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Lee, G.-S. and Cho, H.-Y., 2023. An automatic normal and abnormal wave events classification algorithm: Using continuous wave monitoring data at a fixed station. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 46-50. Charlotte (North Carolina), ISSN 0749-0208. A classification algorithm for the time-series wave data into normal and abnormal wave periods was proposed and applied. Unlike the existing traditional wave duration (persistence) analysis methods, this method divides wave height data into independent individual wave events with objective and automated criteria, so using various criteria and analyzing sensitivity is possible. This technique for detecting peak wave heights and determining the influencing period of the wave heights was applied to the KMA (Korea Meteorological Administration) wave height monitoring time series data, which is a typical type of marine environment observation data. As a result of the application, it was found that a more stable and appropriate wave event periods determination classification is possible when smoothing short-term wave height fluctuations. In addition, it was found that rather than detecting peak waves, it was found to have a sensitive effect on the almost wave-based quantitative criteria that determine the time of growth and decay of these high waves. On the other hand, the statistical characteristics of abnormal and normal wave events were found to show significant differences in mean, variance, and temporal change patterns.

Background Natural language processing (NLP) has become an increasingly significant role in advancing medicine. Rich research achievements of NLP methods and applications for medical information processing are available. It is of great significance to conduct a deep analysis to understand the recent development of NLP-empowered medical research field. However, limited study examining the research status of this field could be found. Therefore, this study aims to quantitatively assess the academic output of NLP in medical research field. Methods We conducted a bibliometric analysis on NLP-empowered medical research publications retrieved from PubMed in the period 2007–2016. The analysis focused on three aspects. Firstly, the literature distribution characteristics were obtained with a statistics analysis method. Secondly, a network analysis method was used to reveal scientific collaboration relations. Finally, thematic discovery and evolution was reflected using an affinity propagation clustering method. Results There were 1405 NLP-empowered medical research publications published during the 10 years with an average annual growth rate of 18.39%. 10 most productive publication sources together contributed more than 50% of the total publications. The USA had the highest number of publications. A moderately significant correlation between country’s publications and GDP per capita was revealed. Denny, Joshua C was the most productive author. Mayo Clinic was the most productive affiliation. The annual co-affiliation and co-country rates reached 64.04% and 15.79% in 2016, respectively. 10 main great thematic areas were identified including Computational biology , Terminology mining , Information extraction , Text classification , Social medium as data source , Information retrieval , etc. Conclusions A bibliometric analysis of NLP-empowered medical research publications for uncovering the recent research status is presented. The results can assist relevant researchers, especially newcomers in understanding the research development systematically, seeking scientific cooperation partners, optimizing research topic choices and monitoring new scientific or technological activities.

An urgent task in creating and using composite materials is the assessment and prediction of their performance properties and reliability. Currently, when studying the reliability of the materials, there is little experimental data, mathematical descriptions, and models for both probabilistic and deterministic methods to assess reliability. Based on the obtained experimental data, this article discusses the development of a methodology for predicting reliability. The article also proposes a statistical model for assessing reliability by the criterion of the structural strength of products made of polymer composite materials. The characteristics of the reliability changes in the materials when in operation are presented. The calculation allowed obtaining graphs showing the dispersion and statistical variability of the characteristics of polypropylene-based polymeric materials at the design, production, and operation stages of the product life cycle. The computational experimental results for determining the influence of the shape of inclusions and mass on the mechanical properties of a polymer composite material aimed at improving the strength characteristics of the products are presented. Based on a computational experiment in the MSC Digimat MF nonlinear solver, equations are provided to demonstrate the regression dependence of the strength of a part made of a polymer composite material on technological factors.

Currently, three-dimensional (3D) laser-scanned point clouds have been broadly applied in many important fields, such as non-contact measurements and reverse engineering. However, it is a huge challenge to efficiently and precisely extract the boundary features of unorganized point cloud data with strong randomness and distinct uncertainty. Therefore, a novel type of boundary extraction method will be developed based on concurrent Delaunay triangular meshes (CDTMs), which adds the vertex-angles of all CDTMs around a common data point together as an evaluation index to judge whether this targeted point will appear at boundary regions. Based on the statistical analyses on the CDTM numbers of every data point, another new type of CDTM-based boundary extraction method will be further improved by filtering out most of potential non-edge points in advance. Then these two CDTM-based methods and popular α-shape method will be employed in conducting boundary extractions on several point cloud datasets for comparatively analyzing and discussing their extraction accuracies and time consumptions in detail. Finally, all obtained results can strongly demonstrate that both these two CDTM-based methods present superior accuracies and strong robustness in extracting the boundary features of various unorganized point clouds, but the statistically improved version can greatly reduce time consumption.

Using a clustering algorithm based on cloud‐to‐ground (CG) lighting data, 72,974 thunderstorms were identified and tracked in the middle reaches of the Yangtze River Basin from May to September of 2016–2020. Thunderstorms predominantly occur in the southeast region and move to the northeast at a speed of 16–64 km/hr. Most thunderstorms have short durations (98.3%, ≤3 hr) and low CG flash frequencies (90.0%, ≤64). Thunderstorms with longer durations are mainly triggered near the mountains and tend to occur (end) earlier (later) in the afternoon (evening). The peak composite reflectivity (CR) corresponding to CG flashes from all thunderstorms is 50 dBZ. Approximately 70% (20%) of CG flashes occur in convective (stratiform) areas. The first CG flash of a thunderstorm tends to occur in convective areas with a higher CR than that of the last CG flash. The average and maximum CRs of CG flashes increase significantly with thunderstorm duration. Plain Language Summary Thunderstorms are known as a type of weather system that is typically accompanied by the presence of lighting and other hazardous weather (high winds, heavy rain, hail and tornadoes). Cloud‐to‐ground (CG) lighting produced by thunderstorms is a highly dangerous weather phenomenon that occurs between a thundercloud and the ground and often causes wildfires, explosions and severe damage to buildings. The middle reaches of the Yangtze River Basin in China are a transition zone between plateaus and plains, with dense urban agglomerations, rivers and lakes. However, thunderstorm activity in such complex underlying surfaces is poorly understood. Based on ground‐based radar and lightning observations, the statistical characteristics of thunderstorm activity in this region during the warm seasons (May to September) of 2016–2020 are analyzed using a lightning clustering method. The CG lighting number, area and displacement of thunderstorms increase with thunderstorm duration. Thunderstorms that last longer are mostly triggered near the mountains and often start earlier in the afternoon and end later in the evening. In addition, CG lighting produced by thunderstorms is associated with high radar echo intensity. These findings are useful for improving the nowcasting of lightning and other hazardous weather caused by thunderstorms. Key Points The cloud‐to‐ground (CG) flash number, area, displacement, etc., of thunderstorms based on lightning data change with increasing thunderstorm duration Thunderstorms with longer durations, mostly triggered near the mountains, occur earlier in the afternoon and end later in the evening Radar echo characteristics of CG flashes from thunderstorms with different durations show certain regularities

The raindrop size distribution (DSD) reflects the size distribution of raindrop particles and is of great significance for studying the microphysical processes of precipitation and improving the accuracy of quantitative precipitation estimation. In this study, eight years of disdrometer data were used to analyse the characteristics of the DSD at different geographical locations (the island station ISL and the inland plain station INL) in eastern China to explore its geographical distribution characteristics. The results show that the peak values of N(D) at the ISL and INL stations appear at 0.56 mm and 0.69 mm in diameter, respectively. The ISL station has a higher N(D) for small particles and a lower N(D) for large particles. In terms of convective precipitation, the ISL station tends to be more maritime-like, while the INL station is more continental-like. In addition, the μ-Λ relationships obtained in this study indicate that for the same Λ, the μ value at the ISL station is less than that at the INL station. The diameters of most particles at the two stations are between 1 and 2 mm, and the range of μ values at the ISL station is larger. For stratiform (convective) precipitation, the Z-R relationships at the ISL and INL stations are Z = 255.2R 1.515 (Z = 133.7R 1.641 ) and Z = 298.8R 1.545 (Z = 206.4R 1.551 ), respectively, indicating that the Z-R relationships mainly depend on the precipitation type rather than the region. In INL, the collisional breakup process is more prominent, with approximately 17% of DSDs reaching EDSD, compared to only 10% at ISL.

The findings of the analysis of data on the accident rate of power transformers indicate that one of the main causes of their failures is a decrease in the dielectric strength of the insulation. To reduce failures and extend the service life of power transformers in operation, the issue of enhancing the techniques for assessing the condition of their internal insulation becomes relevant. Currently, when selecting the major insulation of transformers, one takes into account the dependency of the dielectric strength of the oil passage on its width. Experts discuss the issues involved in the choice of major insulation while taking into account the effect of the generalized factor being the volume of the oil passage. The solution to that problem largely depends on the study of the statistical characteristics of the dielectric strength of oil passages of different volumes and the effect rated parameters of transformers have on them. The efficiency of the application of such diagnostic characteristics depends on the extent of studies available on them and the establishment of their standardized parameters. The paper proposes a method for estimating the change in the transformer oil volume in stressed oil passages of major insulation of high-voltage power transformers and statistical characteristics of the dielectric strength of these passages while taking into account the effect of the rated values of capacity and voltage of transformers. It is shown that the degree of effect of transformer technical parameters on the statistical characteristics of the dielectric strength of oil passages depends on the quality of transformer oil, which undergoes a change in operating conditions.

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.

The article deals with the test of the main hypothesis about regional climate warming based on the analysis of unique continuous long-term observations of air temperature in 1879-2018 at V.A. Mikhelson meteorological observatory. The authors present annual and seasonal trends of air temperature for 140 years, which indicate its increase practically during the entire observation period. All considered statistical series can be characterized by the normal distribution of random variables. The cyclical nature of changes in air temperature for all series relative to their long-term average values and a period of a clear significant increase in temperature, which falls on the last three decades of both annual values and seasonal time intervals, have been revealed. Statistical criteria determined a clearly heterogeneous pattern of this period in relation to both the previous observation years and the entire 140-year period; in particular, its average air temperature is quite higher, which proves the warming of the region's climate over the past decades. It has been noted that the degree of air temperature rise in winter is higher than in summer. Positive changes in the elements of the heat balance, both during the growing season and throughout the year, in particular, the improvement of the conditions for overwintering agricultural crops, predetermines the need for research in the possible expansion of their varieties for cultivation in the Moscow region. Based on a comprehensive analysis and logical conclusions, we made a hypothesis about the influence of intensive development of heated buildings around the meteorological station on the air temperature rise in the last half century; however, it is impossible to measure such an influence today, as well as the influence of global warming due to other factors.

Ukraine experienced expansionism by multiple foreign groups and states throughout its history. However, Russian colonization of the 18th and 19th centuries and Soviet colonization of 1921–1991 had the most significant impact on Ukrainian national state formation and cultural identification through organized Russian supremacy, systemic totalitarian ideology, and methodical Ukrainian language suppression in public spaces. This paper focuses on developing data management tools to identify the representation of Russian colonial impact in Ukrainian public art through analysis of Ukrainian monuments included in the State Monuments’ Rosters for the Sumy region, a territory bordering Russia. The study utilizes digital data collection apparatus and data analysis to evaluate the distribution of monuments as the primary descriptive research technique. A Monument Digital Identification Card was created to indicate that the Russian colonial impact in the Sumy region, represented in monuments through identified criteria such as epoch, political narratives, personalities, and texts’ language, is higher than Ukrainian statehood narratives. The developed data management tools compare the degree of colonial impact in public art objects installed and included in the State Monuments’ Rosters before 1991 and after Ukraine gained independence. An increase in totalitarian propaganda in 1921–1991 directly impacted the number and narratives of monuments, with 94.9% reflecting colonial narratives and only 5.1% representing national identity. At the same time, no increase in Ukrainian national narratives has been identified since Ukrainian independence in 1991. The research findings confirm the need for state regulatory reforms and regional authorities’ amendments to achieve historical justice. AcknowledgmentThis research is financially supported by the Ukrainian Scientific Research Foundation within the framework of the project “Digital archiving of monuments as objects of public memorialization for the preservation of the cultural heritage of Ukraine.”