Cumulative Distribution Function Research Articles

Geospatial mapping and entropy-based analysis for groundwater evaluation with estimation of potential health risks due to nitrate and fluoride exposure.

Groundwater is a vital source of freshwater, but its quality is often compromised by various physiochemical factors. In the Mid-Gangetic Plains, there is a concerning escalation in the degradation of groundwater quality due to anthropogenic interventions. However, there remains a paucity of comprehensive knowledge concerning groundwater quality and the associated health hazards it poses. In response to this gap, the current study focuses on Nalanda district (Bihar), where 78 groundwater samples were collected across district in the month of May 2022 and their various water quality parameters were quantified as per standard methods. The adequacy of groundwater for human use was assessed using an entropy-based water quality index (EWQI), which also evaluated the potential human health risk stemming from nitrate and fluoride contamination. Furthermore, an empirical Bayesian kriging (EBK) driven geostatistical approach was utilized to predict water quality parameters at ungauged sites. The analysis of results disclosed that the ionic dominance in groundwater followed the sequence as cations Ca2+ > Mg2+, and anions HCO3- > SO42- > Cl- > NO3- > F- > PO43-. The concentration of NO3- and F- exceeded the permissible BIS levels by 11.5% and 6.5% of the samples respectively. The analysis of EBK models suggested K-Bessel as the best-fit model for pH, Mg2+, TH, F-, NO3-, and SO42- spatial interpolation while exponential EBK model for EC, Cl-, and PO43- and whittle EBK model for TDS, Ca2+, and HCO3- spatial interpolation. Spearman's correlation analysis revealed that elevated TDS and EC levels, coupled with correlations between NO3-, SO42-, and Cl-, suggest anthropogenic influences. The EWQI of the groundwater samples ranged from 36.28 to 180.80. The analysis of EWQI values revealed predominantly fair to good groundwater quality across the study area, suitable for drinking purposes. The hazard quotients for NO3- and F- indicate that non-carcinogenic health risks are more significant with nitrate pollution. The combined health impact was assessed using total hazard index (HI), ranging from 0.20 to 3.29 for children, 0.19 to 3.05 for males, and 0.17 to 2.70 for females. The cumulative probability distribution revealed total hazard index (HI) > 1 in 41.56%, 34.62%, and 28.21% of samples for children, males, and females. The HI analysis indicated a substantially higher risk for children compared to adults within the study area. This study offers a novel combination of entropy-based water quality assessment and geostatistical EBK modeling to evaluate groundwater quality and health risks in ungauged areas. The findings provide valuable insights for improved groundwater management and health risk mitigation.

Combining unequal variance signal detection theory with the health belief model to optimize shared decision making in tinnitus patients: part 1—model development

IntroductionThe results from different Cochrane studies justify considerable professional equipoise concerning different treatment options for tinnitus. In case of professional equipoise, Shared Decision Making (SDM) is an indispensable tool for guiding patients to the intervention that best fits their needs. To improve SDM we developed a method to assess the accuracy and utility of decisions made by tinnitus patients when freely choosing between different treatment options during their patient journey. The different treatment options were audiological care and psychosocial counseling.MethodsWe developed a statistical model by combining Signal Detection Theory (SDT) with the Health Belief Model (HBM). HBM states that perceived severity of an illness is strongly related to sick-role behavior. As proxies for perceived severity, we selected hearing loss and Tinnitus Handicap Inventory (THI) score at baseline. Next, we used these proxies as predictors in linear regression models based on SDT to determine the likelihood ratio of true positive decisions (choosing a treatment option and experiencing an improvement of more than 7 points in THI-score) and false positive decisions (choosing a treatment option and experiencing an improvement of less than 7 points in THI-score) for audiological care and psychosocial counseling, respectively. Data was gathered in a prospective cohort of 145 adults referred for tinnitus care to an outpatient audiology clinic in the Netherlands. The participants were asked to decide freely on uptake of audiological care (provision of hearing aids with or without a sound generator) and uptake of psychosocial counseling. Logistic regression with Bayesian inference was used to determine the cumulative distribution functions and the probability density functions of true positive decisions and false positive decisions as function of hearing loss and baseline THI-score for both treatment options, respectively. With the cumulative distribution functions, we determined the accuracy of the decisions. With the probability density functions we calculated the likelihood ratios of true positive decisions versus false positive decisions as function of hearing loss and baseline THI-score. These likelihood ratio functions allow assessment of the utility of the decisions by relating a decision criterion to perceived benefits and costs.ResultsBaseline THI-score drives decisions about psychosocial counseling and hearing loss drives decisions about audiological care. Decisions about psychosocial counseling are more accurate than decisions about audiological care. Both decisions have a low accuracy (0.255 for audiological care and − 0.429 for psychosocial counseling), however. For decisions about audiological care the unbiased decision criterion is 37 dB(HL), meaning that a lenient decision criterion (likelihood ratio < 1) is adopted by patients with a hearing loss below 37 dB and a strict criterion (likelihood ratio > 1) by patients with a hearing loss exceeding 37 dB. For psychosocial counseling uptake the decision criterion is always strict, regardless of baseline THI-score. The distributions of the populations, that do and do not experience a clinically important change in THI-score, have unequal variances for psychosocial counseling, while they have almost equal variances for audiological care.DiscussionCombining SDT and HBM can help assess accuracy and utility of patient decisions and thus may provide valuable information that can help to improve SDM by combining patient related outcome measures, decision drivers, and perceived benefits and costs of a treatment.

Random uncertain motor parameters identification combining fourth-order moment and trust region

In random uncertain motor parameter identification field, there is low identification efficiency and ill-conditioned data coming from the second iteration involved in the uncertainty propagation and surrogate model between the motor parameters and the performance response. In this paper, the fourth-order moment method and trust region model management technology are combined to reduce the dependence of the surrogate model accuracy and improve the computational efficiency. In the framework, the inner layer calculates the cumulative probability under different motor performance response thresholds based on the fourth-order moment method, and obtain the probability density function of the calculated motor performance response. The outer layer transforms the random uncertain motor parameters identification into a deterministic optimization problem by minimizing the probability distribution between the calculated and the measured motor performance response. In the outer layer optimization, the trust region model management technology is used to divide the search interval of the entire parameter to be identified into a series of trust regions, construct a surrogate model on the trust region to calculate the motor performance response, and continuously update the trust region by comparing the response residuals between the calculated and measured motor performance response, so that the motor parameters to be identified continue to approach the interval where true values are located. At the same time, the genetic intelligent technology is introduced to further reduce the calculation cost. Finally, the probability distribution of random uncertain motor parameters is obtained according to the identified mean, standard deviation, skewness and kurtosis coefficients. The numerical results in the example verify that the random uncertain motor parameter identification can be effectively achieved by the proposed method.

On bid and ask pricing of European options via direct discretization of Choquet distorted expectations

We present a numerical approach to compute bid and ask prices of European options within the conic finance paradigm. In particular, we show how discretizing option bid and ask pricing formulae, where the latter are expressed in terms of Choquet integrals, allows to perform efficient pricing and calibration by means of the COS method and the fast Fourier transform technique. Additionally, the approach introduced provides alternative expressions for risk-neutral European option prices by leveraging the relationship between Choquet and Riemann-Stieltjes integrals. These alternative formulations, requiring only the discretization of the cumulative distribution function of the (transformed) terminal value of the underlying asset, enhance computational efficiency under risk-neutral settings. We provide practical illustrations of the methodology when the underlying asset process follows Heston or Variance Gamma dynamics for both pricing and implied liquidity calculations.

Machine Learning Based Localization of LoRa Mobile Wireless Nodes Using a Novel Sectorization Method

Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors.

Increased risk of open angle glaucoma in patients with moyamoya disease from a nationwide population-based cohort in Korea

This nationwide population-based retrospective cohort study aims to investigate the association between Moyamoya disease (MMD) and open-angle glaucoma (OAG). In this study using the Korean National Health Insurance Service database, a total of 36,432 patients having diagnostic code with MMD between 2002 and 2022, and their age-, sex-matched non-MMD controls (n = 346,769) were included. We used a Cox proportional hazard model to determine the association between MMD and subsequent OAG after excluding cases with preexisting diagnosis of MMD for the initial 2-year. Kaplan-Meier survival analyses with log-rank test were performed to compare the incidence probability of OAG based on the MMD diagnosis. Cox regression analysis showed that the diagnosis of MMD was associated with increased risk of subsequent diagnosis of OAG (adjusted HR, 1.26; 95% CI, 1.14–1.38; P < 0.001). The cumulative incidence probability of OAG was 3.7% in MMD group and 2.9% in control group at the end of the study period, and was significantly higher among MMD patients than controls consistently during the study period (P < 0.001).In conclusion, the nationwide longitudinal data of Korean population revealed a significant association between MMD and OAG. Presence of MMD may increase the risk of developing OAG.

An evaluation of the Africa-CORDEX Regional Climate Model's performance in simulating air temperatures and precipitation in the Melka-Wakena Catchment, Southeast Ethiopia

Understanding climate science is essential for effective policy development, adaptation, mitigation, and risk management. Given the inherent limitations in climate models, this study evaluates the performance of CORDEX Africa regional climate models to simulate precipitation and temperatures over the Melka-Wakena catchment. To accomplish this, the performance evaluation utilizes techniques such as multi-metric weighted ranking to select top-1 (best individual model), specific multi-model ensembles (top-N ensemble), multi-model ensemble, and average hybrid (top-N ensemble with MME) approaches at various temporal scales. Also, Taylor diagrams and empirical cumulative distribution functions are other useful elements for model comparison visualization. This study finds that, top-3 ensemble (for temporal scale tasmin, monthly pr), top-7 ensemble (monthly and seasonal tasmax), average hybrid (top-1 with MME (daily pr and tasmax), HadGEM2ES_RACMO22T for seasonal and yearly pr), and HadGEM2ES_RCA4 for yearly tasmax) are preferable, with weighted scores of 0.29, 0.44, 0.22, 0.20, 0.35, 0.40, 0.39, 0.33, 0.45, 0.25, and 0.33, respectively. In short, RCA4 and RACMO22T efficiently mimic air temperatures and precipitation in the Melka-Wakena catchment. While ensemble approaches are generally more resilient and possible, single-best-performing model approaches can be quite effective for long-term periods. Finally, these studies have an important implication for improving climate change assessment, adaptation, and mitigation measures.

Deficient postoperative anterior wall index adversely affects joint survival after transposition osteotomy of the acetabulum in patients with hip dysplasia.

This study aimed to determine clinical outcomes; relationships between postoperative anterior, lateral, and posterior acetabular coverage and joint survival; and prognostic factors for joint survival after transposition osteotomy of the acetabulum (TOA). Data from 616 patients (800 hips) with hip dysplasia who underwent TOA between November 1998 and December 2019 were reviewed. The median follow-up period was 8.9 years (IQR 5 to 14). A medical notes review was conducted to collect demographic data, complications, and modified Harris Hip Score (mHHS). Radiological indicators of acetabular coverage included lateral centre-edge angle (LCEA), anterior wall index (AWI), and posterior wall index (PWI). The cumulative probability of TOA failure (progression to Tönnis grade 3 or conversion to total hip arthroplasty) was estimated using the Kaplan-Meier product-limited method. A multivariate Cox proportional hazards model was used to identify predictors for failure. Median mHHS improved from 68 preoperatively to 96 at the latest follow-up (p < 0.001). The overall joint survival rate was 97% at ten years and 70% at 20 years. For the postoperative LCEA subgroups, survival in the deficient group was lower than that in the excessive (p = 0.006) and normal (p = 0.007) groups. For the postoperative AWI subgroups, survival in the deficient group was lower than that in the excessive (p = 0.015) and normal (p < 0.001) groups. Multivariate analysis identified age (p = 0.010), Tönnis grade 2 (p < 0.001), roundness index (p = 0.003), fair joint congruity (p = 0.004), and postoperative AWI (p = 0.002) as independent risk factors. Deficient postoperative AWI adversely affected joint survival after TOA, underscoring the importance of sufficient anterior acetabular coverage, along with precise surgical indications, to ensure successful hip joint preservation in the treatment of hip dysplasia.

Key Node Identification Method Based on Multilayer Neighbor Node Gravity and Information Entropy

In the field of complex network analysis, accurately identifying key nodes is crucial for understanding and controlling information propagation. Although several local centrality methods have been proposed, their accuracy may be compromised if interactions between nodes and their neighbors are not fully considered. To address this issue, this paper proposes a key node identification method based on multilayer neighbor node gravity and information entropy (MNNGE). The method works as follows: First, the relative gravity of the nodes is calculated based on their weights. Second, the direct gravity of the nodes is calculated by considering the attributes of neighboring nodes, thus capturing interactions within local triangular structures. Finally, the centrality of the nodes is obtained by aggregating the relative and direct gravity of multilayer neighbor nodes using information entropy. To validate the effectiveness of the MNNGE method, we conducted experiments on various real-world network datasets, using evaluation metrics such as the susceptible-infected-recovered (SIR) model, Kendall τ correlation coefficient, Jaccard similarity coefficient, monotonicity, and complementary cumulative distribution function. Our results demonstrate that MNNGE can identify key nodes more accurately than other methods, without requiring parameter settings, and is suitable for large-scale complex networks.

Assessing the Suitability of Probability Distribution Models for Streamflow Analysis in Peninsular Malaysia

Streamflow analysis is indeed a need for Malaysia which always threaten by extreme streamflow events including flooding. However, the determination of the most suitable probability distribution for streamflow analysis has been the major concern and received considerable critical attention recently. The purpose of this study is to analyse the performances of several distributions in fitting the streamflow data collected form 11 streamflow stations in Peninsular Malaysia. and select the best performed distribution eventually. Normal, Generalized Extreme Value (GEV), three-parameter Gamma (G3), two-parameter Gamma (G2), three-parameter Weibull (W3), two-parameter Weibull (W2), three-parameter Log-Normal (LN3) and two-parameter Log-Normal (LN2) were used in this study to fit the maximum, minimum, and mean streamflow with monthly and seasonal time scales by using Easyfit software. The performances of distributions were evaluated through constructing Probability-Probability (P-P) Plot, Cumulative Distribution Function (CDF) and conducting goodness-of-fit tests including Chi-square () Test, Anderson-Darling (AD) Test and Kolmogorov-Smirnov (KS) Test. All the results were tabulated for comparison and the most suitable probability distribution was selected. Overall, the results indicated that the GEV distribution was the best fit distribution for most of the stations and most of the data series or time scales, while the LN3 distribution appeared to be the second-best distribution. These findings add to a growing body of literature on the selection of probability distribution especially for the streamflow analysis in Peninsular Malaysia. Besides, these findings provide significant information and practical knowledge in supporting the future development of streamflow-related plans or management including the flood and drought mitigation plans.

Health risk assessment of lead and cadmium in milk-based products of Iran

The primary objective of this study is to assess the health risks associated with lead (Pb) and cadmium (Cd) contamination in dairy products consumed in Iran. To conduct the research, 132 samples of cow milk, yogurt, cream, and doogh (a traditional Iranian beverage) were collected from both traditional and industrial livestock farms in Zanjan, Iran. The samples were microwave-digested, and an atomic absorption spectrophotometer equipped with a graphite furnace was used to determine toxic metals contents. The findings of the study revealed that the average concentrations of Pb in milk and dairy product exceeded the permitted limits. The mean ± se levels of Pb and Cd in milk, yogurt, doogh, and cream were 92.64 ± 8.73, 52.19 ± 8.42, 78.80 ± 12.89, 106.07 ± 8.94 μg/kg and 2.34 ± 0.22, 1.21 ± 0.20, 2.59 ± 0.57, 5.61 ± 0.63 μg/kg, respectively. For the purpose of exposure assessment, the chronic daily intake (CDI) was utilized, while the Hazard Quotient (HQ) and Total Hazard Quotient (THQ) were employed for non-carcinogenic risk assessment. Additionally, the Incremental Lifetime Cancer Risk (ILCR) was utilized for carcinogenic risk assessment. The results revealed that the highest exposure to Pb and Cd in the P 95th of CDI were observed in milk, followed by yogurt, doogh, and cream. According to our finding, the HQ and THQ values for potentially toxic elements (PTEs) related to the consumption of dairy products in the P 95th of cumulative probability were below 1. The results indicate that the ILCR of Cd (P 95th) due to the consumption of dairy products falls within the acceptable risk range (10−4 to 10−6). While our findings indicate levels of Cd in dairy products, it is important to note that there are currently no established standards for permissible Cd concentrations in Iran. Therefore, further research and regulatory frameworks are needed to ensure public safety.

A more holistic view of the logarithmic dose-response curve offers greater insights into insulin responses

The stimulus-response curve is usually modeled by the Hill function due to its simplicity and clear molecular mechanisms (Michaelis-Menten type of kinetics). Unfortunately, the mechanisms do not explain why the stimulus is ubiquitously measured by logarithmic dose rather than the dose itself and why the log(dose)-response curve possesses such fine properties as symmetry and wide adjustability. Here, the dose response is considered from a holistic perspective spanning multiple biological levels from molecules to the whole organism, which reveals that an appropriate model for log(dose) response is the cumulative normal distribution (CND) function, which had only statistical implication previously but now possess mechanistic-statistical duality. The present CND model establishes a connection between single-cell all-or-none responses and the graded response at the tissue/organism level, reveals the raison d'être of the logarithmic transformation, explains why log(dose)-response curve possesses many fine properties, and reveals new mechanisms of tissue/organism dose-response, including homogeneity-induced sensitivity. It also provides new insights into vital biological processes, such as the insulin dose response.

SMETHODS FOR REDUCING THE PAPR OF FBMC SIGNALS BASED ON A DISCRETE-NONLINEAR SPROTT SYSTEM IMPLEMENTED OVER A FINITE GALOIS FIELD

In this article, we provide a comparative analysis of various methods for reducing the peak factor of Filter bank Multicarrier signals in a multiplexing system with a filter bank using the discrete-nonlinear Sprott system implemented over a finite Galois field. The methods of partial transmission sequence, scrambling, and a combined method, which includes both methods, are considered. Each method is based on the use of a pseudorandom sequence, the formation of which is provided by generators based on a discrete nonlinear Sprott system. As a result of the work, the peak factor for each method is estimated and an additional cumulative distribution function is also presented. In addition, the influence of the number of iterations and windows used in the partial transmission sequence method on the peak factor was estimated. The results obtained in the course of the study can be applied in the design of information transmission systems.

A multi-scale uncertainty analysis method based on the Hermite–Chebyshev polynomials for dynamic responses of FRP composite structures with hybrid uncertainties

Multi-scale hybrid uncertainties in material properties of FRP composites stemming from their manufacturing processes present significant challenges for dynamic analysis and reliability assessment. This paper proposes a multi-scale uncertainty surrogate model based on Hermite–Chebyshev polynomials. The relationship between micro- and macro-scale material properties is established using the Mori–Tanaka method. To demonstrate the efficacy of the proposed method, case studies are conducted on both a FRP wide-flange I-beam structure and a FRP truss bridge. Results indicate that this method accurately determines the probability density functions and cumulative distribution functions of natural frequencies and mode shapes. Notably, the method efficiently computes the upper and lower bounds of dynamic failure probability of FRP truss bridge with high numerical efficiency.

A Multi-Model Survival Analysis of Lung Cancer Using Parametric Techniques

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, underscoring the critical need for effective prognostic tools. This study utilizes survival analysis to explore the factors that influence the survival outcomes of North Central Cancer Treatment data related to lung cancer. The main goal of this study is to compare and contrast various statistical models, including the Weibull, Exponential, Log-gaussian, Gumbel, and Rayleigh models. We have computed important functions, such as the survival function, the hazard function, and the cumulative hazard function, for all the considered distributions. The Anderson-Darling and Cramer Von-Mises tests, which are Goodness of fit tests, effectively compare and assess various parametric regression survival models. The Weibull survival model is interpreted to be the most effective and efficient way to study the lung cancer dataset, which is concluded upon evaluating the results of Anderson-Darling statistic 0.28745, Cramer Von-Mises statistic 0.0450, Mean Survival Probability 0.9697, Mean Cumulative Survival Probability 0.0303, Akaike Information Criterion 1,650.753 and Bayesian Information Criterion 1623.329 of the Weibull, Exponential, Log-gaussian, Gumbel, and Rayleigh parametric regression survival models.

Effects of sodium-glucose cotransporter-2 inhibitors on chronic kidney disease progression: a multi-state survival model

BackgroundCurrent guidelines recommend good glycemic control in patients with type 2 diabetes (T2D) to limit the progression of associated complications with combination therapies. This study aimed to compare the rate of chronic kidney disease (CKD) progression between patients who did or did not receive sodium-glucose cotransporter-2 inhibitors (SGLT2i) using a multistate model with two intermediate states (i.e., CKD stage 4 (CKD4) and 5 (CKD5)) and one absorbing state (i.e., death).MethodsData from patients with T2D and CKD stage 3 (CKD3) were retrieved for analysis. Patients treated with SGLT2i were matched 1:2 by prescription date with non-SGLT2i patients. The multistate model was constructed from Cox survival regression models specific to each transition stage. Cumulative failure and transition probabilities were estimated from bootstrapping.ResultsData from 6582 patients (2194 and 4388 patients in the SGLT2i and non-SGLT2i groups, respectively) were analyzed. At 10-year follow-up, patients in the SGLT2i group were more likely to remain at CKD3 compared to the non-SGLT2i group: 82.3% (95% CI 79.9%, 84.6%) vs 60.4% (57.6%, 63.4%). Transition probabilities to CKD4, CKD5, and death were lower in the SGLT2i group than non-SGLT2i group: 11.3% (9.5%, 13.3%) vs 19.8% (17.4%, 22.2%), 2.4% (1.5%, 3.4%) vs 7.4% (5.8%, 9.0%), and 4.1% (2.9%, 5.3%) vs 12.4% (10.3%, 14.6%), respectively.ConclusionSGLT2i may delay the decline in renal function and slow CKD progression compared to standard care without SGLT2i.

Risk of Recurrent Ventricular Tachyarrhythmia Following the Occurrence of a First Ventricular Arrhythmic Event in Patients with a Primary Prevention Implantable Cardioverter-Defibrillator

There are limited data on clinical and arrhythmic outcomes following a first ventricular tachyarrhythmia (VTA) in heart failure (HF) patients who receive a primary prevention implantable cardioverter-defibrillator (ICD). To quantify the burden and identify risk factors for recurrent VTA in this population, and to evaluate the risk of all-cause mortality associated with recurrent VTA. The study comprised 789 patients who experienced VTA following primary prevention ICD implantation in 5 ICD trials (MADIT-II, MADIT-RISK, MADIT-CRT, MADIT-RIT, RAID). Landmark analysis was used to quantify the burden and identify predictors of recurrent VTA. Time-dependent analysis was used to evaluate the association of VTA recurrence with subsequent mortality. Among the study patients, mean age was 63 years and 17% were women. The cumulative probability of experiencing at least one recurrent VTA episode at 3 years following a first VTA episode was 60%; the recurrent VTA burden after a first event over 3 years was 2.8 episodes per patient. The risk of recurrent VTA remained ≥56% at 3 years regardless of baseline clinical and echocardiographic risk factors. VTA recurrence was associated with a significant two-fold increased risk of subsequent all-cause mortality. Patients with a primary prevention ICD who experience an episode of VTA are at very high risk of recurrent VTA, regardless of baseline risk factors. Recurrent VTA is associated with a pronounced increase in the risk of death. These findings suggest a need for early intervention following a first VTA in patients who receive a primary prevention ICD.

An improved dynamic-sensitive centrality based on interactive influence for identifying influential nodes in aviation networks

The identification of influential nodes in complex networks is a hot topic among scholars. Classical methods commonly analyze single node information or static structures but seldom emphasize dynamic properties and the interactive influence of nodes. Here, we proposed an improved Dynamic-Sensitive centrality (IDS) method by considering the interactive influence of both the self and neighbor nodes. Based on six real aviation networks and the Susceptible Infected Recovered (SIR) spreading disease model, we simulated the actual spreading process within these networks. Relevant experiments were conducted through Kendall’s correlation coefficient, the imprecision function, and the complementary cumulative distribution function. The experimental results demonstrated that the IDS can more accurately identify the influential node and effectively differentiate the node influence in the network compared with other benchmark methods. Especially in the EU air-2 network, the IDS results in Kendall’s correlation coefficient are improved by 105% compared to the DS centrality.

Outcomes after a virological failure to first-line second-generation INSTI-based therapy in a real-life setting.

Virological failures of first-line second-generation (SG) INSTI-based regimens are rare, usually characterized by low viremia and absence of drug resistance mutations. To explore the efficacy of rescue regimens introduced after virological failure (VF) to a first-line SG-INSTI therapy. This was a retrospective study on people living with HIV (PWH) failing a first-line SG-INSTI regimen [DTG/3TC, BIC/FTC/TAF, DTG-based three-drug regimen (DTG-3DR)] between 24 March 2016 and 31 December 2021. Follow-up accrued from the second viral load (VL) ≥ 50 copies/mL under SG-INSTI regimen (baseline) until virological success (VS, achievement of at least one VL < 50 copies/mL after baseline) or last visit. Cumulative probabilities of VS were estimated by Kaplan-Meier curves and compared using a log-rank test. Overall, of 521 naïve PWH who started a first-line SG-INSTI regimen, 45 (8.6%) had VF after a median of 14.9 (IQR = 6.9-25.9) months: 33/395 (8.4%) individuals failed a DTG-3DR, 11/102 (10.8%) a BIC/FTC/TAF and 1/24 (4.2%) failed a DTG/3TC.At baseline, 12/45 (27%) PWH changed antiretroviral therapy [median baseline VL 134 (IQR = 81-233) copies/mL], while 33 (73%) maintained their failing regimen [median baseline VL 75 (IQR = 60-145) copies/mL].During a median follow-up of 5.13 (IQR = 3.8-7.1) months, 34 (75.6%) PWH achieved VS: 25/33 (75.8%) maintaining their failing regimen, 9/12 (75%) switched regimen; the estimated 6- and 12-months probabilities of VS were 59% and 84%, respectively.There was no difference in VS curves between PWH who maintained their failing regimen and those who switched therapy. Most individuals remained on their failing regimen, achieving spontaneous virological suppression in most cases. These data help to understand a real-life VF scenario in the context of the current SG-INSTI era.

A parsimonious dynamic mixture for heavy-tailed distributions

Dynamic mixture distributions are convenient models for highly skewed and heavy-tailed data. However, estimation has proved to be challenging and computationally expensive. To address this issue, we develop a more parsimonious model, based on a one-parameter weight function given by the exponential cumulative distribution function. Parameter estimation is carried out via maximum likelihood, approximate maximum likelihood and noisy cross-entropy. Simulation experiments and real-data analyses suggest that approximate maximum likelihood is the best method in terms of RMSE, albeit at a high computational cost. With respect to the version of the dynamic mixture with weight equal to the two-parameter Cauchy cumulative distribution function, the reduced flexibility of the present model is more than compensated by better statistical and computational properties.