. Notably, our study underscores the region-specific challenges in rainfall forecasting. While the hybrid model demonstrates reasonable accuracy in the plains, its performance in the hilly region falls short of expectations. This highlights the nuanced nature of rainfall prediction in areas with varying topography and emphasizes the need for tailored forecasting approaches.