Height Levels Research Articles

Investigation of Digital Game Playing Time and Motivation of Sport Sciences Students

The aim of this study was to investigate the time spent playing digital games and the motivation of Sports Sciences students. A total of 192 students (122 males, 70 females) who are studying at the Faculty of Physical Education of Ondokuz Mayıs University Yasar Dogu took part in the study. The data collection instruments used in this study were the digital game playing motivation scale and the digital game playing attitude scale. For data analysis, SPSS 21 package was used. Independent samples t-test and ANOVA tests were used for data evaluation. Significance was set at 0.05 to compare all variables. There was no significant difference in attitude and motivation to play digital games according to gender, class, height, age and father's educational level among the students of Sports Sciences (p > 0.05). A significant difference was found between the behavioural sub-dimensions of attitude towards digital games and the curiosity and social acceptance sub-dimensions of motivation towards digital games according to the level of education of the mothers (p<0.05). As a result, the curiosity-social acceptance sub-dimensions of the digital game playing motivation scale increase as the level of parental education increases, according to the data obtained from the parental education variable. It can be said that students are influenced by extrinsic sources of motivation in line with their desires and needs in the direction of the target behaviour, as the level of curiosity-social acceptance increases.

Biomechanical effect of dual-dimensional archwire on controlled movement of anterior teeth compared with rectangular archwire: A finite element study

This study aimed to determine and compare the effectiveness of the use of the dual-dimensional archwire and conventional rectangular archwire on tooth movement patterns when combined with various lengths of power arms. Displacements of the maxillary central incisor and the deformation of the wire section were calculated when applying retraction forces from different lengths of power arms using the finite element method. Torque control of the incisor could be carried out more effectively when using the dual-dimensional archwire combined with long power arms than with the rectangular archwire. The use of the dual-dimensional archwire produced bodily movement of the central incisor at height levels of the power arm between 8 and 10 mm and lingual root tipping at the level of 10 mm. The use of the dual-dimensional archwire provided better-controlled movement of the incisor, including bodily movement or root movement, than the rectangular archwire.

Photogrammetric Investigation of Storm-Induced Erosion Process on Sandy Beach Profile in Medium-Scale Flume

In this study, laboratory experiments were conducted to investigate the influence of changes in storm wave height and water level on beach response in a medium-scale wave flume. A schematic storm was simulated (rising, apex, and waning phases). A non-intrusive photogrammetric method was used to collect high-resolution and synchronous data regarding the free surface water elevation and bed level, from which shoreline location, sandbar position, cross-shore sediment transport rates, and nonlinear wave parameters were derived. The cross-shore sediment transport was in agreement with previous laboratory measurements, including the monotonous exchange from foreshore erosion to shoaling zone accretion in most stages of the storm simulation. The surf zone was the main region supplying sediment for beach morphology modification and sandbar generation. The degree of storm erosion was not completely determined by the largest wave height and water level or the cumulative wave power of the apex phase. The largest gradients of the wave parameter sequence change occurred in the rising phase, and this was the main factor generating efficient beachface erosion. It induced an increase in sandbar size, accompanied by the cross-shore motion of maximum velocity amplitude, more violent disturbances of wave nonlinearity, and increased surf zone erosion, with these factors increasing beach instability and leading to more severe storm erosion. The large wave height and water level resulted in shoreline retreat, with a more significant swash zone erosion under a higher runup. The offshore sediment transport turned toward the onshore direction as the original large sandbar deteriorated under the decreasing wave parameter sequence in the waning phase.

Performance evaluation of different reflected signal extraction methods on GNSS-R derived sea level heights

In order to obtain reliable GNSS-R derived sea level heights (SLHs), it is crucial to extract accurate reflected signals from signal-to-noise ratio (SNR) data. In this study, Quadratic Fitting, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Singular Spectrum Analysis (SSA) are adopted for extracting the reflected signals from SNR data, which are then analyzed with Lomb Scargle Periodogram (LSP) assisted with tidal harmonic analysis for GNSS-R SLHs. Three continuous GNSS stations including Onsala in Sweden, Friday Harbor in the United States, and Brest in France with tidal ranges of approximately 1 m, 3 m, and 7 m, respectively, were used in the study. The derived SLHs were evaluated against the nearby or co-located tide gauge records. The results reveal that the three proposed algorithms effectively cope with the multi-peaks spectrum problems when using the conventional Quadratic Fitting without a priori reflected height constraint, and SSA can provide the solutions with fewer outliers among the proposed methods with the RMSEs of 6.7 cm, 13.4 cm and 39.6 cm at Onsala, Friday Harbor, and Brest stations, respectively. Without implementing tidal harmonic analysis, SSA is the method capable of acquiring reliable SLHs among the stations, as evidenced by comparing with co-located gauge records, particularly at the Brest station. In summary, SSA not only distinguishes signals across different frequencies, but also orders the signal components according to their eigenvalues, demonstrating the potential to extract the reflected signals from SNR and enhance the stability and accuracy of GNSS-R applications.

Abstract P394: Associations of Street-View Greenspace Exposure With Cardiovascular Health (Life’s Essential 8) Among Women in Midlife

Introduction: Many women experience suboptimal cardiovascular health (CVH) during midlife. Greenspace exposure has been inversely associated with cardiovascular disease outcomes and risk factors, but the majority of studies use satellite-based assessment of greenspace and limited evidence exists regarding overall CVH. Methods: We performed a longitudinal analysis of women in the Project Viva cohort based in Massachusetts. We applied deep learning algorithms to Google street view images to derive metrics of visible greenspace within 500m of participant’s residential addresses in 2012-2016 (median 46y). We calculated %greenspace from trees, grass, and other greenspace (i.e. plants, fields, flowers) within each image, separately and total combined, and transformed each into z-scores. At the Mid-Life Visit ~5 years later (51y), participants completed questionnaires and research staff measured their height, weight, blood pressure, and blood levels of glucose, HbA1c and cholesterol. We calculated CVH score using Life’s Essential 8 (LE8) algorithm (0-100 points, higher=better), that includes 8 components of CVH, including 4 behavioral measures (heart healthy levels of diet, physical activity, sleep, avoidance of nicotine) and 4 biomedical measures (body mass index (BMI), blood pressure, blood lipids, blood glucose). We used linear regression models adjusted for age, race and ethnicity, and an index score of socioeconomic status comprising both individual and neighborhood metrics. Results: Among 678 participants, 68% were non-Hispanic White and 74% were college grads. In midlife, mean (SD) LE8 score was 76 (12) points. In unadjusted models, greater total %greenspace (per SD) was associated with higher overall CVH score (β 3.09; 95% CI 2.01, 4.17), as well as higher behavioral (β 2.71; CI 1.69, 3.73) and biomedical (β 3.09; CI 1.56, 4.62) sub-scores. Covariate adjustment attenuated these associations but they remained statistically significant for overall (β 1.88; CI 0.58, 3.19), behavioral (β 1.61; CI 0.44, 2.77) and biomedical (β 2.20; CI 0.31, 4.10) CVH scores. When disentangling the components of greenspace, %trees (adjβ 1.83; CI 0.54, 3.13) was associated with overall CVH, but %grass was non-significant (adjβ 0.75; CI -0.53, 2.02). Greater %trees was associated with higher scores for each CVH component in unadjusted models (except lipids): diet (β 1.64; CI 0.41, 2.86), physical activity (β 4.19; CI 1.61, 6.76), nicotine avoidance (β 2.07; CI 0.40, 3.75), sleep (β 3.50; CI 1.93, 5.08), BMI (β 6.51; CI 3.89, 9.14), blood pressure (β 2.39; CI 0.30, 4.48) and glucose (β 3.09; 1.36, 4.83). Covariate adjustment attenuated these associations to non-significance except for diet (β 2.33; CI 0.90, 3.75), BMI (β 3.08; 0.10, 6.06) and glucose (β 2.82; CI 0.73, 4.91). Conclusions: Greater street-view greenspace exposure, especially %trees, was associated with better CVH among women in midlife.

On some methodical aspects of estimation of intergroup morphological variability in growth studies

The significance of growth activity peak in screening studies of children and adolescents as biomarker of chronobiological status of the population is the focus of present research. The broad spectrum of samples (N = 43), including different ethno-territorial groups of Russia and former USSR, examined throughout a wide historical period of second half of 20th — beginning of 21st century, was included in the analysis. The material source was data from literature and authors’ own databases. The following indicators of growth activity peak were calculated for each group: age (in years) of the maximal velocity of growth changes of the average level of height through the adolescence, estimated by empiric rows of annual changes of mean height with further smoothing by the least squares method — separately for boys and girls; its absolute value (cm) and chronological age (years). Intragroup sexual differences of these characteristics were considered as well. The subsequent statistical analysis (Statistica 10 package was used) revealed, that intergroup distribution of age at peak height velocity was not Gaussian and gravitated towards bimodality for both sexes. Herewith the least urbanized groups (rural Abkhazians and urban Mongolians) were excluded from analysis due to the specificity of the dynamic lines of height gains. The factor analysis established auton-omy of pubertal growth acceleration of male and female teenagers — the first factor describes growth activity for males, the second one — for females. The analysis of intergeneration dynamics of age at peak height velocity, on the example of Moscow boys and girls, established, that the used parameter might indicate occasional changes of quality of life of a population (for ex-ample, introduction of free school lunches during the NEP period), as well as more global changes (intensified urbanization throughout the 20th century). The results of the study suggest that the variability of the growth activity peak parameters indicate significant social/anthropogenic base of chronobiological status of the population and independent growth strategies of males and females, which allows considering it as valid and promising biomarker in population growth studies.

Temperature forecasts for the continental United States: a deep learning approach using multidimensional features

Accurate weather forecasts are critical for saving lives, emergency services, and future developments. Climate models such as numerical weather prediction models have made significant advancements in weather forecasts, but these models are computationally expensive and can be subject to inaccurate representations of complex natural interconnections. Alternatively, data-driven machine learning methods have provided new dimensions in assisting weather forecasts. In this study, we used convolutional neural networks (CNN) to assess how geopotential height at different levels of the troposphere may affect the predictability of extreme surface temperature (t2m) via two cases. Specifically, we analyzed temperature forecasts over the continental United States at lead times from 1 day to 30 days by incorporating z100, z200, z500, z700, and z925 hPa levels as inputs to the CNN. In the first case, we applied the framework to predict summer temperatures of 2012, which contributed to one of the extreme heatwave events in the U.S. history. The results show that z500 leads to t2m forecasts with relatively less root mean squared errors (RMSE) than other geopotential heights at most of the lead time under consideration, while the inclusion of more atmospheric pressure levels improves t2m forecasts to a limited extent. At the same lead time, we also predicted the z500 patterns with different levels of geopotential height and temperature as the inputs. We found that the combination of z500, t2m, and t850 (temperature at 850 hPa) is associated with less RMSE for the z500 forecasts compared to other inputs. In contrast to the 2012 summer, our second case examined the wintertime temperature of 2014 when the upper Midwest and Great Lakes regions experienced the coldest winter on record. We found that z200 contributes to better t2m predictions for up to 7-days lead times whereas z925 gives better results for z500 forecasts during this cold event. Collectively, the results suggest that for long-range temperature forecasts based on the CNN, including various levels of geopotential heights could be beneficial.

Biomechanical analysis of total arch maxillary distalization using infrazygomatic crest miniscrews: a finite element analysis study

AimTo evaluate the maxillary incisors and canine’s immediate movement tendency using three different power arms (PA) height levels during total arch maxillary distalization supported on infrazygomatic crest (IZC) miniscrews according to finite element analysis (FEA).MethodsThree finite element models of the maxilla were developed based on CBCT imaging of a teenage male patient presenting a Class II Division 1 malocclusion in the early permanent dentition. Maxillary complex, periodontium, orthodontic accessories, IZC miniscrews and an orthodontic wire were digitally created. The PAs were placed between canines and lateral incisors and projected at 4, 7, and 10 mm height distances. After that, distalization forces were simulated between PA and IZC miniscrews.ResultsThe anterior teeth deformation produced in the FEA models was assessed according to a Von Mises equivalent. The stress was measured, revealing tendencies of initial maxillary teeth movement. No differences were found between the right and left sides. However, there was a significant difference among models in the under-stress areas, especially the apical and cervical root areas of the maxillary anterior teeth. More significant extrusion and lingual tipping of incisors were observed with the 4 mm power arm compared to the 7 mm and 10 mm ones. The 10 mm power arm did not show any tendency for extrusion of maxillary central incisors but a tendency for buccal tipping and intrusion of lateral incisors.ConclusionThe maxillary incisors and canines have different immediate movement tendencies according to the height of the anterior point of the en-masse distalization force application. Based on the PA height increase, a change from lingual to buccal tipping and less extrusion tendency was observed for the incisors, while the lingual tipping and extrusion trend for canines increased.

Ultrasonic liquid level detection method based on the variation of reflected energy on the inner wall of a container

Liquid level detection plays an important role in industry. For the liquid level detection of flammable, explosive and volatile liquids, the detection method based on the ultrasonic principle is usually adopted. Though a lot of efforts, the detection precision is still challenging. Herein, Ultrasonic liquid level detection method is studied to address the detection precision. A projection model of the inner wall of the sound beam is established. The transmitter–receiver probe is placed near the height of the liquid level on the outer wall of the liquid tank. The sound beam emitted by the probe passes through the tank wall and generates a projection area on its inner wall. The energy of the echo signal received by the probe is calculated, and the relationship between the proportion of liquid in the projection area and the energy is used to detect changes in the liquid level. The test system is set up, the echo signal received by the probe is collected, and its normalized energy is calculated. The results show that for different tank wall thicknesses, liquid types and detection frequencies, the normalized energy can clearly characterize the change of liquid level with an error of less than 3 mm. It provides a new avenue for liquid level detection. The research also shows that for the determined detection frequency, when the tank wall thickness corresponds to the values greater than or equal to the critical distance of the probe, the detection accuracy of liquid level change is the highest.

Exploring determinants of sex and family history-based disparity in type 2 diabetes mellitus prevalence among clinical patients

BackgroundType 2 diabetes mellitus represents a multifaceted disorder characterized by intricate pathophysiological mechanisms, encompassing diminished insulin secretion, augmented hepatic glucose production, and heightened insulin resistance. This study aims to assess the sex (Male and Female only) and family history-based differences in the prevalence of T2DM and explore the determinants contributing to this disparity among clinical patients.Subjects and methodsThe study encompassed a diverse pool of clinical patients, encompassing both individuals with diabetes and those without the condition, who had previously sought medical attention for clinical checkups at healthcare centers. The collected data included essential parameters such as blood pressure, weight, height, smoking habits, educational background, and physical activity levels. To ensure methodological rigor and data accuracy, blood pressure measurements adhered to the stringent guidelines set forth by the World Health Organization.ResultsParticipants of the present study reported diabetes, among which notable findings emerged regarding health indicators. It was observed that the prevalence of high blood pressure, obesity, and high blood cholesterol exhibited a statistically significant increase among the female participants, underscoring the sex-based disparities in these health parameters. The male population aged 60 or older, the presence of a family history of DM accentuated this risk, resulting in a striking 3.1 times higher prevalence compared to females, who exhibited a 2.4 times higher risk (OR = 2.4, p = 0.0008). This intriguing relationship between diabetes and cholesterol levels was not limited to sex. Both male (OR = 2.47) and female (OR = 2.1) diabetes patients displayed highly significant associations with cholesterol levels. The risk of T2DM was significantly associated with triglycerides in both sexes (1.58 times higher in males, and 1.71 times higher in females).ConclusionsThe significance of hypertension as a comorbidity in T2DM, highlighting sex-specific associations and the potential impact of a family history of diabetes on blood pressure. Our findings emphasize the importance of considering lipid profiles, obesity, and their sex-specific associations when assessing and managing diabetes risk. Comprehensive diabetes care should include strategies for lipid control, weight management, and cardiovascular risk reduction, tailored to the individual’s sex and specific risk profile.

Developing Representative Test Specimen Conditions for Rutting Mechanical Test Methods of Airfield Pavements

Developing Representative Test Specimen Conditions for Rutting Mechanical Test Methods of Airfield Pavements

An efficient metamodel to downscale total water level in open beaches

A hybrid methodology is described to obtain the wave set-up and infragravity wave level components for estimating total water level (TWL) in coastal areas, taking into account the surf zone hydrodynamics involved. By combining statistical tools and numerical methods, high-resolution spatial distributions of wave height and water level components are calculated efficiently and seamlessly. The proposed methodology is tested and numerically validated on La Salvé beach (Cantabria, Spain), showing a robust performance for diverse scenarios. The resulting TWL can efficiently evaluate coastal hazards in forecast and hindcast mode, having strong potential as a guideline for coastal engineers, managers and emergency planners in the elaboration of adaptation and mitigation strategies. As an application of the metamodel to these coastal management tasks, significant past storm events have been reconstructed, and their associated TWL-driven coastal flooding hazard has been analyzed using a impact rank scale.

A new approach to transformation of micro analysis into macro analysis of forces to study the dynamic behaviour of riverbank morphology

ABSTRACT Due to complex nature of the riverbank erosion and owing to action of wide variety of forces on the particles, it is an uphill task for the researchers to find a general approach to riverbank failure mechanism. Here, a planar failure-block of right-angled trapezoidal shape spanning to the tension crack has been considered. Transformation of inter-particle microscopic forces into macroscopic forces has been made to analyse the stability of the block. Stability of the failure block is a function of the bank erosion rate in terms of the escape velocity. Moreover, varying free surface water level has been investigated to study the dynamic behaviour of the bank. With the increasing number of base particles in the tension crack from 10 to 100, escape velocity increases by 8.47% as the water level rises and 10.74% as it falls. Similarly, the increase in escape velocity with free surface water level height from 0 to full height of the tension crack is 39.4% as the water level rises and is 48.4% as water level falls. Rising water level increases hydrostatic confining pressure force, which is a resistive force. This increases the escape velocity continuously leading to greater stability of the mass.

Experimental study on mechanical properties of FDM 3D printed polylactic acid fabricated parts using response surface methodology

The purpose of this study is to examine the mechanical properties of components produced through the Response Surface Methodology for polylactic acid, utilizing the Fused Deposition Modeling 3D printing technique. Polylactic acid is a commonly employed biodegradable polymer, making it a desirable substance for diverse applications. This study involves carrying out experiments to vary process printing parameters like layer height or thickness, part orientation, and infill density. The values of these parameters were obtained using a Response Surface Methodology Box–Behnken experimental design. The mechanical performance of the 3D Printed polylactic acid fabricated was assessed by evaluating their flexural and tensile strength. The test samples for measuring tensile and flexural strength are fabricated according to American Society for Testing and Material standards. The findings suggest that higher strength is achieved when using increased layer height and infill levels. The experimental results indicated that specimens with a filling ratio of 80% exhibited greater tensile strength, while the flexural strength of samples with 50% infill was observed to be higher. Regression analyses and multi-optimization techniques were employed to predict the experimental results. This study provides valuable insights that can significantly impact various industries. Our research on the complex interactions between process variables and mechanical properties has major implications for improving high-strength component manufacturing. As demand for dependable and efficient 3D-printed materials rises, our discoveries improve material design and manufacturing methods, making a significant contribution to the field.

Portrait of Metabolic Syndrome by Gender in A Rural Area of Gowa Regency

Metabolic syndrome is an important indicator in efforts to control non-communicable diseases. The incidence of metabolic syndrome is directly proportional to the incidence of obesity while the prevalence of metabolic syndrome varies based on sociodemographic characteristics and lifestyle. Efforts to identify metabolic syndrome in the community are a crucial step to identify early the potential for hypertension, coronary heart disease, stroke and diabetes mellitus. This study aims to identify components of metabolic syndrome based on gender. Cross sectional study is the research design that underlies data collection on risk factors for metabolic syndrome which is carried out through interviews and measurements of height, weight, waist circumference, blood pressure and cholesterol levels using NCEP-III criteria as the basis for identifying metabolic syndrome. Data analysis used the Chi-Square test. The metabolic syndrome component in the form of obesity is identified in men and women using different indicators. Both men and women have experienced increases in systolic blood pressure and cholesterol levels. A history of smoking was the only risk factor for metabolic syndrome with a statistically significant difference in proportion between men and women. Behavioral modifications such as eating patterns, rest, and others according to the guidance of the Koran and hadith are needed to minimize the incidence of metabolic syndrome.

Characterization of FDM 3D Printed Parts Using TPU + PETG Filaments For Shin Guard Products

3D printing machines are used to print products that support sports activities, such as shin guards. During sports, shin guards are protective equipment to prevent injury to the lower legs. Filaments that are suitable for making shin guards are thermoplastic polyurethane (TPU) and polyethylene terephthalate (PETG) because they have impact resistance properties needed to protect the feet during sports. The variation is the level parameter layer height, nozzle temperature, printing speed, and bed temperature. Next, an impact test will be carried out to determine the optimal parameter variation on the 3D printing machine, which is expected to be a reference for printing quality products. This study uses a 3D printer, Ender v3, to print specimens and shin guard products. The material used is TPU+PETG filament. The Taguchi method with the orthogonal matrix L9(3)4 was repeated thrice for each experiment. After that, an analysis of variance was carried out. Parameter variations used in the study were layer height (0.1 mm, 0.2 mm, 0.3 mm), nozzle temperature (220℃, 225℃, 230℃), printing speed (45mm/s, 45mm/s, 50mm/s) and bed temperature. (70℃, 75℃, 80℃). In this study, Charpy impact testing will be carried out. The combination of factors that can produce an optimal impact test is layer height level 2 (0.2 mm), nozzle temperature level 1 (220℃), printing speed level 3 (50 mm/s) and bed temperature level 2 (75℃) with an impact strength value the highest was 27.20 and the lowest was 11.07. The combination of factors that have the most significant effect on the impact test strength values is layer height 63.97%, nozzle temperature 6.19%, printing speed 2.07% and bed temperature 4.74%.

An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height

Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine–cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change.

The Effect of Chrysin on the Distribution of Extracellular Matrix Proteins around Leydig Cells under Heat Stress Injury

Objective. In the testicular tissue stroma, matrix proteins such as laminin and fibronectin play a critical role in transmitting signals under normal and stressful conditions to Leydig cells. Heat stress is considered one of the stressful conditions in the testicular tissue. This study has investigated the protective effects of chrysin on the distribution of laminin and fibronectin around Leydig cells under heat stress. Methods. Rats were divided into four groups, including sham (A), heat stress (B), heat stress with chrysin treatment (50 mg/kg) (C), and heat stress with chrysin treatment (75 mg/kg) (D). At the end of the treatment period, on the 21st day, testosterone serum levels were evaluated, and the testes were also assessed to determine the changes in gene expression and distribution of laminin, fibronectin, and caspase-3 proteins. Results. Exposure to heat stress in Group B resulted in increased levels of caspase-3, decreased activity, height, and diameter of seminiferous tubules, and altered distribution of laminin and fibronectin proteins around the Leydig cells when compared to the sham group (p<0.05). Treatment with chrysin 75 resulted in a significant decrease in caspase-3 levels, improved testosterone levels, and increased distribution of laminin and fibronectin around Leydig cells compared to group B (p<0.05). In addition, the group treated with chrysin 75 showed improved seminiferous histological activity markers in comparison to group B (p<0.05). Conclusion. Following heat stress, concurrent with disrupted steroidogenesis, an evident change happens in the distribution pattern of the extracellular matrix (ECM) proteins of the testis, which may indicate the transmission of stress signals from the ECM to the Leydig cells. Treatment of chrysin in 75 mg/kg dose can reduce the damage caused by heat stress on the ECM and Leydig cells.

Application of a comprehensive atmospheric boundary layer model to a realistic urban-scale wind simulation

Atmospheric Boundary Layer (ABL) is the region below the stratosphere that is significantly affected by Earth’s surface. In this article, an improved ABL model has been tested and validated with field measurements, while simultaneously highlighting the consequences of using an incorrectly formulated ABL model when performing pedestrian wind comfort and safety analysis using Computational Fluid Dynamics (CFD) in a full-scale, realistic setting. The improved ABL model was successfully validated on wind tunnel scale test cases previously, and this article provides evidence that the validation can be extended to realistic, urban-scale test cases as well. Steady, incompressible, Reynolds Averaged Navier–Stokes (RANS) simulations were performed and the results obtained from the incorrect ABL model are compared with those from an improved, consistent ABL model, and the main differences between the two formulations are pointed out in terms of wind velocities and turbulent kinetic energies at the pedestrian height level, and wind comfort maps. The analyses show that using an incorrectly formulated ABL model results in an over-prediction of near-ground velocities due to an artificial wind acceleration, a significant under-prediction of turbulent kinetic energy values, and consequently resulting in a significantly worse pedestrian wind comfort map that differs up to 3 comfort classes in some regions.

The Role of Wood Density Variation and Biomass Allocation in Accurate Forest Carbon Stock Estimation of European Beech (Fagus sylvatica L.) Mountain Forests

The European beech (Fagus sylvatica L.) is one of the most common tree species in Romania, with importance both economically and environmentally. Accurate methods of biomass assessment at the tree compartment level (i.e., stump, stem, branches, and leaves) are necessary for carbon stock estimation. Wood density (WD) is an important factor in determining biomass and, ultimately, the tree’s carbon content. The average tree density was found to be 578.6 kg/m3. For this study, WD was evaluated by the weighting method related to tree volume. Also, to investigate a practical approach to determining the weighted wood density (WWDst), models were run using density at the base of the tree (WDBase), density at breast height level using discs (WDDBH), the wood core density (WDic), and the diameter at breast height (DBH) as predictors. The biomass assessment was conducted using different model evaluations for WWDst as well as allometric equations using the destructive method. From the results, it was noted that using the WWDst, the total biomass was underestimated by −0.7% compared to the biomass measured in the field. For allometric equations that included DBH and tree height as independent variables, the explained variability was around 99.3% for total aboveground biomass (AGBtotal), while it was 97.9% for allometric function using just the DBH. Overall, the distribution of biomass across different compartments was as follows: 73.5% in stems, 23.8% in branches, 1.9% in stumps, and 1.3% in leaves. The study findings offer valuable insights into WD, biomass distribution among different components, and biomass allometric quantification in natural beech forest environments in mountainous areas.