Regression Equation Research Articles

Discussion on the calculation formula of haemoglobin glycation index for Chinese population.

To explore the formula for calculating the haemoglobin glycation index (HGI) in an appropriate Chinese population. Evaluating the applicability of the HGI calculation formula proposed by Hempe et al. in the Chinese population; according to the data from CHARLS 2011, 10,587 self-reported nondiabetics aged 20 years and older were selected as the study subjects, regression equations were constructed to fit the linear relationship between HbA1c and FPG in the population, and the profile characteristics of the resultant population were analysed. The standardized HGI formula put forth by Hempe and colleagues has been found unsuitable for application to the Chinese population. Leveraging CHARLS dataset, we derive a novel HGI calculation formula tailored to the Chinese demographic: HGI = Measured HbA1c-Predicted HbA1c, where Predicted HbA1c = 0.011×FPG + 4.032. We found statistically significant differences in HGI among obese individuals; those with higher waist circumference, total cholesterol levels, LDL levels, diastolic blood pressure and those with lower HDL levels. The HGI calculation formula proposed using CHARLS data is more applicable to the Chinese population and can be used as a standardised HGI to Chinese.

Mechanical properties variation of samples fabricated by fused deposition additive manufacturing as a function of filler percentage and structure for different plastics.

One of the key factors in manufacturing products by fused deposition molding (FDM) or layer-by-layer printing technology is the material intensity of the product. The task of reducing the amount of material required to manufacture a product without significant loss of mechanical properties is one of the most practically important technological tasks. Material saving in FDM printing of products allows to reduce financial costs and increase the speed of manufacturing of the final product without reducing (or not significantly reducing) the quality properties of the product. In our work it is demonstrated that using Combs filling type and materials of poly lactic acid (PLA) and polyethylene terephthalate glycol (PETG) it is possible to achieve material savings of up to 23% at 50% filling for PLA and 17% at 75% filling for PETG without significant reduction of product strength in comparison with other filling types. Exceptions are PLA samples with 100% fill and Lateral fill. Application of Kruskal-Wallis criterion and Dunn's criterion with Bonferroni multiple comparison correction showed that there were no statistically significant differences within the strength limits of samples made by FDM printing technology from PLA and PETG plastics (p-value = 0.0514), as well as samples with Triangle and Grid filling type (p-value = 1). Based on this result, three groups of samples statistically significantly differing in ultimate strength were identified by methods of hierarchical cluster analysis; in each group (except for group 1, which included samples made of PLA plastic with Lateral filling type and 100% filling), correlation analysis was performed (Spearman correlation was used). The results of the correlation analysis showed a stable average correlation between the percentage of filling, modulus along the secant 0.05-0.2% strain, ultimate strength and strain corresponding to the yield stress. Analysis of the correlation graph showed that the main parameter correlating with all mechanical properties of the specimen is the 0.05-0.2% strain modulus. Based on this conclusion, robust regression equations predicting the 0.05-0.2% strain modulus as a function of the percentage of specimen filling were constructed for the two selected groups. Analysis of the equations showed that in the third group of specimens, the average modulus of 0.05-0.2% strain is more than twice the modulus of 0.05-0.2% strain in the second group. The detected statistical regularities can be explained by the mechanism of strain hardening, the actual value of which depends on the structure of the macrodefect (type of filling), properties and volume of the material (percentage of filling) used in the fabrication of samples using FDM printing technology.

Validation of water quality parameters using ion balancing and electrochemical approaches: a case study

ABSTRACT The present study validates the accuracy of water quality analysis for twenty geo-referenced water samples from a watershed of Gaya district, Bihar, using ion balancing and electrochemical approaches. Analytical data were presented using box plots, Gibbs diagram and descriptive statistics. Regression equations of various chemical parameters and ionic strength with electrical conductivity (EC) are reported for routine conversion and outlined mathematical equations for data validation. The pH and EC of the water samples varying from 4.9 to 8.7 and 190 to 800 µS cm−1, respectively. Calcium (Ca2+), magnesium (Mg2+), sodium (Na+) and potassium (K+) varied from 0.88 to 2.53, 0.4 to 1.6, 0.8 to 3.75, and 0.01 to 0.22 meq L−1 whereas, bicarbonate (HCO3 –), chloride (Cl–) and sulphate (SO4 2–) ranged from 1.3 to 6.0, 0.25 to 1.5 and 0.01 to 0.6 meq L−1, respectively. Consistency between calculated and measured values judged the correctness based upon relative error (RE %) of the water analysis data. Overall RE on an average of ca. 6% and 5% is reported regarding consistency between the calculated and measured specific conductivity ( calc. and .) and ionic strength ( calc. and .), respectively and difference was statistically non-significant. The ion balancing method showed very good to good analytical data quality for 80% of water samples, while electrochemical approaches (EC and ionic strength) indicated good to moderately good analytical data quality for 85% of the investigated samples. Taking cognisance of the outcome of all validation approaches, an overall 20% rechecking of water samples including (sample ID S3, S8, S13 and S14) is proposed for better analytical data quality for further data mining operations.

Clinical characteristics of hemodynamics and laboratory parameters of recipients during orthotopic heart transplantation

Objective. To study the clinical and laboratory characteristics of recipients during orthotopic heart transplantation according to intraoperative monitoring. Materials and methods. The clinical and laboratory data of 38 patients who underwent orthotopic heart transplantation for vital indications in the period from 2022 to 2024 were studied. The average age of patients was (38.5 ± 13.5) years. There were 35 men (92.1%) and 3 women (7.9%). Peripheral oxygen saturation, arterial and central venous pressure were measured in each recipient delivered to the operating room at 5 stages of the operation during orthotopic heart transplantation. Results. The main cause of chronic heart failure with subsequent heart transplantation in all 38 (100%) patients was dilated cardiomyopathy. Statistically significant (p < 0.05) acidosis and hypercapnia in recipients during heart transplantation were most pronounced at the first stage of surgery, before the connection of a heart–lung machine. Acidosis during heart transplantation had a mixed metabolic and respiratory genesis with hypercapnia and lactatacidosis. A statistically significant (p < 0.05) increased lactate content at the first and second stages and statistically significant (p < 0.05) hyperglycemia at all stages of surgery were found. Central venous pressure was statistically significantly (p<0.05) higher than the reference values at the stages of sternum cutting and before the connection of the heart–lung machine. A direct statistically significant (p<0.05) correlation between the level of arterial blood pH and the level of central venous pressure (r=0.334) was established, with the corresponding linear regression equation: pH=4.7043+central venous pressure × 0.02391. Conclusions. Clinical and laboratory characteristics of recipients undergoing orthotopic heart transplantation demonstrated a direct correlation between mixed metabolic and respiratory acidosis and central venous pressure. Peripheral hemodynamic parameters in orthotopic heart transplantation were characterized by instability at the stage of heart–lung machine disconnection.

Incidence and risk factor analysis of moderate-to-severe pain after preoperative CT-guided hook-wire puncture localization of pulmonary nodules.

Pain is a relatively common complication after hook-wire puncture localization. However, the problem of pain occurrence following this localization procedure has not been sufficiently examined. In this prospective study, we aimed to investigate the incidence and risk factors associated with acute pain after preoperative CT-guided hook-wire puncture localization of pulmonary nodules. Clinical data were collected prospectively from patients with small pulmonary nodules who underwent preoperative CT-guided hook-wire puncture localization between May 2022 and May 2023 at the Affiliated Hospital of Xuzhou Medical University. Patients were divided into two groups on the basis of the presence or absence of moderate-to-severe pain following the localization process. Univariate logistic analysis was conducted to analyse the clinical data of the two groups. Subsequently, the significant variables (P < 0.1) from the univariate analysis were subjected to multivariate logistic analysis, followed by the establishment of regression equations to determine the significant risk factors related to moderate-to-severe pain after hook-wire puncture localization. A total of 281 patients with small pulmonary nodules undergoing preoperative CT-guided hook-wire puncture localization were finally included, with a mean age of 54.86 years (SD = 12.1 years) and female predominance (67.6%). In the resting state, 53% of the patients reported moderate-to-severe pain. Univariate analysis revealed that age (P = 0.085), the depth of localization needle insertion into the lung (P = 0.024) and the location of the chest wall puncture (P = 0.07) were associated with moderate-to-severe pain occurrence after hook-wire puncture localization (P < 0.1). Furthermore, multivariate logistic regression analysis demonstrated that the depth of localization needle insertion into the lung (OR = 0.953, 95% CI: 0.914-0.992, P = 0.02) and the location of the chest wall puncture (lateral chest wall vs. anterior chest wall: OR = 2.101, 95% CI: 1.166-3.784, P = 0.013; posterior chest wall vs. anterior chest wall: OR = 1.163, 95% CI: 0.895-2.971, P = 0.11) were independent predictors of moderate-to-severe pain incidence following CT-guided hook-wire puncture localization (P < 0.05). Moderate-to-severe pain is prevalent in patients with pulmonary nodules who undergo preoperative CT-guided hook-wire puncture localization. The depth of needle insertion into the lung and the location of the chest wall puncture are significant predictors of moderate-to-severe pain onset.

Corrugated veneer panel thermophysical properties

The article substantiates the need to develop new mechanisms for the hardwood use in modern conditions of Republic of Karelia timber industry. One of the potential uses of birch wood in wooden house construction is building materials production from veneer and slab materials based on it. A large amount of associated waste from processing birch wood into veneer stands out as one of the key problems. A new slab joinery and construction material made of corrugated birch veneer is considered. The purpose of this study is to evaluate the thermophysical properties of a corrugated board made of birch wood. To achieve this goal, the tasks and methods of research are defined. An experimental device has been developed to conduct an experiment to determine the values of thermophysical characteristics. DS18B20 temperature sensors were used to measure the surface temperature, as well as to monitor device operation and the room air temperature. The sensors are connected to the Arduino microcontroller platform, which was used to record and transmit sensor readings. Additionally, the course of the experiment was monitored using a thermal imager Testo 875-1i. During the experiment, more than 1000 measurements were carried out. As a result of data processing, a diagram of the dependence of the density of the heat flux passing through the sample on time, as well as diagrams of the dependence of thermal conductivity and thermal resistance on the temperature difference on the sample surfaces, was obtained. The diagrams show the regression dependences of changes in heat flux density, thermal conductivity and thermal resistance during measurements. The values of the heat flux density, thermal conductivity coefficient and thermal resistance calculated on the basis of regression equations and the values obtained experimentally are determined. The directions of further research of the material under consideration are determined.

FORECAST THE USE OF TRAVEL WEBSITES USING THE THEORY OF PLANNED BEHAVIOR

Given the digital era's use of artificial intelligence (AI) in any field, the goal of this article is to investigate the use of the Theory of Planned Behavior (TPB) for forecasting travel websites utilizing AI applications. A total of 120 respondents who used both websites and chatbots were polled. The survey questionnaire elements were adapted from TPB and AI-enabled travel websites. Purposive random sampling was used to randomly assign the sample. Multiple regression analysis, correlation analysis, and descriptive statistics were utilized to investigate the acquired data. The results show that there are correlations among the variables that are included in the model framework. This study used TPB, as demonstrated by the regression equation: Y=0.07+0.46A+0.36S+0.16CB, with a strong R2 of 0.66. These findings demonstrate how well TPB understands and predicts user behavior in the context of AI-driven travel website usage.

Beyond xG: A Dual Prediction Model for Analyzing Player Performance Through Expected and Actual Goals in European Soccer Leagues

Soccer is evolving into a science rather than just a sport, driven by intense competition between professional teams. This transformation requires efforts beyond physical training, including strategic planning, data analysis, and advanced metrics. Coaches and teams increasingly use sophisticated methods and data-driven insights to enhance decision-making. Analyzing team performance is crucial to prepare players and coaches, enabling targeted training and strategic adjustments. Expected goals (xG) analysis plays a key role in assessing team and individual player performance, providing nuanced insights into on-field actions and opportunities. This approach allows coaches to optimize tactics and lineup choices beyond traditional scorelines. However, relying solely on xG might not provide a full picture of player performance, as a higher xG does not always translate into more goals due to the intricacies and variabilities of in-game situations. This paper seeks to refine performance assessments by incorporating predictions for both expected goals (xG) and actual goals (aG). Using this new model, we consider a wider variety of factors to provide a more comprehensive evaluation of players and teams. Another major focus of our study is to present a method for selecting and categorizing players based on their predicted xG and aG performance. Additionally, this paper discusses expected goals and actual goals for each individual game; consequently, we use expected goals per game (xGg) and actual goals per game (aGg) to reflect them. Moreover, we employ regression machine learning models, particularly ridge regression, which demonstrates strong performance in forecasting xGg and aGg, outperforming other models in our comparative assessment. Ridge regression’s ability to handle overlapping and correlated variables makes it an ideal choice for our analysis. This approach improves prediction accuracy and provides actionable insights for coaches and analysts to optimize team performance. By using constructed features from various methods in the dataset, we improve our model’s performance by as much as 12%. These features offer a more detailed understanding of player performance in specific leagues and roles, improving the model’s accuracy from 83% to nearly 95%, as indicated by the R-squared metric. Furthermore, our research introduces a player selection methodology based on their predicted xG and aG, as determined by our proposed model. According to our model’s classification, we categorize top players into two groups: efficient scorers and consistent performers. These precise forecasts can guide strategic decisions, player selection, and training approaches, ultimately enhancing team performance and success.

Eccentric magnetic abrasive finishing of 316 steel

This paper presents a new method of eccentric magnetic-abrasive finishing as applied to 316 stainless steel. The proposed solution is based on solid ferromagnetic abrasive grains (Fe-Si) held by an external magnetic field. A workpiece fixed in a holder with a certain eccentricity is inserted between the grains and the holder is rotated. The use of an additional eccentricity has a significant impact on the nature of the process, in particular, it increases the speed of the sample relative to the abrasive grains, makes it possible to use more abrasive grains, induces mixing abrasive grains, which can prolong their active life time. In this study, a three-factor, five-level design of experiment was conducted. The variable parameters were: machining time, eccentric value, and rotational speed. Samples in the form of rollers with a diameter of 3 mm were mounted in a special holder that allowed to obtain the assumed eccentricity. Based on the results obtained, regression equations were developed for selected roughness parameters Ra, Rz, Rq, Rt. The value of Ra roughness decreased from the initial value of 0.575 μm to a value below 0.1 μm (an 80% decrease) in 9.1 min. The results obtained provided the basis for further research in conducting eccentricity in magnetic-abrasive machining as an intensifying element of the process.

A Socio-Ecological Approach to Work Performance: A pilot study among Workers with Chronic Skin Diseases

Abstract Objectives Chronic skin diseases (CSD) may lead to productivity losses. This mixed-methods study investigated symptom severity, social challenges, need for workplace accommodation, sick leave and their association with perceived impaired work performance (IWP) among workers with CSD. Methods Data were collected from April to June 2023. Participants (n = 102) completed an online questionnaire sent from the Institute for Occupational Safety and Hygiene and almost 20% were interviewed. The analyses included logistic regression and structural equation models and a thematic analysis. Results Of participants, 72.3% reported perceived IWP. The need for workplace accommodation and sick leave duration were directly associated with IWP. These findings were integrated with common themes based on a socioecological approach. Conclusions By working together to address personal, interpersonal, organizational, and societal aspects associated with CSD, occupational physicians and stakeholders can alleviate perceived IWP.

Sphenoid Ridge in Fetuses: Size Analysis, Classification, and Clinical Implications.

To identify the size and angle of the sphenoid ridge (SR) in fetuses. Skull bases of 32 fetuses (11 males / 21 females) aged 17 to 32 weeks of gestations were included the study. The angle of SR (SRA), length of LW (SRL), and also LW widths at the midline (SRW-ML), at the midpoint (SRW-MP), and at the lateral point (SRW-L) were measured. SRL, SRA, SRW-ML, SRW-MP, and SRW-L were measured as 22.98±4.62mm, 150.69±8.05 degrees, 6.57±1.46mm, 4.68±1.07mm, and 2.68±0.71mm, respectively. No significant difference was observed between the measurements in terms of sex and sides. Apart from SRA, the parameters were greater in the third-trimester fetuses than the second-trimester fetuses. SRA was similar in both trimester fetuses. SRA did not alter with advancing gestational weeks, but the other increased. In fetuses, only one configuration regarding SRA types was observed. Type A was observed in all fetuses. Linear function was calculated as y=1.411 + 0.902×age for SRL, y=-0.137 + 0.281×age for SRW-ML, and y=1.024 + 0.069×age for SRW-L. Our study provides beneficial data for neurosurgeons and anatomists to understand the development of SR in the prenatal period. The authors' regression equations for determining the growth dynamics of the ridge may be used to estimate SR parameters.

Predictive modeling of the probability of gynecological complications of tamoxifen therapy: A prospective study

Background. It is well known that adjuvant therapy for breast cancer (BC) with tamoxifen is associated with an increased risk of endometrial hyperplasia (EH). Currently, the problem of hyperplastic processes and endometrial cancer during long-term tamoxifen therapy is particularly relevant since the incidence of endometrial disorders has a direct correlation with the duration of tamoxifen therapy. Follow-up by a gynecologist should be tailored to consider the risk of endometrial cancer in women who survived BC to improve the early detection of endometrial cancer and avoid unnecessary invasive procedures. Aim. To predict adverse drug reactions (ADRs) of tamoxifen, including EH, endometrial polyps, and abnormal uterine bleeding, based on mathematical modeling in relation to the carriage of polymorphic variants of cytochrome P450 enzyme genes and drug transporter proteins. Materials and methods. A prospective pharmacogenetic study of 120 women with stage I–III luminal BC receiving tamoxifen was conducted. The polymerase chain reaction method was used to detect the presence of polymorphisms of cytochrome P450 genes, followed by an assessment of their associations with ADRs of tamoxifen, namely with local gynecological symptoms. Results. Predictive models of ADR occurrence based on the logistic regression function have been created and validated by ROC analysis. We have developed a prognostic model to estimate the probability of developing a composite endpoint (polyp, EH, abnormal uterine bleeding) based on history and genetic risk factors. It was found that the predictors of the combination of local gynecological ADRs (EH, endometrial polyp, and abnormal uterine bleeding) include such factors as weight loss, the presence of asthenia, an increase in the number of births, carriage of the TT genotype of the ABCB1 3435 polymorphic variant and the GG genotype of the CYP2D6*4 polymorphic variant, GG of the CYP3A5 polymorphic variant. The relatively high incidence (43.3%) of local gynecological symptoms requires regular monitoring by obstetrician-gynecologists using instrumental methods (transvaginal ultrasound). Conclusion. This prognostic model has demonstrated high diagnostic effectiveness, which allows it to be implemented in the routine clinical practice of an obstetrician-gynecologist.

Accurate Prediction of 327 Rice Variety Growth Period Based on Unmanned Aerial Vehicle Multispectral Remote Sensing

Most rice growth stage predictions are currently based on a few rice varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build growth stage prediction models that tend to have poor generalization ability, low accuracy, and face various challenges. In this study, multispectral images of rice at various growth stages were captured using an unmanned aerial vehicle, and single-plant rice silhouettes were identified for 327 rice varieties by establishing a deep-learning algorithm. A growth stage prediction method was established for the 327 rice varieties based on the normalized vegetation index combined with cubic polynomial regression equations to simulate their growth changes, and it was first proposed that the growth stages of different rice varieties were inferred by analyzing the normalized difference vegetation index growth rate. Overall, the single-plant rice contour recognition model showed good contour recognition ability for different rice varieties, with most of the prediction accuracies in the range of 0.75–0.93. The accuracy of the rice growth stage prediction model in recognizing different rice varieties also showed some variation, with the root mean square error between 0.506 and 3.373 days, the relative root mean square error between 2.555% and 14.660%, the Bias between1.126 and 2.358 days, and the relative Bias between 0.787% and 9.397%; therefore, the growth stage prediction model of rice varieties can be used to effectively improve the prediction accuracy of the growth stage periods of rice.

Synergistic Effect of Influence Factors on Ammonia Volatilization in Fertilizer Solution: A Laboratory Experiment

Ammonia volatilization, which is one of the main ways that nitrogen gas is released from farmland, restricts promotion of the utilization of nitrogen fertilizer and contains some potential environmental risks. To investigate the general pattern of ammonia volatilization under actual paddy field conditions, we designed an indoor simulated system to measure the amount of ammonia volatilized within a single time period by controlling the pH and concentration of NH4+ (c(NH4+)) in the solution, the gas–liquid interfacial gas velocity, and the ambient temperature. In this paper, the influence of these factors, the synergistic effect on ammonia volatilization, and their quantitative relationship are discussed. We used solutions of ammonium bicarbonate (SAB) and diammonium phosphate (SDP) for the simulation experiments, and the results showed that there is a significant linear relationship between the amount of ammonia volatilization and c(NH4+). The correlation coefficients were between 0.9214 to 0.9897 and 0.8932 to 0.9904 for SAB and SDP, respectively. The quantitative relationship between temperature and pH and the influence factor (CIF) and the initial ammonia volatilization fluxes (IAVFs) was analyzed by the least-squares method, and the degrees of polynomial were one and two, respectively. The regression equations of the SAB and SDP among the amount of ammonia volatilization with the concentration of ammonium nitrogen, the temperature, and the pH were calculated by using MATLAB. Considering the effects of temperature and pH on the CIF and IAVFs under individual conditions, we used a binary cubic model to fit the relationship between temperature and pH to the CIF and IAVFs. The simulation results showed that the correlation coefficients between the CIF and IAVFs for SAB were 0.9980 and 0.9680, respectively, and the correlation coefficients were 0.9946 and 0.9708 for SDP, respectively. The quantitative equation took into account the coefficient of determination and degrees of polynomial, and the ammonia volatilization fluxes can be calculated by using these equations.

Nested cross-validation Gaussian process to model dimethylsulfide mesoscale variations in warm oligotrophic Mediterranean seawater

The study proposes an approach to elucidate spatiotemporal mesoscale variations of seawater Dimethylsulfide (DMS) concentrations, the largest natural source of atmospheric sulfur aerosol, based on the Gaussian Process Regression (GPR) machine learning model. Presently, the GPR was trained and evaluated by nested cross-validation across the warm-oligotrophic Mediterranean Sea, a climate hot spot region, leveraging the high-resolution satellite measurements and Mediterranean physical reanalysis together with in-situ DMS observations. The end product is daily gridded fields with a spatial resolution of 0.083° × 0.083° (~9 km) that spans 23 years (1998–2020). Extensive observations of atmospheric methanesulfonic acid (MSA), a typical biogenic secondary aerosol component from DMS oxidation, are consistent with the parameterized high-resolution estimates of sea-to-air DMS flux (FDMS). This represents substantial progress over existing coarse-resolution DMS global maps which do not accurately depict the seasonal patterns of MSA in the Mediterranean atmospheric boundary layer.

Response surface methodology-based optimization of hairy roots cultures for in vitro AM production

Arbuscular mycorrhizal fungi can do wonders in promoting the crop growth as well maintaining soil health. In vitro root organ culture technology is an exciting avenue for AM biofertilizer production. This study aims to optimize the key influencing factors for enhancing the hairy root production used for in vitro AMF culturing using response surface methodology, a statistical and mathematical tool used for designing optimization studies. Study uses Rhizobium rhizogenes MTCC 2364 for transformation in carrot explant. Factors considered for optimization are concentration of gelling agent (phytagel), carbon source (sucrose) and pH of the Modified Strullu and Romand medium (MSR). Design Expert software uses second order polynomial regression equation for predicting the outcome of each experiment. Totally, 17 experiments were run following Box-Behnken design and average hairy root length and average side branch emergence were taken as response. ANOVA analysis reveals that the concentration of phytagel and sucrose had a strong influence on root length, while the phytagel and pH of the medium had a strong effect on side branch emergence. Overall, taking into account the both responses, concentration of phytagel had a significant impact on hairy root production. The maximum average hairy root length obtained was 1.12 cm and number of side branches emerged were 6.78 per day. Based on these results, the optimal parameters were MSR medium with 3gL-1 phytagel, 11gL-1 sucrose and a pH of 4 for boosting hairy root development. This study is a cost-effective approach and minimizes the time taken for establishing the hairy root technology.

Finite element simulation of knee joint pressure and surface displacement: relationship and influencing factors

Knee sleeves are widely used functional pressure garments that exert pressure on the knee, causing body surface displacement and deformation. Understanding the relationship between knee joint pressure distribution and body surface displacement, along with the factors affecting this displacement, is crucial for optimizing knee sleeves. This study established a biomechanical model of the human knee joint using three-dimensional reconstruction and finite element analysis. The study simulated the pressure distribution and body surface displacement after wearing five different knee sleeve designs. Using SPSS software, it analyzed clothing and human factors to determine the main influences on displacement, resulting in linear regression equations linking knee physiological characteristics and skin displacement. Findings indicated that skin curvature radius and the distance between skin and bone significantly affect displacement, while knee sleeve structure design shows less correlation. The study also divided the knee joint into eight sections to calculate volume shrinkage after compression, providing insights for designing knee sleeves that enhance comfort and functionality based on human physiology.

Dosing of Bacterial Phytase (EC 3.1.3.26) in Laying Hens’ Diets

Despite the acknowledged advancements in the utilization of phytase dosing, limited research has been conducted on commercial laying hens. To assess the effectiveness of doses of exogenous phytase, a total of 320 laying hens aged between 44 and 64 weeks were utilized. These hens were divided into five treatments, which were then distributed using a completely randomized design with eight replicates, each containing eight birds. The treatments consisted of phytase supplementation levels of 0, 500, 1000, 1500, and 3000 FTU/kg. Quadratic regression equations were utilized to determine the maximum and minimum values for the levels of phytase supplementation. A significant difference was observed in the levels of phytase supplementation, where a quadratic model was fitted to various variables including egg weight (g), egg mass (g/bird/day), feed conversion per egg mass (g/g), yolk concentration (%), albumen concentration (%), shell thickness (μm), shell strength (kgf), specific gravity, and yolk color. Further, an increased supplementation improved the digestive tract of the hens, thereby providing a better villus–crypt ratio. Considering the advantageous effects of phytase dosing, it is recommended to supplement corn and soybean meal-based diets with 1500 FTU/kg of bacterial phytase for laying hens during the 44- to 64-week production period.

Modeling the Terminal Velocity of Rising Electrocharged Microbubbles

The generation of electrocharged microbubbles is very important for several separation processes (e.g., water treatment, paper industry, and mineral processing). However, their rising terminal velocities are not fully understood. This work presents a laboratory study of the terminal velocity of single microbubbles (bubble diameter (Db) &lt; 100 µm) rising in stagnant aqueous solutions with different pH levels (from 2 to 12) and reagent types (frother and collector; 30 ppm). The measurements were compared with the respective predicted velocities computed from the Stokes and Hadamard–Rybczynski models. It was found that the terminal velocities of electrocharged microbubbles were larger than the respective predictions from the Stokes equation. A regression equation was proposed to predict the terminal velocity as a function of the bubble diameter, which showed considerable dispersion depending on the type of reagent adsorbed on its surface, the concentration of these reagents, and the physical characteristics that the boundary layer acquires by modifying the zeta potential of the microbubbles; this effect has not yet been addressed in the literature.

Anti-fracking protests and compliance with police: An examination of public opinion in the Anthropocene

Few studies that investigate public views about compliance with police consider the environmental movement. The present study helps fill this gap in the literature by investigating attitudes about protester compliance with police at anti-fracking protests. Study data come from a representative sample of adults living in the United Kingdom ( n = 1051). Multivariate findings generated by ordinary least squares (OLS) regression and structural equation modelling show that the public is less likely to agree that anti-fracking protesters should obey police commands when they perceive that hydraulic fracturing is a risk. Moreover, views about hydraulic fracturing, the environment, protest and protesters all appear to influence perceptions that anti-fracking protest police act in ways that are procedurally (un)just. Procedural justice, in turn, is positively correlated with people’s attitudes that anti-fracking protesters should comply with police. These findings have important implications for future UK energy policy and state legitimacy.