Reliable Method For Estimation Research Articles

Experimental measurement of local noise power spectrum (NPS) in photon counting detector-CT (PCD-CT) using a single data acquisition.

Accurate noise power spectra (NPS) measurement in clinical X-ray CT exams is challenging due to the need for repeated scans, which expose patients to high radiation risks. A reliable method for single CT acquisition NPS estimation is thus highly desirable. To develop a method for estimating local NPS from a single photon counting detector-CT (PCD-CT)acquisition. A novel nearly statistical bias-free estimator was constructed from the raw counts data of PCD-CT scan to estimate the variance of sinogram projection data. An analytical algorithm is employed to reconstruct point-wise covariance between any two image pixel/voxel locations and . A Fourier transform is applied to obtain the desired point-wise NPS for any chosen location . The method was validated using experimental data acquired from a benchtop PCD-CT system with various physical phantoms, and the results were compared with the conventional local NPS measurement method using repeated scans and statistical ensembleaveraging. The experimental results demonstrate that (1) the proposed method can achieve pointwise/local NPS measurement for a region of interest (ROI) located at any chosen position, accurately characterizing the NPS with spatial structures resulting from image content heterogeneity; (2) the local NPS measured using the proposed method show a higher precision in the measured NPS compared to the conventional measurement method; (3) spatial averaging of the local NPS yields the conventional NPS for a given localROI. A new method was developed to enable local NPS from a single PCD-CTacquisition.

Cerebral hemodynamics and optic nerve sheath diameter acquired via neurosonology in critical patients with severe coronavirus disease: experience of a national referral hospital in Peru.

We aimed to describe the neurosonological findings related to cerebral hemodynamics acquired using transcranial Doppler and to determine the frequency of elevated ICP by optic nerve sheath diameter (ONSD) measurement in patients with severe coronavirus disease (COVID-19) hospitalized in the intensive care unit of a national referral hospital in Peru. We included a retrospective cohort of adult patients hospitalized with severe COVID-19 and acute respiratory failure within the first 7 days of mechanical ventilation under deep sedoanalgesia, with or without neuromuscular blockade who underwent ocular ultrasound and transcranial Doppler. We determine the frequency of elevated ICP by measuring the diameter of the optic nerve sheath, choosing as best cut-off value a diameter equal to or >5.8 mm. We also determine the frequency of sonographic patterns obtained by transcranial Doppler. Through insonation of the middle cerebral artery. Likewise, we evaluated the associations of clinical, mechanical ventilator, and arterial blood gas variables with ONSD ≥5.8 mm and pulsatility index (PI) ≥1.1. We also evaluated the associations of hemodynamic findings and ONSD with mortality the effect size was estimated using Poisson regression models with robust variance. This study included 142 patients. The mean age was 51.39 ± 13.3 years, and 78.9% of patients were male. Vasopressors were used in 45.1% of patients, and mean arterial pressure was 81.87 ± 10.64 mmHg. The mean partial pressure of carbon dioxide (PaCO2) was elevated (54.08 ± 16.01 mmHg). Elevated intracranial pressure was seen in 83.1% of patients, as estimated based on ONSD ≥5.8 mm. A mortality rate of 16.2% was reported. In the multivariate analysis, age was associated with elevated ONSD (risk ratio [RR] = 1.07). PaCO2 was a protective factor (RR = 0.64) in the cases of PI ≥ 1.1. In the mortality analysis, the mean velocity was a risk factor for mortality (RR = 1.15). A high rate of intracranial hypertension was reported, with ONSD measurement being the most reliable method for estimation. The increase in ICP measured by ONSD in patients with severe COVID-19 on mechanical ventilation is not associated to hypercapnia or elevated intrathoracic pressures derived from protective mechanical ventilation.

Monte Carlo simulation studies of reliability in psychometrics

Monte Carlo simulation studies are widely used in reliability research. This study reviewed 85 published Monte Carlo simulation studies investigating reliability. The review focused on the prevalence of particular reliability estimation methods and estimators, as well as adherence to previous recommendations for the Monte Carlo simulation method. It appears researchers do not fully adhere to these recommendations. Most of the reviewed studies have limitations in at least one of the following: reporting on the data generation procedure, selection of the number of replications, selection of conditions, benchmark utilization, and performance evaluation. Findings also suggest internal consistency in general and coefficient α are the most prevalent. Conversely, some reliability estimation methods and estimators that can be useful under many empirical conditions appear to be mostly overlooked. In the case of internal consistency, these are relatively obscure forms of α, λ2, λ4, μ-series, Kristof's coefficient, Feldt-Gilmer coefficient, maximal reliability, greatest lower bound to reliability, ω family, structural equation modeling-based coefficients, and internal consistency confidence intervals. Overlooked reliability estimation methods are parallel forms, test-retest, multilevel reliability, latent class-based reliability, reliability of an individual, and Bayesian reliability. Suggestions for future research have been offered.

Travel Time Reliability Estimation in Urban Road Networks: Utilization of Statistics Distribution and Tensor Decomposition

The travel time reliability (TTR) is crucial for evaluating the reliability of road networks, but real traffic data is often incomplete and sparse. This study validates that road network TTR conforms to a normal distribution and devises a quantification approach for road network TTR. Two reliability estimation methods are tailored for two data sources: section detectors and mobile detectors. Simulation experiments have confirmed the effectiveness of these methods. The study emphasizes that the TTR estimation method using traffic section data (S-TTR), which is based on the verified normal distribution assumption, maintains average absolute errors below 10%. On the other hand, the TTR estimation method that utilizes sparse trajectory data (T-TTR), which relies on tensor decomposition, proficiently fills in all missing data with an average error of 0.0059.

Comparison of Different Methods for Ancient Ship Calm Water Resistance Estimation

The remains of ancient ships from various time periods are commonly found on land and under the sea in conditions that make it difficult to reconstruct their original form and structure. For this reason, the reconstruction should be supported by other data, such as data on similar ships, but also by certain assumptions. The results of the reconstruction are significant not only in a historical sense but are of exceptional importance when building floating replicas. Two ships, Nin 1 and Nin 2, today for promotional purposes known as Condurae Croaticae, were found in Nin (Croatia) at the end of the 1960s. They are about 8 to 10 m long, and tentatively dated to the 11th century AD, although there are indications that they could be dated two centuries later. Based on archaeological finds exhibited in the Museum of Nin Antiquities, hull line drawings were created, according to which two floating replicas were made at the end of the 1990s. Considering the problem of hogging that appeared in both ships, a new proposal for the reconstruction of the original hull lines was performed based on the available documentation. The aim of this paper is a systematic analysis of its calm water resistance. Based on the established credibility of experimental testing, a scale model (1:4 ratio) of the Nin 1 vessel is constructed and evaluated through towing tank experiments. The second approach, the CFD method, is a reliable numerical method for calm resistance estimation, but it is rarely used in the analysis of ancient ships. Finally, the widely used empirical Holtrop method is also applied, but it was developed for ships of larger dimensions and with large parts of flat bottoms and, therefore, the more appropriate Delft Hull Yacht Series method is also tested. The results obtained by applying the four mentioned methods are compared and discussed.

Fitting pseudo-Sérsic (Spergel) light profiles to galaxies in interferometric data: The excellence of the uυ-plane

Modern (sub)millimeter interferometers, such as ALMA and NOEMA, offer high angular resolution and unprecedented sensitivity. This provides the possibility to characterize the morphology of the gas and dust in distant galaxies. To assess the capabilities of the current software in recovering morphologies and surface brightness profiles in interferometric observations, we tested the performance of the Spergel model for fitting in the uυ-plane, which has been recently implemented in the IRAM software GILDAS (uv_fit). Spergel profiles provide an alternative to the Sérsic profile, with the advantage of having an analytical Fourier transform, making them ideal for modeling visibilities in the uυ-plane. We provide an approximate conversion between the Spergel index and the Sérsic index, which depends on the ratio of the galaxy size to the angular resolution of the data. We show through extensive simulations that Spergel modeling in the uυ-plane is a more reliable method for parameter estimation than modeling in the image plane, as it returns parameters that are less affected by systematic biases and results in a higher effective signal-to-noise ratio. The better performance in the uυ-plane is likely driven by the difficulty of accounting for a correlated signal in interferometric images. Even in the uυ-plane, the integrated source flux needs to be at least 50 times larger than the noise per beam to enable a reasonably good measurement of a Spergel index. We characterized the performance of Spergel model fitting in detail by showing that parameter biases are generally low (<10%) and that uncertainties returned by uv_fit are reliable within a factor of two. Finally, we showcase the power of Spergel fitting by reexamining two claims of extended halos around galaxies from the literature, showing that galaxies and halos can be successfully fitted simultaneously with a single Spergel model.

Assessment of the estimation methods for the normal boiling point of liquid organic hydrogen carriers

Liquid organic hydrogen carrier (LOHC) presents a promising solution for hydrogen storage. However, its development has been impeded by the absence of normal boiling point (NBP) data for hydrogenation products, especially saturated alicyclic compounds. This gap spurs the search for a reliable and convenient estimation method to aid in the screening of new mediums and the design of chemical processes. Thanks to the intrinsic simplicity, 17 group contribution models were selected and evaluated to identify the optimal ones for different types of alicyclic compounds. The best-performing model was subsequently refined. Furthermore, two models tailored for predicting the NBP of saturated alicyclic compounds were proposed using classical approaches and artificial neural networks. The results indicate that the GCN and GM models yield lower errors in estimating the NBP of alicyclic hydrocarbons, while the GIC2 model performs better for heterocyclic compounds. The developed models are more accurate compared to the existing models. Notably, the neural-network-based model exhibits excellent performance. These models are expected to provide useful insights for large-scale screening of LOHCs for hydrogen storage.

Evaluating legal age of 18 years through observation of third molars using Gambier et al. method in an orthopantomographic sample of subadults from South India

In forensic practice, medicolegal physicians are often tasked with estimating age using dental evidence. This calls for an uncomplicated, reliable, and reproducible method for dental age estimation, enabling physicians to proceed without specific odontological expertise. Among various dental methods, third molar eruption analyses are less complicated and easier to perform. In our study, we explored the effectiveness of Gambier et al.'s scoring system, which examines the eruption of all third molars. We retrospectively analysed 1032 orthopantomograms (528 males and 504 females) of individuals aged between 15 and 24 years. The mean chronological age increased with the progression of stages (1 to 3) and phases (A to D) of the third molar eruption for both sexes. In terms of stages, none showed significant discrimination between minors (<18 years) and adults (>18 years), especially for males. However, Gambier’s phase D displayed a relatively high likelihood of being 18 years or older, with an overall 85.9 % of males and 95.7 % of females having all third molars in stage 3 being 18 years or older. While the tested method could be helpful in indicating the completion of the 18th year of life, caution is advised (due to a high percentage of false positives), and it should be used alongside other age assessment methods by experts.

A reliability estimation method based on combination of failure mechanism and ANN supported wiener processes

For some engineering application, accurately estimating reliability only depend on the history data or failure mechanism is difficult to implement, due to the lack of data and imperfect theory of failure mechanism. Namely, both history data and failure mechanism should be utilized to improve the reliability estimation accuracy for engineering applications. Hence, we construct a reliability estimation method by fusing the failure mechanism and artificial neural network (ANN) supported Wiener processes for utilizing both history data and failure mechanism. ANN and failure mechanism are integrated into Wiener process with random effects, respectively. Bayesian model averaging (BMA) method is adapted to combine the failure mechanism with ANN supported Wiener processes, as well as to update the model parameters by fusing data. Based on a typical aviation hydraulic pump's actual dataset, we illustrate the advantages of our approach by comparing to Wiener process supported only by ANN or failure mechanism in engineering practices. The proposed method shows superiorities on reliability estimation considering the estimation accuracies comparing the other two models.

Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation

Abstract. Cosmic-ray neutron sensing (CRNS) has emerged as a reliable method for soil moisture and snow estimation. However, the applicability of this method beyond research has been limited due to, among others, the use of relatively large and expensive sensors. This paper presents the tests conducted on a new scintillator-based sensor especially designed to jointly measure neutron counts, muons and total gamma rays. The neutron signal is first compared against two conventional gas-tube-based CRNS sensors at two locations. The estimated soil moisture is further assessed at four agricultural sites, based on gravimetric soil moisture collected within the sensor footprint. Muon fluxes are compared to the incoming neutron variability measured at a neutron monitoring station and total gammas counts are compared to the signal detected by a gamma ray spectrometer. The results show that the neutron dynamic detected by the new scintillator-based CRNS sensor is well in agreement with conventional CRNS sensors. The derived soil moisture also agreed well with the gravimetric soil moisture measurements. The muons and the total gamma rays simultaneously detected by the sensor show promising features to account for the incoming variability and for discriminating irrigation and precipitation events, respectively. Further experiments and analyses should be conducted, however, to better understand the accuracy and the added value of these additional data for soil moisture estimation. Overall, the new scintillator design shows to be a valid and compact alternative to conventional CRNS sensors for non-invasive soil moisture monitoring and to open the path to a wide range of applications.

A Framework for Single-Panicle Litchi Flower Counting by Regression with Multitask Learning

The number of flowers is essential for evaluating the growth status of litchi trees and enables researchers to estimate flowering rates and conduct various phenotypic studies, particularly focusing on the information of individual panicles. However, manual counting remains the primary method for quantifying flowers, and there has been insufficient emphasis on the advancement of reliable deep learning methods for estimation and their integration into research. Furthermore, the current density map-based methods are susceptible to background interference. To tackle the challenges of accurately quantifying small and dense male litchi flowers, a framework counting the flowers in panicles is proposed. Firstly, an existing effective algorithm YOLACT++ is utilized to segment individual panicles from images. Secondly, a novel algorithm FlowerNet based on density map regression is proposed to accurately count flowers in each panicle. By employing a multitask learning approach, FlowerNet effectively captures both foreground and background information, thereby overcoming interference from non-target areas during pixel-level regression tasks. It achieves a mean absolute error of 47.71 and a root mean squared error of 61.78 on the flower dataset constructed. Additionally, a regression equation is established using a dataset of inflorescences to examine the application of the algorithm for flower counting. It captures the relationship between the predicted number of flowers by FlowerNet and the manually counted number, resulting in a determination coefficient ( R 2 ) of 0.81. The proposed algorithm shows promise for automated estimation of litchi flowering quantity and can serve as a valuable reference for litchi orchard management during flowering period.

Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation

&lt;abstract&gt; &lt;p&gt;Flood time series forecasting stands a critical challenge in precise predictive models and reliable error estimation methods. A novel approach utilizing a hybrid deep learning model for both point and interval flood prediction is presented, enhanced by improved kernel density estimation (KDE) for prediction comparison and error simulation. Firstly, an optimized gated recurrent unit-time convolutional network (GRU-TCN) is constructed by tuning the internal structure of the TCN, the activation function, the L2 regularization, and the optimizer. Then, Pearson Correlation is used for feature selection, and the hyperparameters of the improved GRU-TCN are optimized by the subtraction-average-based optimizer (SABO). To further assess the prediction uncertainty, interval predictions are provided via Non-parametric KDE, with an optimized bandwidth setting for accurate error distribution simulation. Experimental comparisons are made on 5-year hydro-meteorological daily data from two stations along the Yangtze River. The proposed model surpasses long short-term memory network (LSTM), TCN, GRU, TCN-LSTM, and GRU-TCN, with a reduction of more than 13% in root mean square error (RMSE) and approximately 15% in mean absolute error (MAE), resulting in better interval estimation and error control. The improved kernel density estimation curves for the errors are closer to the mean value of the confidence intervals, better reflecting the trend of the error distribution. This research enhances the accuracy and reliability of flood predictions and improves the capacity of humans to cope with climate and environmental changes.&lt;/p&gt; &lt;/abstract&gt;

A Particular Separation Method Development and Validation of Nadolol and Bendroflumethiazide by using RP-HPLC

We have developed a simple, general, and reliable HPLC Method and validation for the simultaneous estimation of nadolol and bendroflumethiazide drugs according to (ICH) guidelines. Nadolol and bendroflumethiazide peaks have been observed at a retention time of 1.757, and 3.208 minutes, respectively, and they have kept 5 minutes as a total run time. As per linearity results, the average correlation coefficient of nadolol and bendroflumethiazide is 0.999 which indicates they have good linearity, robustness, and stability.

Understanding of Ionic Structure Change Under Hydration Levels by Using CSAFM

Proton exchange membranes (PEMs) are a key component of proton exchange membrane fuel cells. They work as gas separator, insulator, and proton conductor. Thus, characterization of the membrane is a crucial process for developing PEM. However, this work is not easy because its complicated phase separated morphological structure and widely varying ionic structure under hydration. Current sensing atomic force microscopy (CSAFM) measures nano current flow between a tip and the sample surface and can map the nano current flow distribution of scanned area. In this study, the ionic structure of Nafion under different hydration condition is studied by using CSAFM. Specifically, the area variation of ionic domain on the membrane surface under different relative humidity is characterized by quantitative method based on CSAFM measurement. The study is done by several steps. First, the Nafion 212 is scanned in the environmental chamber by varying 15% to 65% relative humidity. In this process, the current distribution and the morphology are simultaneously mapped at each RH. Second, the local net charge value of protons, which is connected with area of ionic domain on the membrane surface, are calculated based on current distribution. In this process, the area of ionic domain on the membrane surface under different RH is estimated. The study shows several remarkable results. First of all, the non-uniform ionic domain increasing is observed from sequentially mapped current distribution on the surface under different RH. Second, the reliable estimation method of ionic domain variation is introduced by this study.

Micro-CT evaluation of morphological degenerative features of sterno-clavicular joint for age-at-death estimation in forensic anthropology – A qualitative analysis

The analysis of the sternoclavicular joint holds significant importance in forensic anthropology, especially through the evaluation of medial clavicular ossification stage, which, however, is helpful only in subjects younger than 30 years of age. Given this limitation, the aim of the present work was to examine, through micro-CT, the surface of sternoclavicular joints of deceased individuals across various age groups, aiming to identify age-related degenerative changes applicable to age estimation in adults over 30 years old. A total of 41 deceased subjects were categorized into three age groups (20–39 years; 40–59 years; ≥60 years) for the analysis. Degenerative morphological features on both clavicular and sternal articular surfaces, including topography, porosity, and presence of osteophytes, were evaluated using a composite scoring system based on a modified “Falys-Prangle method.”, which assigns to each parameter specific score ranges: 1–3 for surface topography (smooth, slightly irregular, markedly irregular), 1–6 for porosity (absent, micro- and macroporosity for <50 % or >50 % of surface, surface breakdown), 1–3 for osteophytes (absent, present, not evaluable). The findings revealed a positive correlation between degenerative morphological features and age for both clavicular (rs = 0.719) and sternal articular surfaces (rs = 0.750). Additionally, the combined assessment of the clavicle and sternum, or the evaluation of the clavicle alone, exhibited better differentiation among the three age groups.The micro-CT evaluation of degenerative alterations affecting the sternoclavicular joint proves to be a reliable and efficient method for age estimation in adults.

JoulesEye

Smartphones and smartwatches have contributed significantly to fitness monitoring by providing real-time statistics, thanks to accurate tracking of physiological indices such as heart rate. However, the estimation of calories burned during exercise is inaccurate and cannot be used for medical diagnosis. In this work, we present JoulesEye, a smartphone thermal camera-based system that can accurately estimate calorie burn by monitoring respiration rate. We evaluated JoulesEye on 54 participants who performed high intensity cycling and running. The mean absolute percentage error (MAPE) of JoulesEye was 5.8%, which is significantly better than the MAPE of 37.6% observed with commercial smartwatch-based methods that only use heart rate. Finally, we show that an ultra-low-resolution thermal camera that is small enough to fit inside a watch or other wearables is sufficient for accurate calorie burn estimation. These results suggest that JoulesEye is a promising new method for accurate and reliable calorie burn estimation.

Accurate leaf area index estimation in sorghum using high-resolution UAV data and machine learning models

Accurate estimation of leaf area index (LAI) is essential for precision agriculture, yet traditional ground-based measurements are destructive, time-consuming, and limited in scale. This study aimed to address the need for rapid, non-destructive LAI monitoring over large areas by evaluating unmanned aerial vehicle (UAV) data and machine learning (ML) models. A field experiment with four irrigation treatments was conducted to obtain wide range of LAI values over two years. Multispectral and thermal UAV images were acquired throughout the growing season along with destructive LAI measurements. Five ML algorithms, including K-Nearest Neighbors (K-NN), Extra Trees Regressor (ETR), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR) were tested. Also, feature selection procedures were implemented to obtain useful information among the features used for the ML model. Results showed the K-NN model achieved the highest accuracy (R2 = 0.97, RMSE = 0.46, MAE = 0.197), followed by ETR. The analysis of feature selection revealed that the combination of Normalized Difference Vegetation Index (NDVI) and canopy height (NDVI×Hc) product had the highest importance, followed by Soil-Adjusted Vegetation Index (SAVI) and Green Normalized Difference Vegetation Index (GNDVI). Also, utilizing vegetation indices calculated from multiple spectral bands proved to be more effective than using individual bands alone. Overall, the study demonstrates that UAV data and ML techniques can estimate sorghum LAI precisely to support precision agriculture applications. Moreover, using low-cost UAVs equipped with multispectral sensors presents a cost-effective and reliable method for LAI estimation using ML models.

YENİ NESİL DENTAL YAŞ TAYİNİ YÖNTEMLERİNE GÜNCEL BİR BAKIŞ: BİR DERLEME

In forensic odontology, dental findings play a significant role in determining age and gender. Due to their anatomical structures and durability, teeth have a critical function in identification, even when exposed to mechanical, chemical, and physical effects. However, most of the estimated dental age determination methods are generally invasive, costly, and require procedures like tooth extraction. Additionally, misinterpretations can occur in 2-dimensional imaging due to distortions and magnifications. Recently, three-dimensional imaging technologies, especially Cone Beam Computed Tomography (CBCT), Computed Tomography (CT), and micro-CT, are being used to investigate the potential relationship between the volumetric data of the tooth and pulp and age. CBCT allows researchers to comprehensively evaluate pulp changes and explain the relationship between chronological age and pulp volume. With the 3D images obtained by CBCT, the volumetric measurements of the tooth and pulp can be objectively evaluated and can be used as a criterion for estimated dental age. This review aims to identify, investigate, and summarize the most reliable, practical, and accurate methods for age estimation based on current data and methods.

Ensemble deep learning enabled multi-condition generative design of aerial building machine considering uncertainties

The Aerial Building Machine (ABM) is a complex construction equipment used in high-rise building construction, facing challenges due to environmental uncertainties. This paper introduces a multi-condition generative design framework to improve ABM's visualization, operability, and intelligence. It seamlessly integrates real-time data between geometric and physical models and employs an ensemble deep learning model for objective value prediction, using a snapshot strategy. Combining structural reliability concepts with Latin hypercube sampling-based stochastic optimization, an optimal design scheme is obtained for uncertain loads. An ABM case study in China illustrates the approach's feasibility, showing it meets reliability requirements across different conditions and achieves significant improvements (16.59% under normal conditions and 16.91% under extreme wind conditions). Additionally, ensemble deep learning outperforms existing methods for ABM structural reliability estimation. Identifying optimal designs and evaluation options, this paper contributes a multi-condition optimization approach for enhanced structural reliability and establishes an efficient generative design workflow and system for exploring a vast solution space.

TC-SEPM: Characterizing soft error resilience of CNNs on Tensor Cores from program and microarchitecture perspectives

As an architectural CNN accelerator integrated into NVIDIA’s GPUs, existing research mainly focuses on improving the performance of Tensor Cores. However, the highly integrated Tensor Cores are vulnerable to transient faults (i.e., soft errors), causing catastrophic consequences in safety-critical applications like automatic driving. Thus, it is imperative to estimate the reliability of CNNs on Tensor Cores. However, obtaining a statistically significant resilience profile of CNNs on Tensor Cores with the existing fault injection (FI)-based reliability estimation methods is expensive. To this end, we build TC-SEPM to predict the error resilience of CNNs on Tensor Cores instead of FI methods. To ensure the accuracy of TC-SEPM, we first investigate resilience-related features from program and microarchitecture perspectives. Then, leveraging these heuristic features, we train machine learning models to learn the hidden relationship between error resilience and the investigated features, enabling us to predict the impact of soft errors in Tensor Cores on CNN output. Experimental results show that TC-SEPM achieves high accuracy for individual soft error resiliency prediction and overall program resilience estimation while its overhead is only 1/27 of FI methods. Additionally, TC-SEPM can provide valuable insights for programmers or architects to design more robust CNN models on Tensor Cores.