Correct Ratio Research Articles

Measuring listeners’ distraction to spatially presented masking noise conditions

This study investigates listeners' biological responses to speech under different masking noise conditions during a spatial selective attention task. Ten participants with normal hearing took part in the experiment which measured both behavioral and biological responses during a speech identification task with varying noise streams. The target speech stream was played through a front-left speaker, while masking noise, consisting of music and non-intelligent speech, was presented through either the front-right or back-center speaker. Behavioral performance was assessed based on the correct answer ratio, while biological responses were analyzed using Event-Related Potentials (ERPs) for four distinct conditions. These conditions included two Signal-to-Noise Ratios (SNRs) and two spatial positions of the masking noise stream. Using an EEG measurement headset with dry electrodes, the study collected neural responses from the participants and calculated the ERPs for each of the four conditions. After excluding one outlier, the ANOVA result revealed a difference (F(1, 3.5), p = 0.0984) in this listener group's ERP response between the two masker positions, with a lower mean peak value observed for the rear masker. This finding suggests that they were less influenced by the rear-located masking noise.

Modified Kozeny-Carman equation for estimating hydraulic conductivity in nanoscale pores of clayey soils with active surfaces

Hydraulic conductivity is a critical parameter for studying the behavior of clay-water systems. However, accurately estimating saturated hydraulic conductivity k in clayey soil by using the Kozeny-Carman (KC) equation is challenging due to the neglect of its active surface properties and nanoscale pores. Clay surfaces can have diverse characteristics resulting from various physicochemical processes such as isomorphous substitution and are typically characterized by the Cation Exchange Capacity (CEC). These properties can significantly impact fluid transport through the clay matrix. To address this issue, this study modifies the KC equation by incorporating an adsorbed water proportion φ that considers the occurrence state of pore water and its correlation with pore diameter, making it applicable to clayey soils. Molecular dynamics (MD) models are established to investigate the influence of surface interaction (CEC) and nanopore size (r) on saturated hydraulic conductivity k of seepage flow in clayey soils. Based on the MD results, a specific function is proposed to quantify the adsorbed proportion φ, which is linearly related to the root of CEC and reciprocal of pore size (r), and then incorporated into a proposed hydraulic correction ratio k* to modify the classical KC equation. The modified KC equation is validated using experimental data from literature, showing a high R2 value of 0.96. This result demonstrates that the proposed correction ratio k* can extend the application scope of the classical KC equation to clayey soils.

The efficient self-checking method for the seafloor geodetic network

The traditional check and calibration of the seafloor geodetic network mainly adopts GNSS-Acoustic technology to check seafloor control points one by one, which is both costly and time-consuming. To address this issue, two self-checking methods for the seafloor geodetic network, the Max-Gap (MG) method and the Stepwise-Forward (SF) method, are proposed based on the mutual ranging between seafloor control points. The MG method combines the free network adjustment model of the seafloor geodetic network with the θ2 criterion, and the SF method combines the adjustment model under the constraint of known points with the optimal control point combination-step forward test based on TOPSIS method. After the self-checking is completed, the stable points in the test results are taken as the constraint, the distance between control points is taken as the observation, then the recalibration of the changing points can be realized through the whole network adjustment model. The simulation experiments show that the MG method is ideal for the seafloor geodetic network with fewer control points. It can accurately find the seafloor control points with centimeter or decimeter displacement. The SF method is suitable for various types of seafloor geodetic networks. It can accurately find the centimeter-level or decimeter-level displacement points in geodetic networks with fewer or large number of control points, and when the acoustic ranging error is less than the displacement, the correct ratio of which is more than 95%. The field experiment shows that the difference between the self-calibration results based on the proposed method and that of the circle-sailing method is millimeter, indicating that the proposed methods are high-accuracy.

Synoptic fingerprints of Zonda wind from a statistical prediction model

AbstractZonda wind is a typical downslope windstorm over the eastern slopes of the Central Andes in Argentina, which produces extremely warm and dry conditions and has substantial socioeconomic impacts. In this study, we propose a new statistical model for Zonda prediction based on the “synoptic fingerprints” of atmospheric diagnostic variables from ERA5. The model combines principal component analysis (PCA) and logistic regression to establish a relationship between the observed occurrence and the PCA loading component of a predictor variable. This approach enables us to determine the probability of Zonda occurrence at selected stations and identify the synoptic structure features (fingerprints) associated with Zonda events. The obtained fields successfully discriminate between Zonda and non‐Zonda events, suggesting that the available information in the reanalysis data is sufficient for predicting the presence of Zonda. The synoptic fingerprints generated by the model reveal a cross‐barrier pressure gradient resulting from a negative geopotential height anomaly at low levels. The cross‐barrier flow remains unimpeded by the Andes, leading to forced vertical motions on the windward side, accompanied by cooling and precipitation. On the lee side, sinking motions, warming and drying are observed, further facilitated by favourable mid‐ and upper‐level conditions that establish the Zonda wind. The model performs comparably to previous research, with the best results achieved using low‐level variables as predictors. Key performance measures, including the area under the receiver operating curve (ROC) (AUC) of 0.9468 and a Brier score lower than 0.1, demonstrate the model's effectiveness. Using a 0.5 threshold, the accuracy, F1 score and correct alarm ratio (CAR) all exceed 88%, with a probability of detection (POD) higher than 90%. Studies on this type of downslope windstorm are scarce in South America, making this work a significant contribution to understanding synoptic‐scale atmospheric structures associated with Zonda occurrences.

Features of rational nutrition of athletes: analysis of university teams

Proper nutrition plays a key role in achieving successful results in physical activity. It not only provides the body with the necessary nutrients, but also helps maintain energy and restore muscles after training. One common nutritional strategy is to distribute nutrients over time. This means it's important to consume a combination of proteins, carbohydrates, and fats regardless of the time of day or workout. However, the proportions of intake of these components can affect the intensity and duration of training. Particular attention should be paid to the post-workout period, as it is considered the most important for nutrient intake and recovery of the body. At this point, the body needs nutrients to repair damaged muscles and energy stores. The correct ratio of nutrients at this time contributes to the improvement of body composition and physical performance. The article provides tips and advice on choosing the right products, taking into account the characteristics of training and individual needs of the body. Advice on nutrient and fluid intake is also provided. It is important to consume enough protein for muscle maintenance and repair, as well as carbohydrates for energy. Proper nutrition is the basis for achieving high results in physical activity, increasing efficiency and effective recovery after training. The purpose of the work is to understand the importance of nutrition in sports and the ability to correctly distribute nutrients, which is an important step towards achieving sports and physical goals.

A novel experimental design for free energy from the heat-gaining panel using multi-thermoelectric generators (TEGs) panel

Thermoelectric generators (TEGs) panel is used to produce electrical energy by converting thermal energy into electrical energy depending on the temperature differences (ΔT) between the two sides of the panel, which can generate electrical power over 24 h for different climate conditions (hot, cold, wet or dry). The TEGs panel was designed using Solidworks and the prototype of the TEGs panel was carried out in this study for practical testing and evaluation. The TEGs panel performances and the limitation, including parameters such as power generation, efficiency, response time was studied. In this study, the TEGs panel was exposed to sunlight and cubic ice to consider temperature variations throughout the day. The results showed that the TEGs panel generates electric powers of 8.04437 W and 80.40171 W during the cubic ice and sunlight tests, respectively, for temperature differences (ΔT) of 18 °C and 3.3 °C. The electric power from this test can be used to charge a small mobile phone. It was indicated that the theoretical results by the MATLAB program are closely resemble the laboratory results. Furthermore, the correction ratio for the power of MATLAB validation was 6.59%, while the correction ratio for the efficiency was 5.46%. The response time of the system was ranged from 2 to 6 min, which it is indicating the time needed for the TEGs panel to respond effectively to changes in temperatures. After incorporating the correction ratios from MATLAB simulation, the results showed that the maximum electric power and a maximum efficiency (η) are 57.44 W and 13.5%, respectively, when the temperatures difference (ΔT) reaches 70 °C. This study presents a prototype of a versatile power generator (TEGs panel) offers free energy which can be utilising in insulation of building walls and power generation at the same time.

A green QuEChERS syringe filter based micro-solid phase extraction using hydrophobic natural deep eutectic solvent as immobilized sorbent for simultaneous analysis of five anti-diabetic drugs by HPLC-UV

BackgroundHere, it has been discussed about creating a specific and sustainable analytical technique for monitoring anti-diabetic drugs in order to accurately determine the dosage in patients and reduce side effects, remove them from wastewater (as emerging contaminants), and ultimately abate pharmaceutical pollutants in the environment. ResultsIn this research, a green and reproducible Quick Easy Cheap Effective Rugged Safe (QuEChERS) method based on syringe filter based micro-solid phase extraction (SF-μSPE) coupled with HPLC-UV using a green sorbent was developed and optimized for the extraction of five anti-diabetic drugs from wastewater, serum, and plasma real samples. A novel green sorbent composed of a liquid mixture of thymol: menthol ([Thy]:[Men], 1:1) hydrophobic natural deep eutectic solvent (HNADES) and curcumin (Cur) immobilized into the non-toxic and biodegradable polyvinyl alcohol (PVA) electrospun nanofibers’ mat was synthesized simply via cheap equipment. Cur was added to enhance the hydrophobicity and functionality of the sorbent. The immobilization process was performed by soaking the mat in the liquid mixture for a specific duration. The correct synthesis and experimental molar ratio of the HNADES components were confirmed by ATR-FTIR and NMR (1H and 13C) spectroscopy. The prepared green sorbent (Cur-HNADES/PVA) was characterized using ATR-FTIR, FE-SEM, EDX/EDX mapping analysis, and water contact angle (WCA) measurement, and it exhibited satisfactory adsorption capacity for the target analytes. SignificanceUnder optimal conditions (pH = 6.0, adsorption cycle = 3, sample volume = 5.0 mL, desorption cycle = 1, type and volume of elution = 80:20 %v/v MeOH/ACN and 500.0 μL), the method was validated in terms of specificity, linear dynamic ranges (LDRs = 0.1–2000.0 μg L−1 and 0.1–1800.0 μg L−1), limits of detection (LODs = 0.03–0.09 μg L−1), and precision (within-day RSDs% = 0.32–1.45% and between-day RSDs% = 0.59–2.03%). Evaluation of the greenness aspects of the proposed method was accomplished using the Green Analytical Procedure Index (GAPI) and Analytical GREEnness (AGREE) approaches. It is noteworthy that the conducted research represents the first report of the synthesis and application of this novel and green sorbent for the determination of anti-diabetic drugs in the mentioned real samples.

The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation

Massive Online Open Course (MOOC) platforms are considered a distinctive way to deliver a modern educational experience, open to a worldwide public. However, student engagement in MOOCs is a less explored area, although it is known that MOOCs suffer from one of the highest dropout rates within learning environments in general, and in e-learning in particular. A special challenge in this area is finding early, measurable indicators of engagement. This paper tackles this issue with a unique blend of data analytics and NLP and machine learning techniques together with a solid foundation in psychological theories. Importantly, we show for the first time how Self-Determination Theory (SDT) can be mapped onto concrete features extracted from tracking student behaviour on MOOCs. We map the dimensions of Autonomy, Relatedness and Competence, leading to methods to characterise engaged and disengaged MOOC student behaviours, and exploring what triggers and promotes MOOC students’ interest and engagement. The paper further contributes by building the Engage Taxonomy, the first taxonomy of MOOC engagement tracking parameters, mapped over 4 engagement theories: SDT, Drive, ET, Process of Engagement. Moreover, we define and analyse students’ engagement tracking, with a larger than usual body of content (6 MOOC courses from two different universities with 26 runs spanning between 2013 and 2018) and students (initially around 218.235). Importantly, the paper also serves as the first large-scale evaluation of the SDT theory itself, providing a blueprint for large-scale theory evaluation. It also provides for the first-time metrics for measurable engagement in MOOCs, including specific measures for Autonomy, Relatedness and Competence; it evaluates these based on existing (and expanded) measures of success in MOOCs: Completion rate, Correct Answer ratio and Reply ratio. In addition, to further illustrate the use of the proposed SDT metrics, this study is the first to use SDT constructs extracted from the first week, to predict active and non-active students in the following week.

No-go theorem for static configurations of two charged dust species

We consider static spacetimes with no specific spacial symmetry where the matter content consists of two charged dust species. This comes motivated by the fact that static configurations are possible with one dust, but only if it is electrically counterpoised dust (ECD). In order to have such dust, the quotient between electric charge density and mass density needs to be fine-tuned to a value that is far less than the charge-mass quotient for any known particle. Here we prove that there are no static configurations with two dust species unless each one is ECD. This shows that ECD spacetimes cannot be made with matter that has on average the correct charge-mass ratio, but that the underlying particles must have such ratio.

Magnetron Sputtering Deposition of High Quality Cs3Bi2I9 Perovskite Thin Films.

Nontoxic all-inorganic perovskites are among the most promising materials for the realization of optoelectronic devices. Here, we present an innovative way to deposit lead-free, totally inorganic Cs3Bi2I9 perovskite from vapor phase. Taking use of a magnetron sputtering system equipped with a radiofrequency working mode power supply and a single target containing the correct ratio of CsI and BiI3 salts, it was possible to deposit a Cs3Bi2I9 perovskitic film on silicon and soda-lime glass. The target composition was optimized to obtain a stoichiometric deposition, and the best compromise was found with a mix enriched with 20% w/w of CsI. Secondly, the effect of post-deposition thermal treatments (150 °C and 300 °C) and of the deposition on a preheat substrate (150 °C) were evaluated by analyzing the chemical composition, the morphology, the crystal structure, and the optical properties. The thermal treatment at 150 °C improved the uniformity of the perovskite film; the one at 300 °C damaged the perovskite deposited. Depositing on a preheated substrate at 150 °C, the obtained film showed a higher crystallinity. An additional thermal treatment at 150 °C on the film deposed on the preheated substrate showed that the crystallinity remains high, and the morphology becomes more uniform.

Framework for identifying network attacks through packet inspection using machine learning

Abstract In every network, traffic anomaly detection system is an essential field of study. In the communication system, there are various protocols and intrusions. It is still a testing area to find high precision to boost the correct distribution ratio. Many authors have worked on various algorithms such as simple classification, K-Means, Genetic Algorithm, and Support Vector Machine approaches, and they presented the efficiency and accuracy of these algorithms. In this article, we have proposed a feature extraction technique known as “k-means clustering,” which has its roots in signal processing and is employed to divide a set of n observations into k clusters, each of which has its origin from the observation with the closest mean. K-Means method is applied in this study to investigate the stream and its implementation and applications using Python and the dataset on the KDDcup99. The effectiveness of the outcome indicates the planned work’s efficiency in relation to other widely available alternatives. Apart from the applied method, a web-based framework is designed, which can inspect an actual network traffic packet for identifying network attacks. Instead of using a static file for testing the network attack, a web page-based solution uses database to collect and test the information. Real-time packet inspection is provided in the proposed work for identifying new attacks.

Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction

We consider the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of an evaluation policy, pie, using a fixed dataset, D, collected by one or more policies that may be different from pie. Current OPE algorithms may produce poor OPE estimates under policy distribution shift i.e., when the probability of a particular state-action pair occurring under pie is very different from the probability of that same pair occurring in D. In this work, we propose to improve the accuracy of OPE estimators by projecting the high-dimensional state-space into a low-dimensional state-space using concepts from the state abstraction literature. Specifically, we consider marginalized importance sampling (MIS) OPE algorithms which compute state-action distribution correction ratios to produce their OPE estimate. In the original ground state-space, these ratios may have high variance which may lead to high variance OPE. However, we prove that in the lower-dimensional abstract state-space the ratios can have lower variance resulting in lower variance OPE. We then highlight the challenges that arise when estimating the abstract ratios from data, identify sufficient conditions to overcome these issues, and present a minimax optimization problem whose solution yields these abstract ratios. Finally, our empirical evaluation on difficult, high-dimensional state-space OPE tasks shows that the abstract ratios can make MIS OPE estimators achieve lower mean-squared error and more robust to hyperparameter tuning than the ground ratios.

Ultrasound Imaging with Flexible Array Transducer for Pancreatic Cancer Radiation Therapy

Pancreatic cancer with less than 10% 3-year survival rate is one of deadliest cancer types and greatly benefits from enhanced radiotherapy. Organ motion monitoring helps spare the normal tissue from high radiation and, in turn, enables the dose escalation to the target that has been shown to improve the effectiveness of RT by doubling and tripling post-RT survival rate. The flexible array transducer is a novel and promising solution to address the limitation of conventional US probes. We proposed a novel shape estimation for flexible array transducer using two sequential algorithms: (i) an optical tracking-based system that uses the optical markers coordinates attached to the probe at specific positions to estimate the array shape in real-time and (ii) a fully automatic shape optimization algorithm that automatically searches for the optimal array shape that results in the highest quality reconstructed image. We conducted phantom and in vivo experiments to evaluate the estimated array shapes and the accuracy of reconstructed US images. The proposed method reconstructed US images with low full-width-at-half-maximum (FWHM) of the point scatters, correct aspect ratio of the cyst, and high-matching score with the ground truth. Our results demonstrated that the proposed methods reconstruct high-quality ultrasound images with significantly less defocusing and distortion compared with those without any correction. Specifically, the automatic optimization method reduced the array shape estimation error to less than half-wavelength of transmitted wave, resulting in a high-quality reconstructed image.

Validation of the method for determining the egg albumin allergen in food products by enzyme-linked immunosorbent assay

An egg is an ecological food product containing protein, balanced vitamins, trace elements, and essential amino acids in the correct ratio. At the same time, egg albumin is an obligate allergen with no species specificity; consuming eggs from other birds does not eliminate the risk of an allergic reaction. The development of an allergy to chicken eggs in children, manifested by exudative diathesis, is especially likely. Allergy to egg protein leads to deterioration of the quality of life and health; therefore, its detection in food products is relevant today. The article contains information on the validation of the method of determining the egg protein content in food products by the enzyme-linked immunosorbent assay method and the main parameters for assessing the suitability of this method. The quantitative determination of processed chicken egg protein in food products was validated using the Ridascreen Egg ELISA test system, R-Biopharm, based on the State Research Institute for Laboratory Diagnostics and Veterinary-Sanitary Examination. The material for the study was negative samples of the matrix and enriched samples of CRM (certified reference material cake mix). Validation of the method for the quantitative determination of processed chicken egg protein in food products was carried out by determining and evaluating the following parameters: the limit of detection, quantification, convergence, reproducibility, accuracy, and uncertainty. The ELISA method for quantitatively determining processed chicken egg protein in food products has been successfully tested and meets the requirements of EU Commission Regulation 2002/657; all validation parameters are within acceptable limits. The method is suitable for food research and recommended for testing laboratories.

Modeling of the corporate capital structure

The capital structure is determined by the ratio of the company’s own and borrowed funds. It has a direct impact on the financial stability of the company, profit and return on equity. Thus a reasonable calculation of the value of the specified ratio is an urgent problem for any large enterprise. The correct ratio of borrowed and own capital in the structure of the aggregate corporate capital will allow to provide the specified return from the use of own capital in changing market conditions. The methodology of optimizing the structure of capital proposed by the authors is based on a formula for increasing return on equity (ROE) as a function of three variables — return on sales (ROS), resource productivity (Y) and equity multiplier M. The interpretation of the DuPont model as a model with lagged variables is original, which allows to predict the change in the equity multiplier at the expense of possible increase in other variables: resource productivity and return on sales, provided that the return on equity will reach the target level. There have been obtained formulas which allow modeling the structure of capital that guarantees a given profitability. The heuristic principle based on the three-factor model of DuPont ROE = ROS×Y×M, was that keeping the return on equity at the same level (as compared with the reference period) should entail compensation for the multidirectional dynamics of return on sales, resource productivity and equity multiplier. The paper considers three variants of the dependence of changes in the ratio of equity and borrowed capital of the company: when resource productivity is unchanged (ΔY = 0), when return on sales is unchanged (ΔROS = 0) and when both indicators change simultaneously (ΔY ≠ 0 and ΔROS ≠ 0). The methodology presented by the authors also allows to calculate the structure of capital which would provide a given level of return on equity ROE based on the forecast of return on assets ROS and return on sales Y with reference to the values of ROS and Y calculated in the reference period. We present the testing of this algorithm (in all three variants) on the basis of the reporting data of one of the metallurgical companies of Russia for the period 2017—2019.

Cross-sectional analysis characterizing the use of rank preserving structural failure time in oncology studies: changes to hazard ratio and frequency of inappropriate use

BackgroundRank preserving structural failure time (RPSFT) is a statistical method to correct or adjust for crossover in clinical trials, by estimating the counterfactual effect on overall survival (OS) when control arm patients do not receive the interventional drug when their tumor progresses. We sought to examine the strength of correlation between differences in uncorrected and corrected OS hazard ratios and percentage of crossover, and characterize instances of fundamental and sequential efficacy.MethodsIn a cross-sectional analysis (2003–2023), we reviewed oncology randomized trials that used RPSFT analysis to adjust the OS hazard ratio for patients who crossed over to an anti-cancer drug. We calculated the percentage of RPSFT studies evaluating a drug for fundamental efficacy (with or without a standard of care (SOC)) or sequential efficacy and the correlation between the OS hazard ratio difference (unadjusted and adjusted) and the percentage of crossover.ResultsAmong 65 studies, the median difference between the uncorrected and corrected OS hazard ratio was −0.1 (quartile 1, quartile 3 : −0.3 to −0.06). The median percentage of crossover was 56% (quartile 1, quartile 3: 37% to 72%). All studies were funded by the industry or had authors who were employees of the industry. Twelve studies (19%) tested a drug’s fundamental efficacy when there was no SOC; 34 studies (52%) tested a drug’s fundamental efficacy when there was already a SOC; and 19 studies (29%) tested a drug’s sequential efficacy. The correlation between the uncorrected and corrected OS hazard ratio difference and the percentage of crossover was 0.44 (95% CI: 0.21 to 0.63).ConclusionsRPSFT is a common tactic used by the industry to reinterpret trial results. Nineteen percent of RPSFT use is appropriate. We recognize that while crossover can bias OS results, the allowance and handling of crossover in trials should be limited to appropriate circumstances.

Beyond independent error assumptions in large GNN atomistic models.

The calculation of relative energy difference has significant practical applications, such as determining adsorption energy, screening for optimal catalysts with volcano plots, and calculating reaction energies. Although Density Functional Theory (DFT) is effective in calculating relative energies through systematic error cancellation, the accuracy of Graph Neural Networks (GNNs) in this regard remains uncertain. To address this, we analyzed ∼483 × 106 pairs of energy differences predicted by DFT and GNNs using the Open Catalyst 2020-Dense dataset. Our analysis revealed that GNNs exhibit a correlated error that can be reduced through subtraction, challenging the assumption of independent errors in GNN predictions and leading to more precise energy difference predictions. To assess the magnitude of error cancellation in chemically similar pairs, we introduced a new metric, the subgroup error cancellation ratio. Our findings suggest that state-of-the-art GNN models can achieve error reduction of up to 77% in these subgroups, which is comparable to the error cancellation observed with DFT. This significant error cancellation allows GNNs to achieve higher accuracy than individual energy predictions and distinguish subtle energy differences. We propose the marginal correct signratio as a metric to evaluate this performance. Additionally, our results show that the similarity in local embeddings is related to the magnitude of error cancellation, indicating the need for a proper training method that can augment the embedding similarity for chemically similar adsorbate-catalyst systems.

Formation and Characterization of MgSe Alloys by Pulsed Laser Welding Technique

AbstractHerein, MgSe alloys with different compositions are fabricated using laser welding techniques within a couple of minutes in an argon atmosphere. The optimum laser welding parameters, which reveal the correct compositional ratios, are determined by varying arc voltage, pulse width, welding frequency, and laser beam diameter. The structure of the MgSe alloy is monoclinic. Additionally, scanning electron microscopy studies have shown that MgSe alloys are least porous and have a layered structure. The electrical resistance of the welded alloys depends on the Mg content in the samples. Measurements from impedance spectroscopy have shown that the alloys can function as band filters, displaying cutoff frequency values of ≈16 GHz for AC signals propagating at a driving frequency of 1.0 GHz with an amplitude of 0.10 V. The properties of the MgSe alloys make them attractive for use in 5G/6G technology applications as band filters.

Этапная реконструктивно-пластическая хирургия при дефекте нижнего века после удаления базалиомы

Purpose. Evaluation of the effectiveness of multi-staged plastic and reconstructive rehabilitation of the patient with a large lower eyelid defect after the excision of basal cell carcinoma. Material and methods. The patient L., 75 years old, was treated with postoperative coloboma of the lower eyelid, lagophthalmos of the right eye after removal of the eyelid basal cell carcinoma, applied to the S.N. Fyodorov Eye Microsurgery Federal State Institution in Moscow. She underwent a multi-stage reconstructive plastic treatment in order to restore the anatomy and function of the lower eyelid, eliminate the cosmetic defect. Results and discussion. As a result of the first surgical stage, we were able to restore the skin defect using a free skin flap from the upper eyelid of the left eye. The second stage of treatment allowed us to restore the function of the lower eyelid by eliminating the artificial ankyloblepharone created earlier. The consequence of performing the following stages of surgery was the filling of the lack of the mucous membrane of the lower eyelid, the creation of the correct ratio and position of the cutaneous and conjunctival layers of the eyelid, the elimination of lagophthalmos and the restoration of anatomical and functional characteristics of the eyelid. Conclusion. As a result of multi-stage surgical treatment, the anatomical and functional integrity of the lower eyelid was achieved, prevented the loss of visual functions, achieved a satisfactory cosmetic effect, and also improved the patient's quality of life. Keywords: basal cell carcinoma, lower eyelid, staged plastic and reconstructive surgery

Real-time bias correction of rainfall nowcasts using biward tracking method

In the rainfall nowcasting, various error sources cause high uncertainty and bias of rainfall nowcasts. This study proposes a novel user-friendly and post-processing approach for rainfall nowcast bias correction from the perspective of hydrological users and operators of flood nowcasting system. This study first shows how the novel approach called the biward tracking (BiT) technique is applied effectively for real-time bias correction of the rainfall nowcast targeting 1-hour lead time and then verifies the applicability to the storm events occurred in Korea. The technique presented here consists of two steps, first the bias correction ratio is derived from backward tracking, which is then applied to the rainfall nowcast, searched by applying forward tracking. A simple and effective pattern correlation method is used for real-time storm tracking. As example applications, this study considers the six largest storm events that occurred in Korea from 2019 to 2021. This study uses the rainfall nowcasts generated by the MAPLE radar-based nowcasting system that contains a set of 36 nowcasts with a 10-minute interval covering the entire territory of Korea with a total of 1,050×1,050 grids with 0.5×0.5 km2 grid size, and the rain gauge data observed at over 580 stations. The derived results are then compared with those based on the conventional bias correction method. All the findings in this study confirm that the BiT-based method outperforms the conventional method. That is, the correction ratios determined by the BiT-based method are all found to be within reasonable range, without any serious outliers. Also, the bias-corrected rainfall nowcasts are found to be close to the ground observations. On the other hand, the conventional method produces many overestimated outliers, including more than 36 mm/h, even worse than the uncorrected rainfall nowcasts.