Variance Of Distribution Research Articles

Influence of alkali molarity on compressive strength of high-strength geopolymer concrete using machine learning techniques based on curing regimes and temperature

The compressive strength behavior of high-strength geopolymer concrete (HSGPC) has been studied in this research work with varying alkali concentration using the novel machine learning techniques. The alkali concentration in the activation solution plays a significant role in the geopolymerization process and affects the resulting compressive strength. In this research work, the range between 4 M and 16 M for alkali molarity (M), 18 kg/m3 and 160 kg/m3 for NaOH and 41 kg/m3 and 229 kg/m3 for NaSi was collected from literature and used in the various design mixes of this exercise. This was necessary because higher alkali concentrations promote a more efficient dissolution and activation of the aluminosilicate compounds, leading to increased geopolymerization and the formation of more calcium silicate hydrate (C-S-H) gel. The increased C-S-H gel content contributes to improved strength development. However, there is an optimal alkali concentration range for the sustainable production of geopolymer concrete, and exceeding this range can have a negative impact on compressive strength and ecofriendly handling of concrete. A total of fifty-three records were collected from literature and deployed in modeling the compressive strength (Fc) considering various curing regimes. Three symbolic machine learning techniques such as genetic programming (GP), evolutionary polynomial regression (EPR), and the artificial neural network (ANN) are used in this research model. The relative importance values for each input parameter were also evaluated, which indicated that all factors have significant impacts on (Fc), but Age (i.e., curing regime) has the most influence compared to FA, NaOH, and CAg then the other inputs. From the model relations between the calculated and predicted values, it can be shown that the decisive model, ANN produced line of parametric equation of y = 0.995x, and produced performance indices; MAE of 2.13 MPa, RMSE of 2.86 MPa and R-squared of 0.981, which makes the ANN the most reliable model in agreement with previous applications of the technique. These are against the poor performance of the EPR and GP, which produced R-squared less than 0.8 with higher error rates. The Taylor chart and the variance distribution, which further compares the accuracy and variances of the developed models support the outcomes. Generally, alkali molarity has shown its potential in the production of HSGPC due to its role in the reactivity phases of the concrete formulation; hydration, activation, pozzolanic, and geopolymerization reactions producing the gel needed for the strength gain in HSGPC.

Numerical study on the thermal performance of space radiator using microencapsulated phase change material slurry as working medium

Microencapsulated phase change material slurry (MPCMS) has good prospects in the field of heat transfer because of its high latent heat and stability. In this research, MPCMS is applied to a space radiator to increase its heat load and temperature stability. The thermal performance of the space radiator is numerically studied by using the two-phase Eulerian (TPE) model and a multi-scale coupling model. The effects of velocity, volume fraction, microcapsule size, and microcapsule size distribution on the thermal performance of the space radiator are studied. Results show that the temperature flat area in the tube increases with the increase in the volume fraction or flow rate of the particle phase in the MPCMS, which means the heat dissipation capacity of the space radiator is also enhanced, and the maximum increase is 14 %. Compared with the use of pure water, the temperature difference between the highest and the lowest temperature of the radiator surface can be reduced by 40 %, and the heat dissipation power can be increased by 5 %. In addition, the large size of microcapsules limits the heat transfer between the two phases, especially when the size is greater than 50 μm. Compared with uniform microcapsule size distribution, the effect of lognormal distribution of variance σ = 0.7 on heat transfer is less than 1 %.

Discussion on the relationship between the distribution characteristics of TCM syndrome types and related objective indicators in hepatolenticular degeneration.

Hepatolenticular degeneration is a rare treatable autosomal recessive inherited copper metabolism disorder with a diverse clinical phenotype and an exceptionally complex pathogenesis. Early definitive phenotypic diagnosis and targeted treatment are major challenges worldwide. In this study, we strictly followed the "National Standards of the People's Republic of China - Terminology of Traditional Chinese Medicine Clinical Diagnosis and Treatment (Syndrome Part)," "Chinese Medicine Nomenclature," and the clinical investigation-determined traditional Chinese medicine syndrome differentiation standards at Anhui University of Chinese Medicine to select 6 of the most common traditional Chinese medicine syndrome differentiations. This study retrospectively analyzed 107 patients admitted between 2019 and 2023 with Wilson's disease based on real-world data. After testing for normal distribution and homogeneity of variance, corresponding analysis of variance was selected, followed by post hoc multiple comparisons. Of the selected 25 objective influencing factors, 22 exhibited normal distribution, while red blood cells, hemoglobin, and type IV collagen did not pass the homogeneity of variance test. After analysis of variance, the factors ceruloplasmin (CP) and copper oxidase (SCO) showed significant differences among patients with different traditional Chinese medicine syndromes (P < .05), with partial η2 for CP being 0.13 > 0.06 and for SCO being 0.143 > 0.14. Post hoc multiple comparison results indicated significant differences in CP and SCO among patients with certain traditional Chinese medicine syndromes (P < .05). There were significant differences in the factors CP and SCO among patients with different traditional Chinese medicine syndromes. Significant differences were observed in the copper blue protein factor between damp-heat syndrome and liver and kidney deficiency syndrome, liver and kidney deficiency syndrome and liver and kidney yin deficiency syndrome, liver and kidney deficiency syndrome and phlegm heat and wind syndrome, as well as liver and kidney deficiency syndrome and syndrome of phlegm and blood stasis (P < .05). Significant differences were also found in the SCO factor between damp-heat syndrome and liver and kidney deficiency syndrome, liver and kidney deficiency syndrome and liver and kidney yin deficiency syndrome, liver and kidney deficiency syndrome and phlegm heat and wind syndrome, and liver and kidney deficiency syndrome and syndrome of phlegm and blood stasis (P < .05).

Employability Skills Requirements in the Professional Services Sector: An In-depth Analysis of Graduates

In today’s competitive job market, the gap between academic preparation and industry expectations has become increasingly evident, highlighting the urgency of identifying the key employability skills required by graduates. This study evaluates the essential employability skills of graduates in the professional services sector. Using a descriptive research design, a questionnaire was administered to 282 respondents in the Philippines. The sample size was determined using Slovin's formula and a stratified random sampling technique. Data analysis involved frequency percentage distribution, weighted mean, and analysis of variance (ANOVA). Findings reveal a significant proportion of respondents as team leaders, holding bachelor’s degrees, and working in professional services firms with more than 151 employees. Effective communication skills emerged as the foremost requirement, complemented by leadership skills that foster enthusiasm and commitment within organizations. Notably, disparities in employability skills were found when respondents were categorized based on organizations’ sizes. Concurrently, respondents reported challenges in articulating well-defined career objectives and demonstrating adaptability. These findings underscore the imperative for higher institutions to emphasize curricula that enhance graduates’ employability skills, particularly in communication, leadership, critical thinking, and problem-solving. Universities should tailor their academic programs to strengthen these crucial skills, benefiting both graduates and the professional services industry. Furthermore, continuous professional development and industry-academic partnerships are recommended to keep curricula aligned with evolving industry needs, ensuring that graduates are well-equipped for the dynamic job market.

The Psychological Well-being of Chinese University Students Under State Bursary

The purpose of this research is to identify, assess and provide solutions to the psychological challenges faced by university students obtaining state bursaries. The research methodology employed in this study is a mixed-methods design. This study took a two-pronged approach, presenting a questionnaire to 360 bursary-receiving university students and completing semi-structured interviews with a chosen group of ten people at Guangxi Liuzhou University. Utilizing purposive sampling, semi-structured interviews were executed with a select cohort of two financial aid personnel, two counselors, and six bursary recipients, facilitating a comprehensive comprehension of the prevailing circumstances. The data collection tools included Internet questionnaires and in-person interviews. The acquired data was then thoroughly examined utilizing statistical methods such as frequency distribution, percentage computation, and analysis of variance (ANOVA). The investigation revealed that students' psychological difficulties are consistent with the six-factor model's flaws and have multiple causes. The study suggests strategies to address these challenges, such as improving students' psychological well-being education and encouraging self-acceptance, personal growth, autonomy and interpersonal interactions.

Controlled experiments fail to capture plant phenological response to chilling temperature

AbstractAimControlled experiments are increasingly important for investigating how and to what degree plant phenology responds to global climate change. Current experiments underline that chilling and forcing temperatures are two major environmental cues shaping the budburst date of temperate species, but whether experiments could reflect the observed responses to chilling has rarely been examined.LocationEurope and North America.Time periods1951–2021.Major taxa studiedTemperate trees and shrubs.MethodsUsing an experimental database of budburst dates for 50 species derived from previous literature and observational data of the same species at 12,579 stations in Europe and 1469 stations in the USA, we compared the response of forcing requirement (FR) of the budburst date to chilling accumulation (CA) between observations and experiments using a common measure of FR and CA.ResultsThe median, variance and probability distribution of CA‐FR curves differed significantly between experiments and observations in most cases. The distinction in chilling effects between experiments and observations could be attributed to the difference in thermal space, heat stress, genetic variation among provenances, different forcing treatments adopted and plant materials used in the experiments.Main conclusionsOur results suggest that the uncertainty of phenological models based solely on the experimental data needs to be re‐evaluated when predicting future spring phenological responses across broad spatial scales.

High Spectral Resolution Lidar – generation 2 (HSRL-2) retrievals of ocean surface wind speed: methodology and evaluation

Abstract. Ocean surface wind speed (i.e., wind speed 10 m above sea level) is a critical parameter used by atmospheric models to estimate the state of the marine atmospheric boundary layer (MABL). Accurate surface wind speed measurements in diverse locations are required to improve characterization of MABL dynamics and assess how models simulate large-scale phenomena related to climate change and global weather patterns. To provide these measurements, this study introduces and evaluates a new surface wind speed data product from the NASA Langley Research Center nadir-viewing High Spectral Resolution Lidar – generation 2 (HSRL-2) using data collected as part of the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. The HSRL-2 can directly measure vertically resolved aerosol backscatter and extinction profiles without additional constraints or assumptions, enabling the instrument to accurately derive atmospheric attenuation and directly determine surface reflectance (i.e., surface backscatter). Also, the high horizontal spatial resolution of the HSRL-2 retrievals (0.5 s or ∼ 75 m along track) allows the instrument to probe the fine-scale spatial variability in surface wind speeds over time along the flight track and over breaks in broken cloud fields. A rigorous evaluation of these retrievals is performed by comparing coincident HSRL-2 and National Center for Atmospheric Research (NCAR) Airborne Vertical Atmosphere Profiling System (AVAPS) dropsonde data, owing to the joint deployment of these two instruments on the ACTIVATE King Air aircraft. These comparisons show correlations of 0.89, slopes of 1.04 and 1.17, and y intercepts of −0.13 and −1.05 m s−1 for linear and bisector regressions, respectively, and the overall accuracy is calculated to be 0.15 ± 1.80 m s−1. It is also shown that the dropsonde surface wind speed data most closely follow the HSRL-2 distribution of wave slope variance using the distribution proposed by Hu et al. (2008) rather than the ones proposed by Cox and Munk (1954) and Wu (1990) for surface wind speeds below 7 m s−1, with this category comprising most of the ACTIVATE data set. The retrievals are then evaluated separately for surface wind speeds below 7 m s−1 and between 7 and 13.3 m s−1 and show that the HSRL-2 retrieves surface wind speeds with a bias of ∼ 0.5 m s−1 and an error of ∼ 1.5 m s−1, a finding not apparent in the cumulative comparisons. Also, it is shown that the HSRL-2 retrievals are more accurate in the summer (−0.18 ± 1.52 m s−1) than in the winter (0.63 ± 2.07 m s−1), but the HSRL-2 is still able to make numerous (N=236) accurate retrievals in the winter. Overall, this study highlights the abilities and assesses the performance of the HSRL-2 surface wind speed retrievals, and it is hoped that further evaluation of these retrievals will be performed using other airborne and satellite data sets.

Correlation between newborn weight and serum BCAAs in pregnant women with diabetes

BackgroundBranched-chain amino acids (BCAAs), including leucine, isoleucine, and valine, are essential amino acids for mammals. Maternal BCAAs during pregnancy have been associated with newborn development. Meanwhile, BCAAs have been tightly linked with insulin resistance and diabetes in recent years. Diabetes in pregnancy is a common metabolic disorder. The current study aims to assess the circulating BCAA levels in pregnant women with diabetes and their relationship with neonatal development.MethodsThe serum concentrations of BCAAs and their corresponding branched-chain α-keto acids (BCKAs) catabolites in 33 pregnant women with normal glucose tolerance, 16 pregnant women with type 2 diabetes before pregnancy (PDGM), and 15 pregnant women with gestational diabetes mellitus (GDM) were determined using a liquid chromatography system coupled to a mass spectrometer. The data were tested for normal distribution and homogeneity of variance before statistical analysis. Correlations were computed with the Pearson correlation coefficient.ResultsThe maternal serum BCAAs and BCKAs levels during late pregnancy were higher in women with PGDM than those in healthy women. Meanwhile, the circulating BCAAs and BCKAs showed no significant changes in women with GDM compared with those in healthy pregnant women. Furthermore, the circulating BCAA and BCKA levels in women with PGDM were positively correlated with the weight of the newborn. The circulating leucine level in women with GDM was positively correlated with the weight of the newborn. BCAA and BCKA levels in healthy pregnant women showed no correlation with newborn weight.ConclusionsThe serum BCAAs in pregnant women with diabetes, which was elevated in PGDM but not GDM, were positively correlated with newborn weight. These findings highlight potential approaches for early identification of high-risk individuals and interventions to reduce the risk of adverse pregnancy outcomes.

Nonparametric mean and variance adaptive classification rule for high‐dimensional data with heteroscedastic variances

AbstractIn this study, we introduce an innovative methodology aimed at enhancing Fisher's Linear Discriminant Analysis (LDA) in the context of high‐dimensional data classification scenarios, specifically addressing situations where each feature exhibits distinct variances. Our approach leverages Nonparametric Maximum Likelihood Estimation (NPMLE) techniques to estimate both the mean and variance parameters. By accommodating varying variances among features, our proposed method leads to notable improvements in classification performance. In particular, unlike numerous prior studies that assume the distribution of heterogeneous variances follows a right‐skewed inverse gamma distribution, our proposed method demonstrates excellent performance even when the distribution of heterogeneous variances takes on left‐skewed, symmetric, or right‐skewed forms. We conducted a series of rigorous experiments to empirically validate the effectiveness of our approach. The results of these experiments demonstrate that our proposed methodology excels in accurately classifying high‐dimensional data characterized by heterogeneous variances.

Analysis of early changes in lymphocyte subpopulations after liver transplantation and their correlation with clinical manifestations

This study aimed to investigate the differences in peripheral blood lymphocyte subsets among patients with different immune statuses in the early postoperative period after liver transplantation, as well as the dynamic changes during the early post-transplantation period. A retrospective study was conducted, selecting a total of 82 patients who underwent liver transplantation at the General Hospital of PLA Southern Theater Command from January, 2018 to December, 2023. Based on the patients' postoperative immune status, they were categorized into stable group (n=40), infection group (n=21), and rejection group (n=21). Peripheral blood samples of 2-3 ml were collected from patients at weeks 1 to 4 postoperatively, and flow cytometry was employed to measure the absolute values of peripheral blood lymphocyte subsets. For metric data conforming to normal distribution and homogeneity of variance, multiple group comparisons were conducted using ANOVA and Bonferroni multiple comparisons; for non-normally distributed data, the Kruskal Wallis test was used. Friedman test was used to compare different time periods within 4 weeks after liver transplantation. The results showed that there were no statistically significant differences in the absolute values of lymphocyte subsets among the three groups in the first week after liver transplantation (P>0.05); however, significant differences were observed in the absolute values of lymphocyte subsets among the three groups in the second, third, and fourth weeks postoperatively (P<0.05). In the second week, the rejection group showed significantly higher absolute counts of T cells, CD4+T cells, CD8+T cells, NK cells, and B cells compared to the infection group (585.0 vs. 199.0; 324.0 vs.113.0; 188.0 vs.56.0; 57.0 vs.11.0; 145.0 vs.65.0 cells/μl), with statistically significant differences (Z=-3.972, P<0.001; Z=-3.590, P=0.001; Z=-3.978, P<0.001; Z=-3.072, P=0.006; Z=-2.472, P=0.040). In the third week, the rejection group showed significantly higher absolute counts of T cells, CD4+T cells, and CD8+T cells compared to the infection group (660.0 vs.216.0; 350.0 vs.123.0; 184.0 vs.76.0 cells/μl), with statistically significant differences (Z=-3.019, P=0.008; Z=-3.492, P=0.001; Z=-2.845, P=0.013). In the fourth week, the rejection group showed significantly higher absolute counts of T cells, CD4+T cells, CD8+T cells, and B cells compared to the infection group (690.0 vs.273.0; 405.0 vs.168.0; 214.0 vs.96.0; 117.0 vs.48.0 cells/μl), with statistically significant differences (Z=-3.379, P=0.002; Z=-3.068, P=0.006; Z=-3.007, P=0.0086; Z=-2.330, P=0.020). Within 4 weeks after liver transplantation, the absolute values of T cells, CD8+T cells, and NK cells in the fourth week were higher than those in the first week, with statistically significant differences (Z=-3.825, P=0.001; Z=-3.466, P=0.003; Z=-3.526, P=0.003); however, the absolute values of B cells showed an overall decreasing trend, and were significantly lower in the fourth week than in the first and second weeks, with statistically significant differences (Z=3.705, P=0.001; Z=2.630, P=0.009). The changes in lymphocyte subset absolute values in the rejection group were more significant than those in the infection group, with T cells, CD4+T cells, and CD8+T cells showing significant increases in the second, third, and fourth weeks postoperatively compared with the first week, with statistically significant differences (Z=-3.466, P=0.003; Z=-4.661, P<0.001; Z=-5.020, P<0.001; Z=-2.749, P=0.036; Z=-4.422, P<0.001; Z=-4.542, P<0.001; Z=-3.466, P=0.003; Z=-3.765, P=0.001; Z=-4.482, P<0.001); NK cell absolute values in the third and fourth weeks postoperatively were significantly higher than those in the first week, with statistically significant differences (Z=-2.570, P=0.061; Z=-3.765, P=0.001). In summary, monitoring the differences and dynamic changes of lymphocyte subsets in patients after liver transplantation may have certain guiding significance for evaluating the immune function status of patients and adjusting treatment plans.

Probability Distributions for Modeling Stock Market Returns—An Empirical Inquiry

Investing in stocks and shares is a common strategy to pursue potential gains while considering future financial needs, such as retirement and children’s education. Effectively managing investment risk requires thoroughly analyzing stock market returns and making informed predictions. Traditional models often utilize normal variance distributions to describe these returns. However, stock market returns often deviate from normality, exhibiting skewness, higher kurtosis, heavier tails, and a more pronounced center. This paper investigates the Laplace distribution and its generalized forms, including asymmetric Laplace, skewed Laplace, and the Kumaraswamy Laplace distribution, for modeling stock market returns. Our analysis involves a comparative study with the widely-used Variance-Gamma distribution, assessing their fit with the weekly returns of the S&amp;P 500 Index and its eleven business sectors, drawing parallel inferences from international stock market indices like IBOVESPA and KOSPI for emerging and developed economies, as well as the 20+ Years Treasury Bond ETFs and individual stocks across varied time horizons. The empirical findings indicate the superior performance of the Kumaraswamy Laplace distribution, which establishes it as a robust alternative for precise return predictions and efficient risk mitigation in investments.

Modeling the deterioration of the stiffness and of the collagen fibril distribution in a discrete model of the cornea microstructure

We present a stochastic approach to model the mechanical deterioration of the reinforcing microstructure of the human cornea. The fundamental structural micro-components of the stroma, collagen and crosslinks, are assembled deterministically into an elementary trusswork cell, multiply repeated and distorted to form a three-dimensional shell with the shape of a cornea. The spatial orientation of the collagen-like elements of each cell is thus characterized stochastically with a non correlated random angle field, obeying an assigned probability density function, leading to a non-deterministic structural stiffness. It follows that the mechanical response of the model to the action of deterministic forces equivalent to the intraocular pressure is stochastic due to the random spatial orientation of collagen fibers. The deterioration of the mechanical stiffness of the collagen components is described through a scalar variable field, evolving in space and in time, representative of a progressive damage which causes heterogeneity and asymmetric behavior. The damage variable acts in two ways on the global stiffness: (i) by reducing the stiffness of the collagen components; (ii) by modifying the dispersion coefficient of the probability density function. The equilibrium equations of the damaging model are solved at discrete time steps, with a fully explicit solution scheme, by means of the stochastic finite element improved perturbation method. The results show that when the collagen fibril stiffness reduces to 10% of the healthy value, as expected in the case of the vision-impairing condition known as keratoconus, the displacement field due to intraocular pressure is significantly affected in terms of both average and variance distributions. This effect confers a typical conical shape to the cornea. In particular, the analysis shows that high values of the response variances are confined in the keratoconus area, which agrees with a high level of uncertainties due to loss of fibril organization and thickness reduction under pathologic conditions.

Simultaneous vs staged bilateral hip arthroplasty: monocentric analysis of satisfaction rate and complications.

Interest in bilateral total hip arthroplasty (THA) has been increasing over the past decade. This study aims to compare postoperative local and systemic complications, hospital readmissions, and satisfaction assessment in patients undergoing simultaneous versus staged bilateral THA. A retrospective observational study was conducted among patients who underwent simultaneous or staged bilateral THA between 2017 and 2020. Data on perioperative parameters, local and systemic complications, and 30-day hospital readmissions were collected. Patient satisfaction was assessed using Forgotten Joint Score (FJS). The comparison of continuous variables with normal distribution of variance was performed by ANOVA; for variables with abnormal distribution, the nonparametric test Mann-Whitney U was adopted. The distribution of dichotomous variables was analyzed by chi-square test, and statistical significance was calculated by Fisher exact test. The study included 199 patients, of whom 156 underwent staged and 43 simultaneous bilateral THA. A total of 44 patients developed complications (21 systemics, 12 locals, and 20 postoperative anemia). There were no significant differences regarding local (simultaneous 5% vs. staged 6%; p = 0.999) or systemic complications (simultaneous 14% vs. staged 10%; p = 0.408). Only the incidence of postoperative anemia was significantly higher in simultaneous group compared with staged group (p = 0.003).There were no significant differences in FJS between the two groups (p = 0.258). No differences in local or systemic complications nor readmission rates were observed following simultaneous or staged bilateral THA. Simultaneous bilateral THA is non-inferior to staged implants in terms of safety and patient satisfaction.

Comparative efficacy and safety of weekly dulaglutide versus weekly insulin in type 2 diabetes: A network meta-analysis of randomized clinical trials

BackgroundAdvancements in type 2 diabetes mellitus (T2DM) therapy, notably with weekly agents like glucagon-like peptide-1 receptor agonists (GLP-RAs) such as dulaglutide, offer promising outcomes in clinical practice. The emergence of once-weekly insulin adds to this therapeutic arsenal. This research aims to explore and compare the efficacy and safety profiles of these agents in diabetes management, facilitating informed decision-making for optimizing their utilization in clinical practice. MethodsA systematic search of PubMed, Scopus, Cochrane, and Web of Science databases was conducted. The research protocol was registered at OSF registries (https://osf.io/gd67x). The primary outcome of interest was the change in hemoglobin A1C (HbA1c), with secondary outcomes including the change in fasting plasma glucose, body weight, prevalence of hypoglycemia, and treatment discontinuation due to adverse events. The evaluation of bias risk was conducted utilizing the RoB2 tool developed by the Cochrane Collaboration. Statistical analysis was performed using RStudio version 4.3.2 with the meta package version 7.0–0 and the netmeta package version 2.9–0. Confidence in network meta-analysis estimates was evaluated using the CINeMA (Confidence In Network Meta-Analysis). Heterogeneity was assessed by comparing the magnitude of the common between-study variance (τ2) for each outcome with empirical distributions of heterogeneity variances. ResultsDulaglutide 1.5 mg (mg) weekly demonstrated superior reduction in hemoglobin A1C (HbA1c) compared to insulin, with a mean difference (MD) of −0.35 (95 % CI: −0.51 to −0.19). Additionally, Dulaglutide 1.5 mg exhibited greater weight loss, with an MD of −3.12 (95 % CI: −3.55 to −2.68). However, it also showed a higher rate of adverse events, with an odds ratio (OR) of 1.40 (95 % CI: 1.12 to 1.75) compared to insulin. Both doses of Dulaglutide (1.5 mg and 0.75 mg) had lower prevalence of hypoglycemia compared to insulin, with ORs of 0.60 (95 % CI: 0.41 to 0.87) and 0.59 (95 % CI: 0.41 to 0.86), respectively. There was no significant difference in treatment discontinuation among the treatment groups. ConclusionDulaglutide, particularly at higher doses, demonstrates superior efficacy in lowering hemoglobin A1C and reducing hypoglycemia risk compared to Icodec insulin in type 2 diabetes management. However, its use is also associated with a higher incidence of adverse events. Clinicians should carefully consider these factors when selecting optimal treatment strategies for patients with type 2 diabetes mellitus.

The Impact of Bosu Training on The Development of Static and Dynamic Balance in Teenage Basketball Players

The study aimed to investigate the impacts of Bosu training on the development of static and dynamic balance in male basketball players aged 14-16 years who play actively in basketball clubs in Bingöl and train more than 7 hours weekly according to a training schedule. The study included 30 basketball players, 15 in the control group and 15 in the experimental group. The experimental group attended 30 minutes of Bosu training in addition to basketball training for 8 weeks. Anthropometric measurements, such as height, weight, BMI, and static and dynamic balance measurements, were taken before and after the training period. The data were analysed using the SPSS package. The distribution of the pre-test and post-test data was examined for each group. Normality of the data distribution and homogeneity of variances were checked using the Mauchly sphericity test and the Levene test. Then, the dependent variables within each group were compared using the paired-sample t-test. The study used a p-value of less than 0.05 to assess the significance of the results. The mean values of height, weight, and BMI of the experimental group after the 8-week Bosu training program showed no significant difference (p&gt;0.05). The analysis results regarding the Bosu training program's static and dynamic balance measurements yielded a statistically significant difference between the pre- and post-test comparisons of the experimental group for most of the static and dynamic balance variables (p&lt;0.05). However, there was no statistical difference in the control group or between groups (p&gt;0.05). It was concluded that Bosu training can positively contribute to the improvement of static and dynamic balance in young basketball players.

Modeling and theoretical exploration of free surface manifestation of submarines in presence and absence of ocean waves

Maritime surveillance using remote-sensing images is one of the emerging technologies. Because of the inaccessibility of real-time remote-sensing images, it is imperative to model them. The main intention of this study is to investigate the factors influencing the detection of submarines, irrespective of the imaging sensors. In the present work, submarine wakes (Bernoulli hump and Kelvin wake) are modeled and integrated with the sea surface to examine the key features of the submarine. Here, the sea surface is simulated by linear Airy theory using the Elfouhaily spectrum, and novel submarine position projection in imaging coordinate (SPPIC) technique is introduced to generate synthetic free surface wakes in different orientations. Besides, Computational Fluid Dynamics (CFD) technology is utilized to assess and endorse the SPPIC-based simulated wake models. The Beaufort scale is used to validate the modeled sea surface, and the root mean square error of the model is 0.314 m. Then, the simulated submarine wakes are verified by analyzing its essential features and extracting the significant parameters of submarine, such as moving velocity, heading angle, and diving depth. Further, the current research explores these significant characteristics in the context of ocean waves. The Fourier transform technique is employed to retrieve the heading angle and velocity of the submarine. The introduction of variance distribution in the Radon transform method resolves the 180⁰ ambiguity problem in heading angle calculation. Additionally, negative exponential relation is established between the diving depth of the submarine and the height of the Bernoulli hump. From this empirical equation, one can fetch the diving depth of the submarine. The discrete wavelet transform approach enhances the edges of the wedge Kelvin wake in the presence of sea waves and is demonstrated with real-time synthetic aperture radar images. Moreover, primary wake features are investigated and evaluated for different classes of submarines.

Stochastic reconstruction of geological reservoir models based on a concurrent multi-stage U-Net generative adversarial network

For complex geological reservoir modeling, some numerical-simulation-based methods, such as traditional multiple-point statistics (MPS), cannot extract nonstationary patterns of training images (TIs) effectively. CPU-intensive calculations require that variables information only be stored in RAM instead of other storage mediums to avoid huge time consumption if many simulations are performed successively. Generative Adversarial Network (GAN) modeling methods are able to overcome these issues, but when the training datasets are limited, mode collapse easily occurs. Meanwhile, fixed receptive fields might cause the problem that the corresponding reconstruction cannot take local features and global features of the TIs into account simultaneously. Therefore, we propose the Concurrent Multi-Stage U-Net GAN (Con-MSUGAN) based on a single TI. The pyramid structure is used for spatial pattern multi-scale representation in Con-MSUGAN, which can capture the corresponding feature information of TIs under different scales. Meanwhile, concurrent training patterns can make network parameters in adjacent stages correlated with each other to accelerate network convergence speed, thereby realizing stochastic multi-scale reconstructions. Stationary channel and nonstationary delta facies TIs were used to verify the performance of Con-MSUGAN. Results show that simulation of different datasets using this method are more similar in terms of spatial variance, connectivity degree and facies type frequency distribution to the original TIs. Meanwhile, muti-dimensional-scaling (MDS) plots show small discrepancies between simulation results and the corresponding TI. It indicates that Con-MSUGAN can overcome the problem that complex spatial patterns are hard to reproduce. In addition, saved network parameters can be reused. A variety of equiprobability simulation results can be obtained rapidly, thereby realizing stochastic reconstruction of geological reservoir models.

Environmental effects on genetic variance are likely to constrain adaptation in novel environments.

Adaptive plasticity allows populations to cope with environmental variation but is expected to fail as conditions become unfamiliar. In novel conditions, populations may instead rely on rapid adaptation to increase fitness and avoid extinction. Adaptation should be fastest when both plasticity and selection occur in directions of the multivariate phenotype that contain abundant genetic variation. However, tests of this prediction from field experiments are rare. Here, we quantify how additive genetic variance in a multivariate phenotype changes across an elevational gradient, and test whether plasticity and selection align with genetic variation. We do so using two closely related, but ecologically distinct, sister species of Sicilian daisy (Senecio, Asteraceae) adapted to high and low elevations on Mt. Etna. Using a quantitative genetic breeding design, we generated and then reciprocally planted c. 19,000 seeds of both species, across an elevational gradient spanning each species' native elevation, and then quantified mortality and five leaf traits of emergent seedlings. We found that genetic variance in leaf traits changed more across elevations than between species. The high-elevation species at novel lower elevations showed changes in the distribution of genetic variance among the leaf traits, which reduced the amount of genetic variance in the directions of selection and the native phenotype. By contrast, the low-elevation species mainly showed changes in the amount of genetic variance at the novel high elevation, and genetic variance was concentrated in the direction of the native phenotype. For both species, leaf trait plasticity across elevations was in a direction of the multivariate phenotype that contained a moderate amount of genetic variance. Together, these data suggest that where plasticity is adaptive, selection on genetic variance for an initially plastic response could promote adaptation. However, large environmental effects on genetic variance are likely to reduce adaptive potential in novel environments.

Wave Modulation in a Strong Tidal Current and Its Impact on Extreme Waves

Abstract Accurate estimates of extreme waves are central for maritime activities, and stochastic wave models are the best option available for practical applications. However, the way currents influence the statistics of space–time extremes in spectral wave models has not been properly assessed. Here we demonstrate impacts of the wave modulation caused by one of the world’s strongest open ocean tidal currents, which reaches speeds of at least 3 m s−1. For a bimodal swell and wind sea state, we find that most intense interactions occur when the wind sea opposes the tidal current, with an increase in significant wave height and spectral steepness up to 45% and 167%, respectively. The steepness modulation strengthens the second-order Stokes contribution for the normalized extreme crests, which increases between 5% and 14% during opposing wind sea and current. The normalized extreme wave heights have a strong dependence on the narrow-bandedness parameter, which is sensitive to the variance distribution in the bimodal spectrum, and we find an increase up to 12% with currents opposing the wind sea. In another case of swell opposing a tidal jet, we find the spectral steepness to exceed the increase predicted by a simplified modulation model. We find support in single-point observations that using tidal currents as forcing in wave models improves the representation of the expected maximum waves, but that action must be taken to close the gap of measurements in strong currents. Significance Statement The purpose of this study is to investigate how a very strong tidal current affects the surface wave field, and how it changes the stochastic extreme waves formulated for a space–time domain. Our results suggest that the expected maximum waves become more realistic when tidal currents are added as forcing in wave models. Here, the expected extremes exceed traditional model estimates, i.e., without current forcing, by more than 10%. These differences have implications for maritime operations, both in terms of planning of marine structures and for navigational purposes. However, there is a significant lack of observations in environments with such strong currents, which are needed to further verify our results.

Optimization of the parameters of a ground speed meter of low-flying aircraft based on the application of the theory of optimal filtration

The basic principle of parametric optimization according to the criterion of minimax of variance of the resulting error of estimation of the measured parameter is considered using the example of one of the main elements of an integrated measuring system – a ground speed meter. The meter consists of a Doppler ground speed sensor (location) and an accelerometer with a longitudinal axis of sensitivity (inertial). Based on the data on their measurement errors, the coefficients of the transfer functions of each channel of the meter containing these sensors were optimized. The result of the research is a comparative analysis of the resulting accuracy of the optimized meter for the invariant and non-invariant cases, based on the obtained graphs of the frequency distribution of variances. A reasonable conclusion is made about the advantage of an invariant or non-invariant meter depending on the frequency of the signal. The Matlab R2015a software was used for calculations.