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The application of a doubly Type-Ⅱ censoring scheme, where observations are censored at both the left and right ends, is often used in various fields including social science, psychology, and economics. However, the observed sample size under this censoring scheme may not be large enough to apply a likelihood-based approach due to the occurrence of censoring at both ends. To effectively respond to this difficulty, we propose a pivotal-based approach within a doubly Type-Ⅱ censoring framework, focusing on two key aspects: Estimation for parameters of interest and prediction for missing or censored samples. The proposed approach offers two prominent advantages, compared to the likelihood-based approach. First, this approach leads to exact confidence intervals for unknown parameters. Second, it addresses prediction problems in a closed-form manner, ensuring computational efficiency. Moreover, novel algorithms using a pseudorandom sequence, which are introduced to implement the proposed approach, have remarkable scalability. The superiority and applicability of the proposed approach are substantiated in Monte Carlo simulations and real-world case analysis through a comparison with the likelihood-based approach.

In many applications, modeling based on a normal kernel is preferred because not only does the normal kernel belong to the family of stable distributions, but also it is easy to satisfy the stationary condition in the stochastic process. However, the characteristic of the data, such as count or proportion, is a major obstacle to complete modeling based on a normal distribution. To solve a limited boundary or non-normal distribution problem, we provided a novel transformation method and proposed a nonparametric Bayesian approach based on a normal kernel of the transformed variable. In particular, the provided transformation transforms any probability space into a real space and is free from the constraints of the previous transformation, such as skewness, presence of power, and bounded domains. Another advantage was that it was possible to use the Dirichlet process mixture model with full conditional posterior distributions for all parameters, leading to a fast convergence rate in the Markov chain Monte Carlo. The proposed methodology was illustrated with simulated datasets and two real datasets with non-normal distribution problems. In addition, to demonstrate the superiority of the proposed methodology, the comparison with the transformed Bernstein polynomial model was made in the real data analysis.

This paper proposes an inference approach based on a pivotal quantity under the adaptive progressive Type-II censoring scheme. To exemplify the proposed methodology, an extensively employed distribution, a Pareto distribution, is utilized. This distribution has limitations in estimating confidence intervals for unknown parameters from classical methods such as the maximum likelihood and bootstrap methods. For example, in the maximum likelihood method, the asymptotic variance-covariance matrix does not always exist. In addition, both classical methods can yield confidence intervals that do not satisfy nominal levels when a sample size is not large enough. Our approach resolves these limitations by allowing us to construct exact intervals for unknown parameters with computational simplicity. Aside from this, the proposed approach leads to closed-form estimators with properties such as unbiasedness and consistency. To verify the validity of the proposed methodology, two approaches, a Monte Carlo simulation and a real-world data analysis, are conducted. The simulation testifies to the superior performance of the proposed methodology as compared to the maximum likelihood method, and the real-world data analysis examines the applicability and scalability of the proposed methodology.

Currently, more than 30% of the fine dust generated in the Seoul metropolitan area is a pollutant emitted from automobiles such as diesel vehicles, and air pollution caused by this is becoming increasingly serious. In addition, the importance of electric vehicle distribution is increasing due to the strengthening of international environmental regulations on automobile exhaust gas and increasing the possibility of depletion of petroleum resources. This manuscript proposes a method for selecting an optimal electric vehicle charging station location in expanding charging facilities to activate electric vehicle distribution. For the sake of illustration, directions will be provided on how to select the best location for electric vehicle charging stations using data from Seoul, which has the best access. As the features, the number of living population and work force people and the number of guest facilities, which are determined to affect demand for quick charging, are considered. The missing values of the observed data are imputed based on the kriging technique from spatial correlation, and by segmenting the data through clustering, a representative technique of unsupervised learning, the characteristics of each cluster are examined and the characteristics of the clusters are identified. In addition, machine learning techniques such as the elastic net, random forest, support vector machine, and extreme gradient boosting are applied to examine the influence of the features used in predicting classes of data. In clustering analysis, the optimal number of clusters was determined to be 3 based on the heuristic and information-theoretic methods, and all the machine learning techniques considered showed that the number of work force population is the most important feature in predicting classes of data. All things considered from our results, it is reasonable to install quick electric vehicle charging stations in the places with the highest concentration of work force population and guest facility.

This paper provides an estimation method for an unknown parameter by extending weighted least-squared and pivot-based methods to the Gompertz distribution with the shape and scale parameters under the progressive Type-II censoring scheme, which induces a consistent estimator and an unbiased estimator of the scale parameter. In addition, a way to deal with a nuisance parameter is provided in the pivot-based approach. For evaluation and comparison, the Monte Carlo simulations are conducted, and real data are analyzed.

This paper proposes a new approach based on the regression framework employing a pivotal quantity to estimate unknown parameters of a Weibull distribution under the progressive Type-II censoring scheme, which provides a closed form solution for the shape parameter, unlike its maximum likelihood estimator counterpart. To resolve serious rounding errors for the exact mean and variance of the pivotal quantity, two different types of Taylor series expansion are applied, and the resulting performance is enhanced in terms of the mean square error and bias obtained through the Monte Carlo simulation. Finally, an actual application example, including a simple goodness-of-fit analysis of the actual test data based on the pivotal quantity, proves the feasibility and applicability of the proposed approach.

This paper derives the entropy of a generalized half-logistic distribution based on Type-II censored samples, obtains some entropy estimators by using Bayes estimators of an unknown parameter in the generalized half-logistic distribution based on Type-II censored samples and compares these estimators in terms of the mean squared error and the bias through Monte Carlo simulations.

The maximum likelihood method is the most widely used estimation method. On the other hand, it can produce substantial bias, and an approximate confidence interval based on the maximum likelihood estimator cannot be valid when the sample size is small. Because the sizes of the record values are considerably smaller than the original sequence observed in the majority of cases, a method appropriate for this situation is required for precise inference. This paper provides the exact confidence intervals for unknown parameters and exact predictive intervals for the future upper record values by providing some pivotal quantities in the two-parameter Rayleigh distribution based on the upper record values. Finally, the validity of the proposed inference methods was examined from Monte Carlo simulations and real data.

The body color state is an important determinant of the value of golden severum (Heros efasciatus)—a popular ornamental fish. The use of dietary supplements to improve the color development and health of this species is unexplored. Herein, the effects of marigold extract (MG) and carophyll red (CR) are examined on the growth, body color development, antioxidant properties, and innate immunity in golden severum. Fish were maintained under controlled water quality conditions (pH, temperature, and dissolved oxygen) and fed six experimental diets containing either 0% MG and CR, 1% MG, 2% MG, 5% MG, 0.5% CR, or 2% CR for five weeks. Both MG and CR significantly decreased lipid peroxide levels in hepatic tissues. In contrast, only MG enhanced the activities of reactive oxygen species (ROS)-scavenging enzymes (superoxide dismutase and catalase). Although MG and CR decreased the respiratory burst activity of splenic leukocytes, other innate immune parameters remained unchanged. Additionally, MG and CR stimulated body color development patterns in golden severum that reflect their unique coloring principles. The ROS-scavenging abilities of MG and CR appear to be related to their antioxidant activity. Hence, MG and CR at the optimal levels of 1.0% and 0.5%, respectively, can improve the body color of golden severum and protect against oxidative stress.

We developed an electro-mechanical actuator for use as a steering device for railway vehicles. The specifications for a 50 kN-class electro-mechanical actuator capable of steering control for a sharp of a railway radius curve of up to 250 m, and with Direct Drive motor (DD motor) specifications, were derived as a power source to drive it. The DD motor and an electric mechanical steering actuator equipped with it were designed and a prototype was manufactured. As a result of testing laboratory tests on motor and actuator prototypes, all performance requirements were satisfied. We conducted a field test on the actuator prototype by installing it on a railway vehicle to verify the steering angle performance. The steering action of the actuator worked well when running in curved sections, and the steering angles measured for each curve were in line with the target steering angles. The mean error between the target steering angle and the measured steering angle was only 3.4%, indicating that the wheel steering action of the developed steering system was working very well. Therefore, the developed electro-mechanical actuator is expected to be fully utilized as a steering device for railway vehicles by meeting all the performance requirements of the steering system.

Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately.

Abstract Interleukin-7 (IL-7) is a cytokine known for its importance in T cell development and survival. How IL-7 shapes CD8 T cell responses during an acute viral infection is less understood. We had previously shown that IL-7 signaling deficient mice have reduced accumulation of influenza-specific CD8 T cells following influenza infection. We sought to determine whether IL-7 affects early CD8 T cell expansion in the mediastinal lymph node and effector function in the lungs. Using IL-7Rα signaling deficient mice, we show that IL-7 is required for a normal sized mediastinal lymph node and the early clonal expansion of influenza-specific CD8 T cells therein. We show that IL-7 plays a cell-intrinsic role in the accumulation of NP366–374 and PA224–233-specific CD8 T cells in the lymph node. We also found that IL-7 shapes terminal differentiation, degranulation and cytokine production to a greater extent in PA224–233-specific than NP366–374-specific CD8 T cells. We further demonstrate that IL-7 is induced in the lung tissue by viral infection and we characterize multiple cellular sources that contribute to IL-7 production. Our findings on IL-7 and its effects on lower respiratory diseases will be important for expanding the utility of therapeutics that are currently available.

Abstract Developing an effective and efficient recycling process for marine debris (MD) is one of the most urgent issues to maintain environmental sustainability on Earth. However, restricted storage capacities and secondary pollution (e.g., microbial adhesion, putrefaction) limit the proper MD recycling. Here, we proposed a complete eco-friendly low-temperature MD pulverizing system that utilizes excessive liquefied natural gas (LNG) cold energy (LCE) in an LNG propulsion ship to improve the efficiency and effectiveness of MD recycling. The prototype design of the low-temperature pulverization (LTP) system showed that consumable refrigerant (liquid nitrogen) up to 2831 kg per hour could be substituted. Furthermore, with a 20% ship output, 1250 kg of MD could be treated with 363 kg of additional refrigerant. In addition, LTP systems utilizing LCE could increase the storage capacity by more than 10 times compared to bulk MD while minimizing the required energy consumption. To determine the feasibility of LTP for MD recycling, four types of plastics obtained from actual MD from a coastal area in Busan, Korea were classified and tested.

Renal epithelioid angiomyolipoma (EAML) is a rare variant of angiomyolipoma (AML), with a prominent epithelioid component. EAML usually presents as a large heterogeneous soft tissue lesion with intratumoral hemorrhage and variable necrosis or cystic changes. We present a case of multiloculated cystic renal EAML mimicking renal cell carcinoma in a 64-year-old female. Intracystic massive hemorrhage, hyperattenuating wall and septa on an unenhanced study, and enlarged intratumoral vessels can be helpful imaging features for distinguishing renal EAML from renal cell carcinoma.

Abstract This study was conducted to examine the physiological activity of Ulva ohnoi, some of which may be used for food or natural products but could disturbing coastal ecosystems due to large scale green-tide, to check values of U. ohnoi oil through experimental results. U. ohnoi oil was extracted from bulk of Ulva biomass to confirm its antioxidant and antibacterial activity, and the efficacy of U. ohnoi oil in the state of inflammation was confirmed through animal experiments. To confirm the anti-inflammatory effect, a mouse model induced with DSS was used. As a result of measuring NO using plasma after induction of inflammation, the amount of NO produced in the U. ohnoi oil group was decreased compared to the control group. Expression of inflammatory cytokines TNF-α, IL-6, and IL-1β was decreased compared to the control group. As a result of observing H&E staining, lower crypt loss and inflammatory cell infiltration were found in the U. ohnoi oil group compared to the control group. Consequently, U. ohnoi oil appears to have great anti-inflammatory properties.

Current industrial practices and approaches are simplified and do not describe the actual behavior of plated elements of offshore topside structures for safety design due to fires. Therefore, it is better to make up for the defective methods with integrated fire safety design methods based on fire resistance characteristics such as residual strength capacity. This study numerically investigates the residual strength of steel stiffened panels exposed to hydrocarbon jet fire. A series of nonlinear finite element analyses (FEAs) were carried out with varying probabilistic selected exposures in terms of the jet fire location, side, area, and duration. These were used to assess the effects of exposed fire on the residual strength of a steel stiffened panel on a ship-shaped offshore structure. A probabilistic approach with a feasible fire location was used to determine credible fire scenarios in association with thermal structural responses. Heat transfer analysis was performed to obtain the steel temperature, and then the residual strength was obtained for the credible fire scenarios under compressive axial loading using nonlinear FEA code. The results were used to derive closed-form expressions to predict the residual strength of steel stiffened panels with various exposure to jet fire characteristics. The results could be used to assess the sustainability of structures at risk of exposure to fire accidents in offshore installations.

Objectives: To investigate the effects of a smartphone‐based oral and whole‐body exercise programme on oral function in older adults. Methods: Individuals aged 65 years or above were randomized into three groups (non‐app use, app use and control group), and a combined oral and whole‐body exercise programme was conducted for 5 weeks. Oral muscle strength, saliva flow rate, Oral Health Impact Profile‐14 (OHIP‐14), and Geriatric Oral Health Assessment Index (GOHAI) were measured before and after the intervention. The changes in each group were analysed at the end of the programme using paired sample t‐tests, and the differences among the groups were analysed using the chi‐square test, Fisher's exact test and ANOVA. Results: The anterior tongue strength increased by 2.80 kPa after the intervention in the non‐app use group; however, the change was not statistically significant. In the app use group, the anterior tongue strength significantly increased by 4.48 kPa. The saliva flow rate increased by 0.54 and 0.71 g/min in the non‐app and app use groups, respectively, after the intervention (p < 0.05), and the change was greater in the app use group than in the other groups (p < 0.01). There were no significant changes in the posterior tongue strength, cheek strength, OHIP‐14 or GOHAI scores over the course of the study. Conclusions: A smartphone‐based combined oral and whole‐body exercise programme can improve anterior tongue strength and saliva flow rate in older participants. The programme, however, did not result in significant changes in posterior tongue strength, cheek strength and perceived oral health. [ABSTRACT FROM AUTHOR]

Introduction: Acute coronary syndrome (ACS) is one of the leading causes of death from cardiovascular diseases worldwide, with ST-segment elevation myocardial infarction (STEMI) representing a severe form of ACS that exhibits high prevalence and mortality rates. This study proposes a new method for accurately diagnosing STEMI and categorizing the infarction area in detail, based on 12-lead electrocardiogram (ECG) data using a deep learning-based artificial intelligence (AI) algorithm. Methods: Utilizing an ECG database consisting of 888 myocardial infarction (MI) patients, this study enhanced the generalization ability of the AI model through five-fold cross-validation. The developed ST-segment elevation (STE) detector accurately identified STE across all 12 leads, which is a crucial indicator for the clinical ECG diagnosis of STEMI. This detector was employed in the AI model to differentiate between STEMI and non-ST-segment elevation myocardial infarction (NSTEMI). Results: In the process of distinguishing between STEMI and NSTEMI, the average area under the receiver operating characteristic curve (AUROC) was 0.939, and the area under the precision-recall curve (AUPRC) was 0.977, demonstrating significant results. Furthermore, this detector exhibited the ability to accurately differentiate between various infarction territories in the ECG, including anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), lateral myocardial infarction (LMI), and suspected left main disease. Discussion: These results suggest that integrating clinical domains into AI technology for ECG diagnosis can play a crucial role in the rapid treatment and improved prognosis of STEMI patients. This study provides an innovative approach for the diagnosis of cardiovascular diseases and contributes to enhancing the practical applicability of AI-based diagnostic tools in clinical settings. [ABSTRACT FROM AUTHOR]

Immunoglobulin G4 (IgG4)-related disease is a systemic disease characterized by dense lymphoplasmacytic infiltrates with abundant IgG4-positive plasma cells and fibroblast proliferation. The retroperitoneal involvement of IgG4-related disease usually appears as a soft-tissue mass covering the abdominal aorta or entrapping the ureters, resulting in hydronephrosis. Here, we present a case of IgG4-related disease with retroperitoneal involvement in a 75-yearold woman with an unusual manifestation. A preoperative computed tomography (CT) scan revealed an irregular infiltrative retroperitoneal mass invading the normal anatomic barriers, raising the suspicion of malignancy or inflammation. Contrast-enhanced CT revealed a homogeneous progressive enhancement of the mass.