Domain Software Research Articles

Diagnosing and Characterizing Chronic Kidney Disease with Machine Learning: The Value of Clinical Patient Characteristics as Evidenced from an Open Dataset

Applying artificial intelligence (AI) and machine learning for chronic kidney disease (CKD) diagnostics and characterization has the potential to improve the standard of patient care through accurate and early detection, as well as providing a more detailed understanding of the condition. This study employed reproducible validation of AI technology with public domain software applied to CKD diagnostics on a publicly available CKD dataset acquired from 400 patients. The approach presented includes patient-specific symptomatic variables and demonstrates performance improvements associated with this approach. Our best-performing AI models, which include patient symptom variables, achieve predictive accuracies ranging from 99.4 to 100% across both hold-out and 5-fold validation with the light gradient boosting machine. We demonstrate that the exclusion of patient symptom variables reduces model performance in line with the literature on the same dataset. We also provide an unsupervised learning cluster analysis to help interpret variability among, and characterize the population of, patients with CKD.

Bioinspired Diffuse Reflectance Coatings for Fabric-Based Radiative Coolers

As global temperatures rise due to anthropogenic climate change, thermal comfort becomes an increasingly critical area of research. While most buildings are equipped to cool and heat their interiors, this process is very energy intensive; a smarter approach is to enable personal thermal management using clothing and textiles. In particular, we are interested in the ability to use textiles and clothing to cool bodies in extreme heat, both to maintain comfort and prevent deaths in urban deserts due to heat stroke. One innovative category in this realm is radiative coolers—surfaces adept at manipulating light to passively regulate temperature. These coolers achieve sub-ambient surface temperatures by selectively reflecting or refracting sunlight wavelengths (200nm-2.5µm) while emitting infrared light (8-13µm). This unique capability allows them to maintain cooler temperatures, even when exposed to direct sunlight and the open sky. Coatings facilitating this cooling action typically consist of a porous media filled with refractive gaps, requiring specific substrates and custom production processes, thereby limiting their applications. Furthermore, the necessity of low light transmission presents issues in creating fabrics that radiatively cool while still retaining high breathability and durability. In this study we present a method whereby an optically active coating comprised of a mixture of CaCO3 and BaSO4 microcrystals was applied to multiple fabric substrates to create a radiative cooler. Through the use of photoinitiated chemical vapor deposition (pICVD), a 5 µm thick layer of a hydrophilic polymer polyhydroxyethyleneacrylate (pHEA) was deposited on the fabric substrate. Subsequently, through serial immersion in solutions containing Ca and Ba ions and solutions containing CO3 and SO4 ions, inorganic microcrystals were grown directly on the surface of the fabric. These microcrystals formed with a large polydispersity, endowing the coating with excellent reflective properties in the solar spectrum. Mie scattering calculations confirmed that the CaCO3 and BaSO4 microcrystals were primarily responsible for scattering. Further simulations using finite difference time domain software revealed, surprisingly, that surface-immobilized crystals provided the highest reflection efficiency, indicating that composite fibers with embedded particles are not as efficient. Upon outdoor testing, the device showed a cooling ability of 8°C compared to an uncoated fabric, achieving a maximum cooling of 4°C below ambient temperature. Washing and durability testing of the coating found no degradation in the material's performance, affirming its resilience and long-term effectiveness.

MARIN rudder mathematical model

This paper presents the mathematical model used in MARIN’s time domain software to describe the hydrodynamic loads excited by a rudder. The paper provides a detailed description of the mathematical model and its theory, along with highlighting the novel features that have been added to extend the model to four quadrants operations. Furthermore, the paper compares the mathematical model with experiments, showcasing the model’s capabilities and shortcomings. For this comparison, a spade rudder behind the KRISO Container Ship with and without a propeller upstream has been used as a case study to illustrate how the model performs in practice.

Design and optimization of a long-range surface plasmon resonance-based plasmonic SERS biosensor

In this paper, we designed and studied a novel type of hybrid chip based on long-range surface plasmon resonance (LRSPR) and surface-enhanced Raman scattering (SERS). The substrate has periodic arrangement gold nanoring cavity arrays structure, which can obtain a uniform and stable electromagnetic field distribution. In addition, due to the clever design of the long-range configuration (a “sandwich” structure with symmetric refractive index), the chip has a significant extended electromagnetic field enhancement ability. In numerous existing studies focusing on SERS performance, the consideration of plasmonic structure is limited to its shape and size, while neglecting the significance of spacer layer parameters. Thus, we use the Finite Difference Time Domain (FDTD) software to design and optimize the chip substrate configuration by investigating the impact of various parameters such as refractive index, spacer layer thickness, plasmonic structure's inner and outer radius, and period of array on the long-range effect. The presented geometrically-tunable long-range plasmonic chip provides a way to identify the governing factors for SERS and presents a design principle for optimizing SERS substrates.

Optofluidic SERS based on Ag nanocubes with high sensitivity for detecting a prevalent water pollutant.

To enhance the integration and practical applicability of the Raman detection system, silver nanocubes (Ag NCs) were synthesized using a polyol method. A liquid-liquid interface approach was employed to transfer a monolayer of Ag NCs "film" onto a SiO2 substrate, resulting in the fabrication of a highly sensitive and uniform surface-enhanced Raman scattering (SERS) substrate denoted as "Ag NCs@SiO2." The electromagnetic field distribution of various dimers on the Ag NCs@SiO2 was analyzed using finite difference time domain (FDTD) software. The results reveal that the electromagnetic enhancement effect is most pronounced in cube-cube dimers, indicating that Ag NCs exhibit superior localized surface plasmon resonance (LSPR) response due to their well-defined geometric regularity and sharp geometric angles. For Rhodamine 6G (R6G) probe molecules, the Ag NCs@SiO2 shows ultrahigh sensitivity, with a limit of detection (LOD) of 10-12 mol/L, and the enhancement factor (EF) can reach 1.4 × 1010. The relative standard deviation (RSD) at the main characteristic peaks is below 10%, demonstrating good consistency in substrate performance. In addition, the Ag NCs@SiO2 modified with hexanethiol exhibits high sensitivity, uniformity, and repeatability in detecting for pyrene, with the LOD of 10-8 mol/L and a minimum RSD of 6.09% at the main characteristic peak, and effective recognition capabilities for pyrene and anthracene in mixed solutions. Finally, chemisorption and physisorption strategies were prepared in optofluidic channels and experimentally compared, enabling real-time detection of the pyrene solution. This method can achieve a rapid detection and precise differentiation of polycyclic aromatic hydrocarbons in a water pollutant.

Embracing the Era of Artificial Intelligence: Innovation and Preparation in Nursing Education

Rapid recent advances in information technology have opened the door for artificial intelligence (AI)-related technologies to be applied extensively across many industries. The Ministry of Education has emphasized the importance of cultivating advanced-level professionals in diverse fields, particularly in smart machinery, the Asia-Silicon Valley sector, green energy technology, biotechnology, national defense, new agricultural, and circular economy industries, to enhance innovation and promote industrial competitiveness (Kuo, 2019). While interdisciplinary talent in AI and digital innovation is being actively developed elsewhere, nursing education remains in the exploratory phase of AI and digital technology talent cultivation. Although AI is now a well-known term, the competencies required for its application in nursing remain unclear. Moreover, most nursing professionals are unfamiliar with how to best integrate AI into nursing expertise or practice settings. With the application of AI in the healthcare industry now unstoppable, it is vital to consider how to help nursing students adapt to healthcare's new technology landscape (Huang et al., 2021). AI facilitates the digital simulation of human thought patterns, logic, and behaviors with the goal of assisting human users solve problems, especially those that are time-consuming and require repetitive processing. The development of AI requires interdisciplinary collaboration among domain experts, data scientists, software engineers, robotics experts, and computer programmers. Such collaboration is essential to developing products able to meet the demands of the times and to help students become competent future nursing professionals (Murray, 2018). Nurses spend the most time interacting with patients and are thus best able to understand the perceptions and challenges of patients and their families. Collaborating with professionals from interdisciplinary fields is the best strategy for achieving optimal healthcare outcomes. However, nursing schools have yet to provide a clear response to the impact of AI on nursing education. Nursing educational institutions must enable nursing students to comprehend the concepts and principles of AI and equip them with AI literacy to allow them to unleash their potential, continuously innovate, and stay abreast with the times (Ng et al., 2021). In this issue, experts and scholars currently engaged in AI-related research in the nursing discipline share their research findings in the realms of machine learning, deep learning, emotional recognition, and natural language application. These articles offer insights into the implications of AI, suggest how nursing education may best respond to emerging AI trends, and provide the authors' perspectives on nursing education reform. The editor hopes readers will be inspired to explore new concepts, gain a deeper understanding of the application and significance of AI, and apply AI to address clinical and educational challenges to foster competent nursing professionals for tomorrow.

Simultaneous enhancements on emissions from quantum dot and quantum well by Ag nanoparticles for color conversion

Quantum dots (QDs) have been paid much attention on the color conversion for light-emitting diode (LED) in micro-display recently. However, it is hard to achieve high color conversion efficiency in a thin QD layer. In this paper, we fabricated silver nanoparticles (Ag NPs) with radii ranging mostly from 25 to 35 nm on a blue LED with a peak wavelength of 450 nm, then spin-coated QDs with a peak wavelength of 565 nm. Scanning electron microscopy, cathodoluminescence, photoluminescence (PL), and time-resolved PL measurements were performed. The PL emissions from quantum wells (QWs) of blue LED and QDs were enhanced by 10% and 32%, respectively, when the Ag NPs were included. The PL lifetimes of QWs and QDs were reduced by 10 and 6 times, respectively, compared to their initial states. Finite difference time domain software and the perturbation method were used to simulate the PL measurements and variable separation. It was concluded that the coupling of QDs and QWs with localized surface plasmon improves the external quantum efficiency and enhances the spontaneous emission rate in both QWs and QDs. This paper provides a new idea for designing high-efficiency color conversion micro-LED.

Near-ultraviolet photodetector using cerium hexaboride alloy

This article investigates a new type of heterostructure photodetector (PD) made of cerium hexaboride (CeB6) and tungsten (W) alloy deposited on top of a nitrogen-doped silicon carbide substrate. Density function theory (DFT) suggests that the alloy with 67% CeB6 and 33% W provides an optimal photo response for the PD. The optimum alloy thickness is computed numerically using particle swamp optimization (PSO) in lumerical Finite difference time domain (FDTD) software, and the alloy is fabricated using direct current (DC) and radio frequency (RF) sputtering. The performance of the PD is experimentally characterized, and our findings show a maximum photocurrent of 71.86 nA and a maximum responsivity of 94.1 µAW−1 at the near ultraviolet (UV) wavelength of 405 nm.

A Mixed‐Integer Linear Programming Framework for Optimization of Water Network Operations Problems

AbstractWater distribution systems (WDSs) are critical infrastructure used to convey water from sources to consumers. The mathematical framework governing the distribution of flows and heads in extended period simulations of WDSs lends itself to application in a wide range of optimization problems. Applying the classical mixed integer linear programming (MILP) approach to model WDSs hydraulics within an optimization framework can contribute to higher solution accuracy with lower computational effort. However, adapting WDSs models to conform to a MILP formulation has proven challenging because of the intrinsic non‐linearity of system hydraulics and the complexity associated with modeling hydraulic devices that influence the state of the WDS. This paper introduces MILPNet, an adjustable framework for WDSs that can be used to build and solve an extensive array of MILP optimization problems. MILPNet includes constraints that represent the mass balance and energy conservation equations, hydraulic devices, control rules, and status checks. To conform to MILP structure, MILPNet employs piece‐wise linear approximation and integer programming. MILPNet was implemented and tested using Gurobi Python API. Modeling accuracy was shown to be comparable to EPANET, a public domain software for hydraulic modeling, and sensitivity analyses were conducted to examine the impacts of the modeling assumptions on the performance of MILPNet. Additionally, application of the framework was demonstrated using pump scheduling optimization examples in single and rolling horizon scenarios. Our results show that MILPNet can facilitate the construction and solution of optimization problems for a range of applications in WDSs operations.

Active Participatory Regional Surveillance for Notifiable Swine Pathogens.

We evaluated an active participatory design for the regional surveillance of notifiable swine pathogens based on testing 10 samples collected by farm personnel in each participating farm. To evaluate the performance of the design, public domain software was used to simulate the introduction and spread of a pathogen among 17,521 farms in a geographic region of 1,615,246 km2. Using the simulated pathogen spread data, the probability of detecting ≥ 1 positive farms in the region was estimated as a function of the percent of participating farms (20%, 40%, 60%, 80%, 100%), farm-level detection probability (10%, 20%, 30%, 40%, 50%), and regional farm-level prevalence. At 0.1% prevalence (18 positive farms among 17,521 farms) and a farm-level detection probability of 30%, the participatory surveillance design achieved 67%, 90%, and 97% probability of detecting ≥ 1 positive farms in the region when producer participation was 20%, 40%, and 60%, respectively. The cost analysis assumed that 10 individual pig samples per farm would be pooled into 2 samples (5 pigs each) for testing. Depending on the specimen collected (serum or swab sample) and test format (nucleic acid or antibody detection), the cost per round of sampling ranged from EUR 0.017 to EUR 0.032 (USD 0.017 to USD 0.034) per pig in the region. Thus, the analysis suggested that an active regional participatory surveillance design could achieve detection at low prevalence and at a sustainable cost.

Spectrally Selective Ultra‐Broadband Solar Absorber Based on Pyramidal Structure

Here, a spectrally selective solar absorber is explored and an ultra‐broadband solar absorber is proposed based on pyramidal structure. The finite‐difference in time domain (FDTD) software is used to model the spectral characteristics and magnetic absorption patterns of this absorber. The emissivity of the absorber is less than 20% in the far‐infrared band over 6000 nm, showing good selectivity, and the total solar thermal conversion efficiency is very close to that of an ideal truncated selective solar absorber by analyzing the performance of our proposed absorber‐related indexes. By studying the high absorption band of the absorber, the selectivity can be better investigated in depth. Here, 200–4000 nm is chosed as the depth study band. The absorber possesses an ultra‐wide bandwidth of 3554 nm and an average absorption of over 97.4%, and in the 200–3754 nm band, the absorber has an ultra‐high absorption rate of more than 98.3%, and its thermal emitter has a high emission efficiency of 94% at a temperature of 1000 K. Notably, the weighted average absorption in the 280–4000 nm band at AM1.5 is as high as 98.86%, with a loss of only 1.14%. The ultra‐broadband absorption property of this solar absorber is mainly a joint effect of surface plasmon resonance coupling.

Two-photon polymerization for fabrication of metalenses for diffraction-limited focusing and high-resolution imaging

Metalenses consisting of nanoscale unit cells provide new opportunities for miniaturization, light-weight, and diversification of conventional optical lenses. Fabrication of metalenses is conventionally by photolithography technique, which is complex involving multi-steps. In this study, two-photon polymerization (TPP) was used to directly write single-layer metalenses. The metalenses was designed according to Pancharatnam Berry (P-B) phase distribution model at the wavelengths of 515, 628, and 1030 nm, respectively. The focusing performance of the designed metalenses was simulated using a Finite Difference Time Domain (FDTD) software. Near optical diffraction limit focusing of the incident light was achieved on a low refractive index photoresist. The geometrical dimensions of the fabricated unit cells were compared with the designed parameters. The influence of laser power density on dimensions of the unit and the resultant the focusing effect were investigated. The metalenses prepared by TPP with optimum parameters show a minimum full width at half maximum (FWHM) of 978 nm and a focusing efficiency of 1.02% with 628 nm light source. A line width of 980 nm was obtained under a white light source. The research results demonstrate that TPP can be a feasible and simplified approach for fabrication of metalenses, but for higher focusing efficiency and imaging resolution metalenses, some high refractive index photoresist materials being suitable for the TPP should be developed.

Left-to-right atrioventricular activation delay, a novel measure of AV-conduction, predicts clinical outcomes in non-LBBB patients

Abstract Funding Acknowledgements Type of funding sources: None. Background The PR-interval reflects atrial and atrioventricular activation. A prolonged PR has been linked to heart failure (HF), atrial fibrillation and mortality. This might at least in part be the consequence of compromised cardiac performance (e.g. left ventricular filling). On the other hand, very long PR may just be a marker of advanced heart disease. Alternative measures that account for atrial and ventricular activation might better predict adverse hemodynamic consequences of delayed atrioventricular conduction. The DEEPER-PR mechanistic pilot study suggested that various novel measures of AV conduction are associated with Doppler-derived cardiac performance. However, the clinical significance of these novel parameters is unknown. Study objective: To examine the association of novel measures of AV conduction with clinical outcomes in a HFrEF population with non-LBBB and a wide QRS (non-LBBB ICD-only MADIT-CRT subset). Methods Time intervals from anonymized scanned baseline 12-lead ECGs from the Multicenter Automatic Defibrillator Implantation Trial – Cardiac Resynchronization Therapy (MADIT-CRT), non-LBBB patients (n=537) were assessed in detail using ImageJ (public domain software provided by NIH). The primary endpoint of the study was the combined endpoint of heart failure (HF) or death events. The earliest onset of atrial activation (P wave onset), zero crossing of the P wave in lead V1, earliest onset of the QRS complex, time interval to the first peak of the R wave in V1 and V6, and QRS width were determined. The PR interval and 8 novel parameters (see figure 1) were measured and calculated accordingly. Bivariate non-parametric correlations were calculated to ascertain the associations between these covariates. In addition, Kaplan-Meier analyses by quintiles of all AV delay measures, and multivariable Cox regression analyses were performed among ICD-only patients (n=209) to determine the associated risk regarding of the combined MADIT-CRT clinical outcome of HF events or death. Results Weak correlations were found between the AV delay variables (figure 2). Four of the variables (P0PV1, P0R, PePV6, PR) were significantly associated to the risk of HF or death with the upper quintile defining a high-risk HF population, as compared to quintiles 1 to 4 (see figure 3). This indicated a threshold behavior of the associations. P0PV1 was the most powerful risk predictor (Chi-square=16.31, HR for Q5 vs. Q1-4=3.12, CI=1.80-5.41, p<0.001), followed by PRmax (Chi-square=13.19, HR for Q5 vs. Q1-4=2.81, CI=1.61-4.92, p<0.001), P0R (Chi-square=10.42, HR=2.54, CI=1.44-4.48, p=0.001), and PePV6 (Chi-square=7.96, HR for Q5 vs. Q1-4=2.2, CI=1.29-4.07, p=0.005). Conclusion This post-hoc ECG-based study identified in MADIT-CRT non-LBBB patients the left-to-right atrioventricular activation delay (P0PV1) as the most powerful risk predictor of HF or death. Prospective evaluation of this measure is warranted.

Automatic tree detection and attribute characterization using portable terrestrial lidar

Key messageThis study details a methodology to automatically detect the positions of and dasometric information about individual Eucalyptus trees from a point cloud acquired with a portable LiDAR system.Currently, the implementation of portable laser scanners (PLS) in forest inventories is being studied, since they allow for significantly reduced field-work time and costs when compared to the traditional inventory methods and other LiDAR systems. However, it has been shown that their operability and efficiency are dependent upon the species assessed, and therefore, there is a need for more research assessing different types of stands and species. Additionally, a few studies have been conducted in Eucalyptus stands, one of the tree genus that is most commonly planted around the world. In this study, a PLS system was tested in a Eucalyptus globulus stand to obtain different metrics of individual trees. An automatic methodology to obtain inventory data (individual tree positions, DBH, diameter at different heights, and height of individual trees) was developed using public domain software. The results were compared to results obtained with a static terrestrial laser scanner (TLS). The methodology was able to identify 100% of the trees present in the stand in both the PLS and TLS point clouds. For the PLS point cloud, the RMSE of the DBH obtained was 0.0716, and for the TLS point cloud, it was 0.176. The RMSE for height for the PLS point cloud was 3.415 m, while for the PLS point cloud, it was 10.712 m. This study demonstrates the applicability of PLS systems for the estimation of the metrics of individual trees in adult Eucalyptus globulus stands.

Raman enhancement properties of TiO2/Ag carbon fiber cloth using the ultraviolet-induced method: high sensitivity, flexible and reusable

We report a low-cost fabrication strategy to prepare a large-area carbon fiber cloth (CFC) coated by Ag nanoparticles (AgNPs) as a flexible surface-enhanced Raman scattering substrate. AgNPs were deposited on a hydrophilized CFC by ultraviolet (UV) irradiation of AgNO3 solution, named UV-AgNPs@CFC. The UV irradiation duration and AgNO3 solution concentration can affect the AgNPs structure. SERS property is investigated using rhodamine 6 G (R6G) and crystal violet (CV) as standard analytes and the detection concentration level is down to 10−10 mol/L. The analytical enhancement factor can reach 1.22 × 109. Also, the substrate has remarkable stability and uniformity; the relative standard deviation (RSD) of the characteristic peak calculated at 611cm−1 is 14.4%. Additionally, the UV-AgNPs@CFC substrate can detect two different molecules simultaneously. Our flexible SERS substrate enables efficient molecular extraction and Raman measurements on the curved surface of apples. The detection concentration level for CV is down to 10−5 mol/L. Moreover, the electric field distribution of the hybrid structure is simulated by the finite difference time domain and COMSOL Multiphysics software, and the maximal electric field intensity is 25.7 V/m. Meanwhile, we deposited AgNPs on the TiO2 nanorods-modified CFC (TiO2-CFC) by UV irradiation. A reusable self-cleaning UV-AgNPs@TiO2-CFC substrate was fabricated.

Logic programming for deliberative robotic task planning

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application.

Ultra-short bilayer graphene optical fiber intensity modulator based on silver-semicircular hybrid plasmonic waveguide

The intensity modulator is a key component in optical fiber communication. In this paper, an ultra-short graphene light intensity modulator based on hybrid plasmonic waveguide (HPW) array is proposed. Al2O3 embedded with double-layer graphene at a certain interval is placed on the side polishing surface of the microfiber Mach–Zehnder modulator (MZM), and the silver-semicircular array is placed on the surface of the transition material as hybrid plasmonic waveguide for the first time in all-fiber modulator. Analyzing by building a 3D model in finite difference time domain software, the hybrid surface plasmon polariton (HSPP) between silver-semicircles significantly enhances the subwavelength confinement and the light–graphene interaction. The modulation range of the real part of effective refractive index (Neff) can reach to 0.01984 by adjusting the Fermi level of graphene. Therefore, π phase change can be achieved only in a modulation length of 45 μm, and the insert loss can be controlled below 0.0159 dB/μm through the analysis of the imaginary part of the Neff, which also leads to a low total loss of 0.7155 dB. Owing to the excellent phase modulation efficiency, high extinction ratios of 39.39 dB and 47.77 dB can be reached for port 1 and port 2, respectively. Double-layer graphene forms the flat capacitor, which brings high modulation efficiency while achieving a high modulation bandwidth of 59.945 GHz. The application of HPW in graphene optical fiber modulator greatly improves the working efficiency, reduces the difficulty of integration, overcomes high insert loss, improves the extinction ratio, this modulator can be widely used in the future optical fiber communication system.

Exploration of themes based twitter data in fraud-forensic accounting studies

The rapid development of information technology is also followed by developing databases and information based on social media. Social media Twitter is one area for disclosing accounting information in tweets, retweets, and other posts. This study aims to explore fraud-forensic accounting disclosures from the viewpoint of the four largest public accounting firms in the world (The Big 4) on the Twitter database. The approach used in this research is exploratory qualitative, with the content analysis method using NVivo software R1. The data collected a target of around 1000+ tweets. Researchers logged onto Twitter (as @agunggdeagung) and searched for all tweets containing the hashtag #fraud #forensic #accounting # big4. This research produces the theme of business, knowledge, time, and reports, and the themes of accounting, fraud, and forensics, which have already been defined as keywords in disclosure on social media Twitter. This research implies that in carrying out a qualitative approach with the twitter database, elements of the domain, community (user), information posts, and powerful data-based analysis software must be fulfilled.

Machine-learning method for analyzing and predicting the number of hospitalizations of children during the fourth wave of the COVID-19 pandemic in the Lviv region.

The purpose of this paper is to develop a machine-learning model for analyzing and predicting the number of hospitalizations of children in the Lviv region during the fourth wave of the COVID-19 pandemic. This wave is characterized by dominance of a new strain of the virus-Omicron-that spreads faster than previous ones and often affects children. Their high sociability and a low level of vaccination in Ukraine resulted in a sharp increase in the number of hospitalizations. The complexity of the research is also related to the geolocation of the Lviv region. This article analyzes and predicts the number of hospitalizations of children during the fourth wave of the COVID-19 pandemic for the first time for the Lviv region. Data were obtained from publicly available resources. Public Domain Software-the Python programming language and the Pandas library-was used for software implementation of the machine-learning method: the developed model consists of two components-analysis and prediction. The analysis of the number of hospitalized children was performed using the Pearson correlation coefficient. Short- and medium-term predictions were made with the use of non-iterative SGTM neural-like structures that were taught in supervised mode and tested in online mode. The RMS and maximum ones that were reduced to the range of error values of short-term (up to a week) and medium-term (up to 2 weeks) predictions did not exceed 0.48% and 0.61% and 1.81% and 2.83%, respectively. The developed model can also be used for predicting other COVID-19 parameters.

Coupled dynamic response analysis of multi-column floating offshore wind turbine with low center of gravity

To realize the application of the floating offshore wind turbine (FOWT) from deep to relatively shallow waters, a new concept of multi-column floating wind turbine platform with low center of gravity (CG) is designed and validated. The multi-column low CG platform is designed to support a 6MW wind turbine class and operated at a water depth of 50m in the South China Sea. The frequency domain software WADAM and time domain software NREL-FAST are used to simulate coupled dynamic responses of the floating wind turbine system with second-order wave loads considering. The dynamic behaviors of multi-column low CG FOWT system under normal operation and parked conditions are presented. The influence of second-order wave force on the motion responses of the multi-column platform, fore-aft force and moment of the tower base and mooring force are researched respectively. The results demonstrate that the coupled dynamic responses at rated operating condition and extreme condition meet the normal operating requirements and extreme survival requirements of FOWT system in the shallow water (50m) of South China Sea. In addition, it is found that, the wave frequency response gradually replaces the second-order low frequency response as the main influencing factor of the coupled dynamic response of the FOWT system with the increasing severity of the sea states. However, in general, the magnitude of second-order low frequency response increases with the increasing severity of the design load case. Thus, in the subsequent design of the shallow water FOWT system, the second-order effects should be paid enough attention.