Average Estimation Research Articles

Adapting the High-Resolution PlanetScope Biomass Model to Low-Resolution VIIRS Imagery Using Spectral Harmonization: A Case of Grassland Monitoring in Mongolia

Monitoring grassland biomass accurately and frequently is critical for ecological management, climate change assessment, and sustainable resource use. However, the use of single-satellite data faces challenges due to trade-offs between spatial resolution and temporal frequency, especially for large areas. High-resolution imagery, such as PlanetScope, provides detailed spatial data but presents significant challenges in data management and processing over large regions. Conversely, low-resolution sensors such as JPSS-VIIRS offer daily global coverage with low memory data but lack the spatial detail required for precise biomass estimation, making it difficult to retrieve or validate model parameters due to the mismatch with small ground reference data polygons. To overcome these limitations, this study introduces a robust methodology for accurate frequent biomass estimation based on JPSS-VIIRS data through spectral harmonization, adapting a high-resolution biomass estimation model originally developed from PlanetScope imagery. The core innovation is an optimized Spectral Band Adjustment Factor (SBAF) approach tailored specifically to grassland spectral characteristics. This method significantly enhances spectral alignment, reducing red-band reflectance discrepancies from 6.2% to 4.8% in grassy areas and from 6.9% to 4.0% in bare areas. NDVI discrepancies also improved substantially. Applied across Mongolia, the harmonized VIIRS data estimated a five-year average biomass of 71.4 g/m2, clearly reflecting environmental variability. Specifically, the P375 dataset showed average biomass estimates of 54.8 g/m2 for desert grasslands (10.5% higher than PlanetScope), 122.6 g/m2 for dry grasslands (9.6% higher), and 134 g/m2 for mountain grasslands (1.9% lower). The uncertainty analysis showed strong overall agreement with PlanetScope-derived biomass, with an RMSE of 11.6 g/m2, a mean percentage difference of 10.74%, and an R2 of 0.92. While mountain grasslands exhibited the lowest RMSE, a relatively lower R2 indicated limited variability. Higher uncertainty in desert and dry grasslands highlighted the impact of ecological heterogeneity on biomass estimation accuracy. These detailed comparisons demonstrate the effectiveness and accuracy of the proposed methodology in bridging spatial and temporal gaps, providing a valuable tool for large-scale weekly grassland biomass monitoring with applicability beyond the Mongolian context.

Conformalized Interval Arithmetic with Symmetric Calibration

Uncertainty quantification is essential in decision-making, especially when joint distributions of random variables are involved. While conformal prediction provides distribution-free prediction sets with valid coverage guarantees, it traditionally focuses on single predictions. This paper introduces novel conformal prediction methods for estimating the sum or average of unknown labels over specific index sets. We develop conformal prediction intervals for single target to the prediction interval for sum of multiple targets. Under permutation invariant assumptions, we prove the validity of our proposed method. We also apply our algorithms on class average estimation and path cost prediction tasks, and we show that our method outperforms existing conformalized approaches as well as non-conformal approaches.

Weighted average ensemble for Cholesky-based covariance matrix estimation

The modified Cholesky decomposition (MCD) is an efficient technique for estimating a covariance matrix. However, it is known that the MCD technique often requires a pre-specified variable ordering in the estimation procedure. In this work, we propose a weighted average ensemble covariance estimation for high-dimensional data based on the MCD technique. It can flexibly accommodate the high-dimensional case and ensure the positive definiteness property of the resultant estimate. Our key idea is to obtain different weights for different candidate estimates by minimizing an appropriate risk function with respect to the Frobenius norm. Different from the existing ensemble estimation based on the MCD, the proposed method provides a sparse weighting scheme such that one can distinguish which variable orderings employed in the MCD are useful for the ensemble matrix estimate. The asymptotically theoretical convergence rate of the proposed ensemble estimate is established under regularity conditions. The merits of the proposed method are examined by the simulation studies and a portfolio allocation example of real stock data.

Tracking grid-level freshwater boundary exceedance along global supply chains from consumption to impact

Abstract Consumption behaviours exert pressure on water resources both locally and globally through interconnected supply chains, hindering the achievement of Sustainable Development Goals 6 (Clean water and sanitation) and 12 (Responsible consumption and production). However, it is challenging to link hotspots of water depletion across spatial scales to final consumption while reflecting intersectoral competition for water. In this study, we estimated the global exceedance of regional freshwater boundaries (RFBs) due to human water withdrawal at a 5-arcmin grid scale using 2015 data, enabling the identification of hotspots across different spatial scales. To reduce uncertainty, we used average estimates from 15 global hydrological models and 5 environmental flow requirement methods. We further attributed the hotspots of exceedance to final consumption across 245 economies and 134 sectors via the multi-region input–output model EMERGING. Our refined framework revealed previously unknown connections between regional hotspots and consumption through international trade. Notably, we found that 24% of grid-level RFB exceedance (718 km3 yr−1; 95% confidence interval of 659–776 km3 yr−1) was outsourced through trade, with the largest flows (52 km3 yr−1; 95% confidence interval of 47–56 km3 yr−1) from water-stressed South and Central Asia to arid West Asia. The demand for cereals and other agricultural products dominated global consumption-based RFB exceedance (29%), while the exports of textiles and machinery and equipment exacerbated territorial exceedance in manufacturing hubs within emerging economies. Our methodology facilitates the tracing of global hotspots of water scarcity along supply chains and the assignment of responsibilities at finer scales.

Investigation on short-term slow slip events in the northeast Japan subduction zones using decadal GNSS data

Abstract Since their discovery over 25 years ago, slow slip events (SSEs) have been regarded as key phenomena for better understanding the characteristics and kinematics of faults. Although ordinary and seismic slow earthquake activities indicate numerous SSE occurrences, the number of SSEs detected by geodetic measurements remains limited in northeast Japan, where the Pacific plate subducts beneath the Okhotsk and Philippine Sea plates. In this study, we focus on short-term SSEs (S-SSEs) with a duration of several days to weeks and investigate their activity by a systematic detection method and a time series stacking technique using data from Global Navigation Satellite System (GNSS). By applying the systematic detection method to ~ 27-year data, we identified 71 S-SSEs. Most of them are located in the southernmost part of the analyzed region. These isolated distributions are likely attributable to the GNSS station distribution and the subduction of the Philippine Sea plate. In addition, we elucidate the Sanriku and Tokachi-Oki SSEs, which are synchronized with repeating and slow earthquake activities, respectively. We conduct the time series stacking with reference to very low-frequency earthquakes in Tokachi-Oki and average fault model estimation using displacements obtained from the stacked series to discuss their possible location range with their uncertainty. The average displacement field exhibits southeastward displacements in the coastal area, which indicates the occurrence of interplate slip. Although the estimated fault size has a large uncertainty, the average fault model is located offshore Hokkaido and overlaps with the source area of very low-frequency earthquakes, tectonic tremors, and the afterslip of the 2003 Tokachi-Oki earthquake. Our scrupulous data processing and techniques to emphasize deformation signals demonstrate the overlap between the source area of the SSEs and those of other interplate slip phenomena including repeating earthquakes, seismic slow earthquakes, and afterslip. Graphical abstract

Test on Impact Location Estimation of Missile Warhead Fragments Using Acceleration Signals

This study investigates the feasibility of using acceleration signals to estimate the initial impact location of fragments generated by blast-fragmentation warheads. The experimental setup involved attaching three single-axis accelerometers to a steel plate, spaced 400 mm apart. A preliminary test used hammer strikes to verify the method, while the primary test employed active warheads. For each test, the time difference of arrival(TDOA) was analyzed using the acceleration signals. Trigonometric techniques were used to determine the impact locations, using the group velocity(<i>C</i><sub><i>g</i></sub>) calculated based on the wave propagation characteristics. Active warhead results showed that the tests achieved an average location estimation error of less than 10 % in both horizontal and vertical axes, validating the methodology despite the environmental complexities. This demonstrates the potential of using acceleration signals for fragment impact evaluation and highlights avenues for further research.

Outcomes for recurrent or metastatic head and neck cancer by HPV status: a systematic review and meta-analysis.

Human papillomavirus (HPV)-positive (HPV+) recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC) exhibits distinct clinical behavior compared with HPV-negative (HPV-) R/M HNSCC. However, most studies reference historical efficacy benchmarks from cohorts combining HPV+ and HPV- R/M HNSCC for comparison with the efficacy of novel therapeutics. This analysis aims to assess differential outcomes specific to HPV+ and HPV- R/M HNSCC to reflect HPV-associated prognostic considerations. Studies from March 2006 to June 2024 were searched. All therapeutic trials in R/M HNSCC that reported outcomes by HPV status were included. Meta-analyses were conducted to evaluate overall survival (OS) as the primary outcome of interest, objective response rate, and progression-free survival as secondary outcomes. Weighted averages were used to estimate the outcome measures for each group. A total of 10 studies representing 3509 patients met the inclusion criteria. For the first-line setting, the weighted average OS estimates were 20.7 months (95% CI, 19.4-21.9) and 12.2 months (95% CI, 11.9-12.4) for HPV+ and HPV- HNSCC, respectively. For the second or later line treatment setting, the weighted average OS estimates were 11.1 months (95% CI, 10.2-12.1) and 8.3 months (95% CI, 7.9-8.6) for HPV+ and HPV- subgroups, respectively. Our analysis demonstrated a clinically meaningful difference between in the HPV+ and HPV- R/M HNSCC populations. These findings support the establishment of HPV-specific outcome benchmarks, which are essential for prognostic awareness, optimizing therapeutic development, and accurately interpreting clinical trial data.

Epidemiology of Chronic Suppurative Otitis Media: Systematic Review To Estimate Global Prevalence

BackgroundChronic Suppurative Otitis Media (CSOM) is a disorder characterised by a perforation of the tympanic membrane leading to ear discharge and hearing loss, a disability causing marginalisation in employment, education, social stigma, and reduced wellbeing and quality of life. Previous studies on the global epidemiology of this condition, despite methodological limitations, have estimated global prevalence at 200 million.MethodsAdhering to PRISMA guidelines, published literature was systematically reviewed across Ovid and Embase databases, with original community-based studies on CSOM published from 2004 to March 19th, 2025 extracted for final consideration on quality and relevance. Studies focusing on hospital populations were excluded as they seem more likely to represent a biased group of severe cases. Published articles were analysed for data on CSOM prevalence and associated risk factors.ResultsFrom 5,394 articles, 29 cross-sectional studies met the inclusion criteria. Included studies predominantly originated from low- and middle-income countries (LMICs) and focused on paediatric populations. A pooled average estimate revealed a CSOM global prevalence of 3.8% of the global population, or 297 million people, 85% (252 million) of which in LMICs. 64 million (21.5%) of those affected had bilateral disease, and 184 million (62%) had disabling hearing loss defined as > 25–30 dB. Hearing impairment was reported in only four studies, which affected 50–78% of participants. Frequency of ear discharge was reported in only one study.ConclusionThe study identifies a significant global burden of CSOM, disproportionately affecting LMICs, and signals to healthcare providers and policymakers a pressing need for initiatives to prevent and manage this disease.

BioMeta: modular reprogrammable metasurface for noninvasive human respiration monitoring.

Human vital-sign sensing using electromagnetic wave has emerged as a promising technology for the noninvasive monitoring of individuals' health status. Here, a modular reprogrammable metasurface system is presented to suppress noise in noninvasive human respiration sensing. The proposed reprogrammable Biological Metasurface (BioMeta) provides three-dimensional dynamic control over wavefront shaping and thus can reduce interference from human limb motions. This capability allows the system to acquire health data accurately and reliably and is particularly beneficial in real-world environments where human subjects may change posture or location frequently. Furthermore, the meta-atom in BioMeta is modular and detachable, thereby resulting in reusable properties and promoting environmental sustainability. Meanwhile, the characteristics of mechanical control enable BioMeta to operate without continuous power supply, thus saving energy to a certain extent. A contactless human respiration sensing prototype based on the proposed BioMeta is demonstrated. Experimental results validate that the BioMeta system can accurately monitor the breathing of multiple individuals with limb movements by means of time multiplexing, with an average estimation error of 0.5 respiration per minute. The proposed system enhances sensing accuracy and reliability for noninvasive human respiration monitoring, presenting a versatile and environmentally friendly solution for applications like elderly care and disease monitoring.

Mean ecological indicator values: use EIVE but no cover-weighting

Aims: To test the predictive power of mean ecological indicator values (EIVs) based on different EIV systems and weighting approaches. Study area: Preda in Grisons, Switzerland. Methods: We used three regional datasets of vegetation plots accompanied with measured soil pH values or mean annual near soil air temperature. We calculated mean EIVs for each plot with four EIV systems that cover the region, namely “Ellenberg”, “Landolt”, “Tichý” and the Ecological Indicator Values for Europe (EIVE), combined with four weighting approaches (unweighted, cover-weighted, square-root cover-weighted, inverse niche width weighted). We correlated the mean EIVs of each combination with the measured environmental variables and compared the mean Pearson r values. Results: No cover-weighting (0.78) was slightly better than square-root cover-weighting (0.75) and clearly better than full cover-weighting (0.68). In the two EIV systems providing a niche width measure (EIVE and “Landolt”), inverse niche weighting gave similar results than no weighting. Mean EIVE values (0.76) had significantly stronger correlations than “Tichý” (0.73), while the differences to “Ellenberg” (0.75) and “Landolt” (0.71) were not significant. Conclusions: The results suggest that even within the definition areas of two long-established EIV systems (“Ellenberg”, “Landolt”), EIVE gives at least as good predictions as these and significantly better than “Tichý”. Likewise, any type of cover-weighting reduces the predictive power of mean EIVs. Both findings could be the consequence of the statistical principle “wisdom of the crowd”, according to which the average estimate of several sources (be it regional scientists or plant species) is usually better than the estimate of one or few experts. Accordingly, EIVE currently is the best choice for mean EIVs in Europe, and no cover-weighting should be applied. We recommend that similar studies should be undertaken in other regions and for other niche dimensions. Syntaxonomic reference: Mucina et al. (2016). Abbreviations: EIV = ecological indicator value; EIVE = Ecological Indicator Values for Europe 1.0; R = ecological indicator value for soil reaction; T = ecological indicator value for temperature.

Psychophysical evidence for an internal model of gravity in the visual and vestibular estimates of vertical motion duration

The motion of objects and ourselves along the vertical is affected by gravitational acceleration. However, the visual system is poorly sensitive to accelerations, and the otolith organs do not disassociate gravitational and inertial accelerations. Here, we tested the hypothesis that the brain estimates the duration of vertical visual motion and self-motion by means of an internal model of gravity predicting that downward motions are accelerated and upward motions are decelerated by gravity. In visual sessions, a target moved up or down while participants remained stationary. In vestibular sessions, participants were moved up or down in the absence of a visual target. In visual-vestibular sessions, participants were moved up or down while the visual target remained fixed in space. In all sessions, we verified that participants looked straight-ahead. We found that downward motions of either the visual target or the participant were systematically perceived as lasting less than upward motions of the same duration, and vice-versa for the opposite direction of motion, consistent with the predictions of the internal model of gravity. In visual-vestibular sessions, there was no significant difference in the average estimates of duration of downward and upward motion of the participant. However, there was large inter-subject variability of these estimates.

A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation

A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation

Sandwich Miura-Ori Enabled Large Area, Super Resolution Tactile Skin for Human-Machine Interactions.

With substantial advances in materials science and electronics, flexible tactile sensors have emerged as a promising sector with extensive applications, notably in human-machine interactions. However, achieving large-area sensing with few sensing units at a low cost remains a challenge; the use of sensor arrays will complicate wiring and increase costs. To solve these issues, a sandwich Miura-ori (SMo)-enabled super-resolution tactile skin capable of resolving normal and shear forces is proposed, and a theoretical model that incorporates the impact of actual manufacturing process is also developed, enabling the model to be employed for different tactile skins following calibration. Using machine learning techniques, the proposed tactile skin can accurately localize touch inputs (average localization error of 1.89 mm) and estimate the external force (average estimation error of 8%). Furthermore, a curved SMo skin is designed and fabricated using the tessellation algorithm, then installed on a robotic arm to control the motion, demonstrating its potential in human-machine interactions. This research introduces a straightforward and cost-effective approach to the design and manufacturing of super-resolution tactile skins, and it also offers a valuable solution for future large-area tactile sensortechnologies.

Study on effects from motion on an estimation method of average speed of sound

Abstract In the image reconstruction of ultrasound B-mode images, an average speed of sound (SoS) in living tissue is assumed to be a constant value. However, the assumed value may be different with the true values because the SoS varies depending on the properties of the tissue. The error between true and assumed SoSs causes degradation of the ultrasound B-mode images. We have proposed a method for estimating an average SoS distribution. In this study, we conducted numerical simulation and evaluated effects of motion on the average SoS estimation method. Furthermore, we conducted phantom experiments. In both the numerical simulation and phantom experiment, when a B-mode image was reconstructed using the estimated average SoS distribution, the contrast value was improved regardless of the motions.

Blood Oxygenation Quantification in Multispectral Photoacoustic Tomography Using A Convex Cone Approach.

Multispectral photoacoustic tomography (PAT) can create high spatial and temporal resolution images of oxygen saturation (sO2) distribution in deep tissue. However, unknown distributions of photon absorption and scattering introduces complex modulations to the photoacoustic (PA) spectra, dramatically reducing the accuracy of SO2 quantification. In this study, a rigorous light transport model was employed to unveil that the PA spectra corresponding to distinct SO2 values can be constrained within separate convex cones (CCs). Based on the CC model, SO2 estimation is achieved by identifying the CC nearest to the measured data through a modified Gilbert-Johnson-Keerthi (GJK) algorithm. The CC method combines a rigorous physical model with data-driven approach, and shows outstanding robustness in numerical, phantom, and in vivo imaging experiments validated against ground truth measurements. The average SO2 estimation error is approximately only 3% in in vivo human experiments, underscoring its potential for clinical application. All of our computer codes and data are publicly available on GitHub.

ADHD superior frontal gyrus is hypoactive during working memory tasks and experiences a smaller activation difference between tasks

Attention-deficit/hyperactivity disorder (ADHD) is a common neurobehavioral disorder that affects children’s learning and daily activities. According to a survey in 2022, 11.4% of children in the U.S. have been diagnosed with ADHD (Centers for Disease Control and Prevention, 2024). To better understand the biological impact of ADHD on human brains, I tested for a functional difference in their brain activation by fMRI neuroimaging analysis on the superior frontal gyrus of ADHD and TD (Typically Developed) boys during working memory and fixation tasks. They were asked to perform visuospatial working memory tasks with 1-back and 2-back tracks, with a fixation task before each 2-back track task. They were shown a series of single letters being presented at one of the four corners of a square, and were asked to determine whether the new letter was in the same corner. The fixation task required minimal cognitive effort, in which participants were asked to press down a button as the color of the box changed, thus serving as a baseline condition. The brain area studied was the superior frontal gyrus, the part of the brain responsible for cognitive functioning especially in working memory. Results of average beta weight estimates suggested ADHD boys’ superior frontal gyrus to be hypoactive during visuospatial memory tasks and hyperactive during fixation tasks compared to TD boys, suggesting potential difficulty in task engagement and managing disruptive thoughts; and a smaller difference in ADHD boys’ brain activation between the two tasks, suggesting potential difficulty in brain specialization and task switching.

Airborne particulate matter exposure in male sugarcane workers at risk for chronic kidney disease in Guatemala.

There is an international epidemic of chronic kidney disease of unknown cause (CKDu) in agricultural working populations. Particulate air pollution is a likely contributing factor in populations at risk for CKDu, but there is little personal breathing zone data for these workers. We collected 1 to 3 personal breathing zone particulate matter <5 microns (PM5) gravimetric measurements in 143 male sugarcane harvesters over 2 seasons and concurrent ambient samples using personal sampling pumps and cyclone inlets as a sampling train. Due to very high concentrations observed during a pilot of these methods, personal breathing zone sampling duration was set to 4h, beginning either at the start of a work shift (AM) or delayed for 4h (PM). To obtain full-shift exposure concentrations we calculated 8-h time-weighted average (TWA, in µg/m3) estimates of each worker's full-shift personal breathing zone PM5 exposure concentration by averaging their individual monitored concentration with the median concentration of the unmonitored AM or PM segment from all workers that day to obtain an 8-h TWA. Median full-shift personal TWA PM5 concentrations were 449 μg/m3 (range 20.5 to 1,930 μg/m3), which were much higher than ambient concentrations in these fields (median 136, range 22.5 to 2,360 μg/m3). These findings document very high personal breathing zone PM5 exposure in workers at risk for CKDu: median concentrations for all workers were 3.5 (range <1 to 33.6) times as high as concurrent ambient concentrations. These findings suggest that ambient measurements of particulate matter are insufficient to estimate personal exposure in this population and that personal breathing zone monitoring should be used to fully explore air pollution as a risk factor for CKDu. Given that particulate matter from this source likely has multiple hazardous constituents, future research should focus on characterizing all constituents and explore associations with biomarkers of kidney injury.

Evaluation of Dual Emission Control Effects and Green Innovation Mechanisms: Evidence Based on the Construction of Free Trade Pilot Zones

With the replication and promotion of the experience of pilot reform and innovation, as well as the continuous deepening of institutional openness, exploring green and low-carbon models has become an important measure for the country to promote the high-quality development of free trade pilot zones. This study focused on the core task of institutional innovation in free trade pilot zones, measured and quantified the level of green investment liberalization and green trade facilitation in each region, and explored the carbon emission dual control effect of free trade pilot zones using cutting-edge methods, such as group period average processing effect estimation （CATT）, imputation, and stacked DID. The research conclusions were as follows： ① The phased establishment of free trade pilot zones reduced the total carbon emissions by 27.40% and the carbon emission intensity by 14.30%. ② The pilot free trade zone achieved dual carbon emission control through the spillover effect of green innovation from foreign investment, the direct effect of green innovation from local enterprises, and the indirect effect of green innovation from digital trade. The comprehensive implementation of the negative list of market access, the signing and implementation of free trade agreements between China and other countries, and the increase of international green trade barriers have all had a significant positive regulatory effect on the dual control of carbon emissions in free trade pilot zones. The dual control effect of carbon emissions in the free trade pilot zone exhibited significant spatial spillover characteristics, showing maximum efficiency within a radius of 550 km, resulting in a reduction of 18.30% in the total carbon emissions of surrounding cities and a decrease of 10.10% in carbon emission intensity. Heterogeneity analysis showed that regions with higher levels of institutional innovation and government function transformation had a 13.10% and 13.70% higher total carbon emission control effect compared to that of regions with lower levels and a 8.40% and 3.40% higher carbon emission intensity control effect, respectively. The total carbon emission control effect of cities along the "Belt and Road" was 9.60% higher than that of cities not along the line, and the carbon emission intensity was 8.80% higher. The total carbon emission control effect of cities that had opened the China Europe freight train was 16.80% higher than that of cities that had not opened it, and the carbon emission intensity was 7.50% higher. In terms of aligning with international economic and trade rules and achieving high-quality development, the free trade pilot zone should focus on the green and low-carbon qualification review of newly registered foreign-funded enterprises entering and the first attempt to break through technical barriers to green trade, to help carbon emission dual control process of China with high-level openness and coordination.

Allele and genotype frequencies of variants in P450 cytochromes, transports, and DNA repair enzymes in the Dominican Republic population.

Single-nucleotide variants (SNVs) give rise to important inter-individual and inter-ethnic variabilities in the metabolism and disposition of several therapeutic agents and may cause differences in the treatment response to clinically important drugs like antiarrhythmics, antidepressants, antihistamines, and antipsychotics, among others. Information about the prevalence of variants in the Dominican Republic population is still limited. The aim of this study was to describe the frequency distribution of 32 SNVs from 14 genes with pharmacogenetic interest within a sample of 150 unrelated healthy individuals. Genotype and allele frequencies were determined, and pairwise Wright's FST statistic was evaluated. Hardy-Weinberg equilibrium deviations were found in seven loci from CYP2D6 (rs16947, rs3892097, rs1058164, rs1135840, and rs28371725) and CYP2C19 (rs12769205 and rs4244285) genes. The minor allele frequencies ranged from 0.01 to 0.50 values in the xenobiotic biotransformation enzymes and transporter genes. The average admixture estimates were 51.6%, 39.5%, and 8.9% for European, African, and Amerindian ancestries, respectively. Pairwise FST analysis revealed that Dominicans displayed genetic similarity to Latin American populations, especially those with Afro-Caribbean ancestry, given the selected variants. Higher differences were identified from East and South Asians, Europeans, and Africans, in which several values above the FST threshold for moderate differentiation were identified within variants in CYP2C, CYP3A, CYP1A1, ABCB1, SLC45A2, XRCC1, and XRCC3 genes. These results should allow establishing the clinical relevance of pharmacogenetic testing in variant alleles related to drug transport and metabolism genes in this population.

A Study on Exponential Poisson Lindley Distribution, Properties, Simulations and its Application in Engineering

This study extends the continuous Poisson Lindley distribution with the aid of an existing link function, the Exp-G family. It studied the probability density function (pdf) and the cumulative distribution function (cdf) of the new Extended Poisson Lindley distribution (ExPLinD) and presents appropriate plots. Statistical Properties of the ExPLinD were studied and presented. Some of these properties include its moments, moment generating function (mgf), characteristics function and quantile function. Reliability studies was carried out and relevant charts showing the plot of the survival function and hazard functions were presented. The ExPLinD has an increasing hazard shape among others displayed in the hazard shape plots, this makes the distribution useful in reliability studies. Maximum Likelihood method was employed to estimate the parameters of ExPLinD. Simulation studies of the ExPLinD was done using different parameter values and different sample sizes. Simulation studies of ExPLinD shows that the average estimates of the parameters of ExPLinD tends to the true parameters as sample size increases, this is in order with first order asymptotic theory. The ExPLinD was fitted to an Engineering data and compared to some closely related distributions. It provided a better fit compared to the related distribution which makes it an important distribution in Statistical modeling.