Rate Of Change Research Articles

A Noncontact Method for Simultaneous Measurement of di/dt and dv/dt

This letter proposes a non-contact method for simultaneously measuring the current and voltage change rates by Rogowski coils. The dv/dt measurement capability is achieved based on electric field coupling, which is incorporated into the state-of-the-art Rogowski coil based current sensing solutions. The application of the proposed method for power semiconductor devices is demonstrated by a case study of an IGBT switching circuit. The Rogowski coils are integrated with a Printed Circuit Board (PCB) and fit with the IGBT module terminals. The comparable results from high-performance commercial instruments verify the di/dt and dv/dt measurement accuracy of the presented compact and cost-effective solution.

Incidence trends of kidney cancer in China from 1990 to 2019: A joinpoint and age-period-cohort analysis.

Occupational carcinogens, smoking, and obesity are believed to be the main causing agents of kidney cancer. China is undergoing rapid industrialization, and hence the people's lifestyles have witnessed tremendous changes. However, the trend of kidney cancer incidence during the late 20th and early 21st centuries remains unexplored in China. Data from the Global Burden of Diseases (GBD; 2019) was retrieved for the incidence of kidney cancer from 1990 to 2019. The rates of disease average annual percentage changes (AAPC) were assessed using joinpoint regression analysis. Age-period-cohort (APC) model was used to assess age, period, and cohort effects on the incidence of the disease simultaneously. An increase in age-standardized incidence rates (ASIR) of kidney cancer was observed from 1990 to 2019 in total residents (1.33 - 4.24), men (1.56 - 6.15), and women (1.11 - 2.31) per 100,000 population suggesting a more obvious increase in males than in females. A consistent increase in age effects was observed in all age groups, especially in males. On the other hand, the 70 - 74 age group in females showed greater age effects. In addition, the period effects analysis showed that the incidence of kidney cancer increased with time. Moreover, the analysis of cohort effects showed a decrease in the disease in birth cohorts, especially before 1940. The incidence of kidney cancer is increasing rapidly in China. The kidney cancer burden will rise in the next decades due to population aging, environmental pollution, occupation, food safety, and so on. Results of this study suggest that more etiological studies should be performed to identify the driving factors for kidney cancer trends, and appropriate preventive measures should be implemented for the age-, period-, and cohort-related factors in the population.

Drought Responses in Three Apple Cultivars Using an Autonomous Sensor-based Irrigation System

Irrigation decision support systems evolving in the domestic temperate tree fruit production industry incorporate measures of soil moisture status, which diverges from classic physiological indicators of edaphic stress. This study used an autonomous sensor-based irrigation system to impose a water deficit (soil matric potential targets of –25, –40, –60, and –80 kPa) on ‘Autumn Gala’, ‘CrimsonCrisp’, and ‘Golden Delicious’ apple (Malus domestica) cultivars grafted to ‘Budagovsky 9’ rootstock in the greenhouse (n = 60). It was hypothesized that relationships between physiological plant function, assessed via infrared gas exchange and chlorophyll fluorescence, and the soil matric potential may be used to advance emerging irrigation decision support systems. Complications arising from defoliation by day 11 at –60 and –80 kPa indicate the generation of substrate-specific soil–water relationships in research applications of autonomous sensor-based irrigation systems. ‘Autumn Gala’ carbon assimilation rates at –80 kPa declined from day 0 to day 8 (9.93 and 5.86 μmol⋅m–2⋅s–1 carbon dioxide), whereas the transpiration rate was maintained, potentially reducing observed defoliation as other cultivars increased transpiration to maintain carbon assimilation. Correlation matrices revealed Pearson’s r ≤ |0.43| for all physiological metrics considered with soil matric potential. Nevertheless, exploratory regression analysis on predawn leaf water potential, carbon assimilation, transpiration, stomatal conductance, and nonphotochemical quenching exposed speculatively useful data and data shapes that warrant additional study. Nonlinear piecewise regression suggested soil matric potential may useful as a predictor for the rate of change in predawn leaf water potential upon exposure to a water deficit. The critical point bridging the linear spans, –30.6 kPa, could be useful for incorporating in emerging irrigation decision support systems.

An ANN-Based Output-Error-Driven Incremental Model Predictive Control for Buck Converter Against Parameter Variations

DC/DC buck converters have been widely applied in the power processing of distributed energy resources (DERs) penetrated DC microgrid. Model predictive control (MPC) provides optimal control for the buck converter, but it often requires multiple sensors or complex observers to realize zero-steady-state-error control against parameter variations. An output-error-driven incremental MPC (OEDIMPC) for buck converter is proposed in this paper. Different from the traditional concept of calculating the optimal duty cycle, the proposed MPC calculates the optimal rate of change of the duty cycle by using a reduced-state output-error-driven prediction model. As a result, the OEDIMPC achieves zero-steady-state-error control against parameter variations, including load value, input voltage, output inductor, and output capacitor, with significantly improved dynamic performance and minimal sensor and observer requirements Besides, small- and large-signal analyzes are used to study the stability boundary of the OEDIMPC under parameter variations, and a shallow layer artificial neural network (ANN) is utilized to realize the OEDIMPC on the hardware. The proposed technique has been evaluated experimentally on a buck converter with resistor load and constant power load (CPL), confirming its effectiveness.

Network Pharmacology and Intestinal Microbiota Analysis Revealing the Mechanism of Punicalagin Improving Bacterial Enteritis.

The Chinese medicine punicalagin (Pun), the most important active ingredient in pomegranate peel, has significant bacteriostatic and anti-inflammatory properties. The potential mechanisms of Pun for bacterial enteritis, however, are unknown. The goal of our research is to investigate the mechanism of Pun in the treatment of bacterial enteritis using computer-aided drug technology, as well as to investigate the intervention effect of Pun on mice with bacterial enteritis using intestinal flora sequencing. The targets of Pun and Bacterial enteritis were obtained by using the specific database, and cross-targets were screened among these targets, followed by PPI and enrichment analysis of the targets. Furthermore, the degree of binding between Pun and key targets was predicted through molecular docking. After successfully establishing the bacterial enteritis model in vivo, mice were randomly assigned to groups. They were treated for 7 days, the symptoms were observed daily, and the daily DAI and body weight change rate were calculated. Following administration, the intestinal tissue was removed, and the contents were separated. The tight junction protein expression was detected in the small intestine by the immunohistochemical method; ELISA and Western Blot (WB) were performed to detect the expressions of tumor necrosis factor-α (TNF-α) and interleukin- 6 (IL-6) in the serum and intestinal wall of mice. The 16S rRNA sequence was used to determine the composition and diversity of the intestinal flora of mice. In total, 130 intersection targets of Pun and disease were screened by network pharmacology. The enrichment analysis showed cross genes were closely related and enriched in the cancer regulation and the TNF signal pathway. The active components of Pun could specifically bind to the core targets TNF, IL-6, etc., determined from molecular docking results. In vivo experiment results showed that the symptoms in the PUN group mice were alleviated, and the expression levels of TNF-α and IL-6 were significantly reduced. A Pun can cause substantial changes in the intestinal flora of mice in terms of structure and function. Pun plays a multi-target role in alleviating bacterial enteritis by regulating intestinal flora.

Modelling hydrologic processes of a karst region in Southern China using coupled hydrological models

ABSTRACT In the karst area, the epikarst increases the infiltration of rainfall and the depressions and conduits make water exchange frequently between surface rivers and karst aquifers. To improve simulation accuracy, we proposed the Soil and Water Assessment Tool-Genetic Algorithm-Back Propagation (SWAT-GA-BP) model. Also, the SWAT-tank model was created to elaborate the runoff processes in different karst systems. Furthermore, we designed three scenarios to study the influence of land use changes on runoff. The Zuo River Basin was selected as a case study. Compared to SWAT, the determination coefficient of the SWAT-GA-BP increased by 4.8% and the Nash-Sutcliffe efficiency increased by 4.9%; the rapid flow accounted for more than 50% of the total runoff, and the slow flow for less than 10%; the contribution of the land use changes was 10.80% from 2008 to 2012 and 5.57% from 2013 to 2017. Therefore, the effect of land use change on runoff should not be ignored.

Blood Kinetics of Lipophilic and Proteinophilic Pollutants during Two Types of Long-Term Fast in King Penguins.

In vertebrates, fasting is an intricate physiological process associated with strong metabolic changes, yet its effect on pollutant residue variation is poorly understood. Here, we quantified long-term changes in plasma concentrations of 20 organochlorine and 16 perfluoroalkyl pollutants in king penguins Aptenodytes patagonicus during the breeding and molting fasts, which are marked by low and high levels of protein catabolism, respectively, and by strong lipid use. The profile of measured pollutants in plasma was dominated by perfluorooctanesulfonic acid (PFOS, initial relative contribution of 60%). Initial total pollutant concentrations were similar in molting (3.3-5.7 ng g-1 ww) and breeding penguins (range of 4.2-7.3 ng g-1 wet weight, ww). Long-term fasting (25 days) for molting and breeding led, respectively, to a 1.8- and 2.2-fold increase in total plasma pollutant concentrations, although the rate and direction of change were compound-specific. Hexachlorbenzene (HCB) and PFOS concentrations increased in plasma (net mobilization) during both types of fasting, likely due to lipid use. Plasma perfluoroundecanoate (PFUnDA) and perfluorotridecanoate (PFTrDA) concentrations increased in breeders (net mobilization) but decreased in molting individuals (net excretion), suggesting a significant incorporation of these pollutants into feathers. This study is a key contribution to our understanding of pollutant variation in blood during long-term fasting in wildlife.

Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients.

To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS). Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated. Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms' predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms. CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.

Forecasting some climate parameters of Türkiye using the SSP3-7.0 scenario for the years 2040–2059

This study employs the Coupled Model Inter-comparison Projects (CMIPs) and the Sixth phase of CMIPs (CMIP6) to unravel the multifaceted impacts of global climate change on climate of Türkiye. The CMIP6 data, fundamental to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports, forms the basis for projecting future climate scenarios, specifically under the medium-high reference scenario SSP3-7. Utilizing a suite of global climate models, including the innovative Multi-Model Ensemble (MME) approach, this study combines predictions to enhance the precision climate projections of Türkiye. Historical data spanning from 1951 to 2020 were subjected to rigorous statistical analysis, including descriptive statistics and regression analysis. The findings reveal an unequivocal upward trajectory in Türkiye’s annual mean temperature, with an accelerated pace in recent decades. Despite a lack of a significant long-term trend in annual precipitation from 1951 to 2020, the rate of change in precipitation is accelerating, indicating potential future challenges. Projections for 2040-2059 under the SSP3-7.0 scenario indicate a non-uniform increase in mean temperature across Türkiye, with the southern and western regions facing the most significant impact. This warming trend poses imminent threats to agriculture, altering crop yields and increasing the risk of heat stress for livestock. Additionally, the projected decrease in precipitation, alongside a surge in hot days and tropical nights, underscores the urgency for adaptive measures. As Türkiye navigates the complex terrain of climate change, this study provides valuable insights, emphasizing the significance of robust climate modeling for informed decision-making. The results underscore the imminent challenges Türkiye faces and emphasize the critical importance of proactive climate action on both national and global fronts.

Mathematical model for characterization of temperature-responsive polymers: A study on the rheological behavior of gelatin and poly(N-isopropylacrylamide)

This study introduces a mathematical model for detailed characterization of temperature-responsive polymers, focusing on critical temperature, viscosity, storage modulus, their rate of change in the transition region, flow behavior, and potential hysteresis. Applied to gelatin (GEL) and poly(N-isopropylacrylamide) (pNIPAm), the model analyzes their rheological responses across various temperatures. It was observed that gel's critical temperature is slightly above its polymer's lower critical solution temperature, and vice versa for upper critical solution temperature polymers. The critical temperature for GEL was 28 °C, as opposed to 37 °C for pNIPAm. While concentration changes affected these values, the contribution of tetramethylethylenediamine (typically used in combination with various initiators to start the polymerization reaction) was found to be insignificant. GEL exhibited significant hysteresis, unlike pNIPAm's rapid transition. Their storage modulus and viscosity showed a linear correlation with concentration, with deviations at high pNIPAm concentrations suggesting the influence of polymer interactions and crosslinking. These findings offer valuable insights for applications requiring temperature sensitivity.

A novel approach to model cumulative stress: Area under the s-factor curve

ObjectiveUsing a large longitudinal sample of adults from the Midlife in the United States (MIDUS) study, the present study extended a recently developed hierarchical model to determine how best to model the accumulation of stressors, and to determine whether the rate of change in stressors or traditional composite scores of stressors are stronger predictors of health outcomes. MethodWe used factor analysis to estimate a stress-factor score and then, to operationalize the accumulation of stressors we examined five approaches to aggregating information about repeated exposures to multiple stressors. The predictive validity of these approaches was then assessed in relation to different health outcomes. ResultsThe prediction of chronic conditions, body mass index, difficulty with activities of daily living, executive function, and episodic memory later in life was strongest when the accumulation of stressors was modeled using total area under the curve (AUC) of estimated factor scores, compared to composite scores that have traditionally been used in studies of cumulative stress, as well as linear rates of change. ConclusionsLike endogenous, biological markers of stress reactivity, AUC for individual trajectories of self-reported stressors shows promise as a data reduction technique to model the accumulation of stressors in longitudinal studies. Overall, our results indicate that considering different quantitative models is critical to understanding the sequelae and predictive power of psychosocial stressors from midlife to late adulthood.

A quantitative genetic model of background selection in humans.

Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This "linked selection signal" reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference better predicts observed divergence in conserved regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans.

Telehealth during COVID-19 restrictions in patients with cardiovascular disease: impact on medication prescriptions and patient satisfaction.

Telehealth plays an integral part in healthcare delivery. The impact of telehealth and the COVID-19 pandemic on medication prescribing and patient satisfaction with telehealth in cardiology clinics remains unknown. A retrospective study of cardiology clinic patients at an Australian tertiary hospital was conducted; 630 patients seen before the COVID-19 pandemic (0.6% telehealth) and 678 during the pandemic (91.2% telehealth) were included. Medication changes, new prescriptions and time to obtaining prescriptions after clinic were compared. To evaluate patients' experiences, cardiology clinic patients reviewed during the pandemic were prospectively invited to participate in an electronic survey sent to their mobile phones. The overall rates of medication changes made in the clinic between the prepandemic and the pandemic periods did not differ significantly (26.9% vs 25.8%). Compared with prepandemic, new cardiac medication prescriptions during clinic were significantly less (9.3% vs 2.5%; P < 0.0001) and recommendations to general practitioners (GP) to initiate cardiac medications were significantly more (2.6% vs 9.1%; P < 0.0001). Time to obtaining new prescriptions was significantly longer in the pandemic cohort (median 0 days (range: 0-32) vs 10.5 days (range: 0-231); P < 0.0001). Two hundred forty-three (32.7%) patients participated in the survey; 50% reported that telehealth was at least as good as face-to-face consultations. Most patients (61.5%) were satisfied with telehealth and most (62.9%) wished to see telehealth continued postpandemic. Telehealth during the COVID-19 pandemic was associated with greater reliance on GPto prescribe cardiac medications and delays in obtaining prescriptions among cardiology clinic patients. Although most patients were satisfied with telehealth services, nearly half of the cardiac patients expressed preference towards traditional face-to-face consultations.

The Distribution and Evolution of Groundwater Level Depths and Groundwater Sustainability in the Hexi Corridor over the Last Five Years

Groundwater overexploitation for agricultural irrigation is prone to lead to numerous ecological concerns. This study delved into the present distribution and recent trend of groundwater levels in the plain areas of the Hexi Corridor in Northwest China according to the groundwater level depth (GWD) data from 264 monitoring wells in the Shiyang River Basin (SYB) and 107 in the Shule River Basin (SLB), recorded annually in April from 2019 to 2023. The key findings include the following: (1) Over the five-year span, the SYB’s GWD experienced change rates (CRs) ranging from −12.17 to 9.11 m/a (average: −0.13 m/a), with the number of monitoring wells showing increased and decreased GWDs accounting for 50% and 50%, respectively. By contrast, the SLB’s GWD exhibited CRs ranging from −1.87 to 2.06 m/a (average: 0.01 m/a), with the number of monitoring wells showing increased and decreased GWDs accounting for 52% and 48%, respectively; (2) the Wuwei (CR = 0.09 m/a) and Changning (0.58 m/a) basins in the SYB and the Yumen (0.06 m/a), Guazhou (0.05 m/a), and Huahai (0.03 m/a) basins in the SLB, witnessed rising groundwater levels. In contrast, the Minqin Basin (0.09 m/a) in the SYB and the southern Dunhuang Basin (0.04 m/a) in the SLB witnessed declines in the groundwater levels; (3) The groundwater sustainability assessment showed that the groundwater is still extremely unsustainable. This study’s insights are instrumental in targeted treatment, as well as the preparation and adjustment of sustainable groundwater protection strategies.

Research on vacuum glass insulation performance prediction based on unsteady state multivariate data screening and multi-model fusion self-optimization

A self-optimization regression method based on multivariate data screening and multi-model fusion is proposed for the regression prediction of vacuum glass insulation performance. Firstly, we analyzed the positive correlation between the temperature change rate and heat transfer coefficient. Secondly, we combined the technique of multi-distribution overall diffusion to generate virtual sample data with multiple variables corresponding to the small neighborhood of the target variable, addressing the issue of insufficient sample size. We conducted quantity and threshold screening on the generated data to enhance the effectiveness of the virtual sample data. The screened data and original training set data are integrated to form new training samples. Then, the limited-memory Broyden Fletcher Goldfarb Shanno with boundary constraints (L-BFGS-B) algorithm is introduced to optimize the parameters of multiple models to improve the accuracy of model regression prediction. Finally, through the self-searching optimization framework structure, the virtual sample data screening and optimization of various model parameters are updated. Eventually, the optimal model for predicting the heat transfer coefficient of vacuum glass insulation performance is determined from multiple models. In order to validate the effectiveness of self-optimization regression method, we conducted insulation performance heat transfer coefficient predictions under 30 different ways for vacuum glass. Experimental results demonstrate that the self-optimization regression method based on multivariate data screening and multi-model fusion can obtain the most effective regression prediction model, achieving accurate, rapid and intelligent prediction of vacuum glass insulation performance.

Change in cardiovascular health and rate of cognitive decline in older adults: a 15-year population-based study

BackgroundPrevious research on associations between cardiovascular health, measured at a single timepoint, and rate of age-related cognitive decline shows divergent findings dependent on the participants’ age and the health metric studied. The aim of this study was to add to the knowledge in this field by investigating whether change in cardiovascular health, assessed with Life’s Simple 7 (LS7) score, is associated with rate of cognitive change in young-old and old-old adults.MethodsThe study included 1022 participants aged ≥ 60 years from the Swedish National Study on Aging and Care-Kungsholmen (SNAC-K), who underwent repeated neuropsychological testing (episodic memory, semantic memory, verbal fluency, and perceptual speed) across up to 15 years. LS7, composed of seven cardiovascular health metrics (smoking, diet, physical activity, body mass index, plasma glucose, total serum cholesterol, and blood pressure), was assessed at baseline and at the 6-year follow-up. Change in LS7 was calculated as the difference between baseline and 6 years (range − 5 to 8 points) and categorised into worse (−5 to −2 points), stable (−1 to 1 points), and improved (2 to 8 points). Change in cognitive performance as a function of LS7 change categories was estimated using linear mixed-effects models.ResultsParticipants were classified as stable (67.1%), improved (21.0%), or worse (11.8%) according to changes in LS7 score. Both the worse and improved categories were associated with faster cognitive decline. Age-stratified analyses revealed that worsening of LS7 was clearly associated with faster cognitive decline in the old-old (≥ 78 years), whereas improvement tended be associated with faster cognitive decline in the young-old (< 78 years) group.ConclusionsChange in cardiovascular health in old age may lead to accelerated cognitive decline, particularly in late senescence. These results suggest that it is important to monitor and maintain cardiovascular health status in very old adults.

The Importance of Fetal Station in the First Stage of Labor.

This study aimed to examine the relationship of fetal station in the first stage of labor to labor curves and cesarean delivery rates among women presenting in spontaneous labor. Labor curves for patients with nonanomalous singletons who presented in spontaneous labor to our hospital's Obstetric Triage Unit with intact membranes from January 1, 2012, to August 31, 2016 were reviewed. Cervical exams and time of exam were obtained for each patient from presentation to triage until delivery. Station for each presentation and cervical dilation was estimated using a random effects model and the slope of cervical station change was calculated to estimate the change in dilation by hour. Perinatal outcomes, including cesarean delivery rates, were examined according to fetal station at initial presentation. Factors known to affect labor curves-epidural analgesia, infant birthweight, maternal habitus, and parity-were also examined. There were 8,123 patients presented in spontaneous labor with intact membranes. For patients presenting at 6-cm dilation, the rate of change of labor was significantly different when identified to have a station greater than 0 (+1 and more caudad) when compared with those with -1 and more cephalad station (both p < 0.001). This relationship persisted when analyzed according to epidural analgesia, birthweight, maternal habitus, and parity. The frequency of cesarean delivery was significantly higher for women presenting in spontaneous labor with negative fetal station (p < 0.05). When stratified across all dilation (3-9 cm), this trend remained significant (p < 0.001). In the first stage of labor, advanced fetal station was significantly associated with differing rates of labor progression, and positive fetal station was significantly less likely to result in cesarean delivery. Physical examination, including station, remains a critical element in labor management. · Fetal station is important in labor management.. · Fetal station at initial exam is related to time to delivery.. · Positive fetal station at initial exam is less likely to result in cesarean delivery..

Methodology for detecting and removing outliers in statistical studies

The paper presents a calculation method for detecting and eliminating outlying values. It is shown that its effectiveness depends on the amount of a priori information on the examined process. The proposed method is used for cases whereas the process is stationary and has a Gaussian probability density law. When analysing non-stationary random processes, the existing methods and algorithms rely on the fact that the outlying component is additive and the characteristics of the outlying values are known a priori. The work used the statistical decisions theory that allows formalising the verification algorithms and selecting a criterion for detecting outlying values. Both parametric and non-parametric methods were proposed. In the first case, it is required to have a priori information both on the function of the useful component and on the distribution law of the outlying component of the process, as well as its parameters. It is postulated that the use of non-parametric processing methods requires significantly less a priori information, but their effectiveness is defined by the processing parameters that, in turn, depend on the function of the useful and the distribution law of the outlying components of the process. It is noted that an outlier may prove to be one of the extreme values of the probability distribution of a random variable. The authors outline the problems of ambiguity of input data in case of classical computing. The paper examines the way the external factors affect the dependability and the degree to which such factors are taken into consideration in the existing methods. Methods for assessing the life of the examined items are presented, among which control chart-based methods hold a prominent place. It is shown that the range proves to be a more convenient measure for data dispersion calculation than the standard deviation. Plotting the range of sample on a control chart along with the expectation makes it easier to notice an anomaly. The range is a rough measure of the rate of change of the monitored variable and its value may exceed the control limits on the range chart and inform of an anomaly much earlier than the change in the mean that may still be within the specified control limits.

Analysis of Rainfall Trend in North Interior Karnataka (NIK) Meteorological Subdivision of Karnataka by Non-Parametric Methodologies

The present study analyzed the trend in the month-wise, seasonal and annual rainfall employing three non-parametric tests viz. Mann-Kendall (M-K), Modified Mann-Kendall (MM-K) tests and Sen’s slope estimator for North Interior Karnataka (NIK) meteorological subdivision of Karnataka using 60 years (1960-2019) rainfall data. The M-K test results revealed that there is no monotonic trend in all data sets which indicates that there may exist the influence of serial correlation in the rainfall data. The MM-K test was applied to rainfall data of the NIK subdivision, the result of the test revealed that January, February, March and August months have a monotonic increasing trend whereas April, May, July and September months have a monotonic decreasing trend whereas June, October, November and December have no monotonic trend in monthly rainfall data. The winter season has a monotonic increasing trend, monsoon and post-monsoon seasons have a monotonic decreasing trend, and the pre-monsoon season has no monotonic trend in seasonal rainfall data. The MM-K test statistic (Tau) value for the NIK (-0.12) subdivision was found to be significant and negative indicating that there is a monotonic decreasing trend. Larger negative Sen’s slope values for NIK (-1.40) subdivision indicate the high decreasing rate of change of rainfall for annual rainfall data. Therefore, to know the trend in rainfall data most accurately in the presence of outliers and serial correlation, the Modified Mann-Kendall (MM-K) test and Sen’s slope estimator are recommended instead of Mann-Kendall (M-K) and Sen’s slope estimator.

Tensor-SqRA: Modeling the transition rates of interacting molecular systems in terms of potential energies.

Estimating the rate of rare conformational changes in molecular systems is one of the goals of molecular dynamics simulations. In the past few decades, a lot of progress has been done in data-based approaches toward this problem. In contrast, model-based methods, such as the Square Root Approximation (SqRA), directly derive these quantities from the potential energy functions. In this article, we demonstrate how the SqRA formalism naturally blends with the tensor structure obtained by coupling multiple systems, resulting in the tensor-based Square Root Approximation (tSqRA). It enables efficient treatment of high-dimensional systems using the SqRA and provides an algebraic expression of the impact of coupling energies between molecular subsystems. Based on the tSqRA, we also develop the projected rate estimation, a hybrid data-model-based algorithm that efficiently estimates the slowest rates for coupled systems. In addition, we investigate the possibility of integrating low-rank approximations within this framework to maximize the potential of the tSqRA.