Obtained Correlation Coefficient Research Articles

The Mediating Role of the Use of technology in the Relationship Between Cognitive Flexibility, Social Participation, Purpose in Life, and Successful Aging

Objectives: In recent years, with an increase in the rate of aging around the world, the concept of successful aging and its related factors have been considered. The purpose of the current study is to investigate the mediating role of using technology in cognitive flexibility, social participation and purpose in life in achieving successful aging. Methods and Materials: The research design of this study is descriptive-correlational study. A number of 409 elderly people over 60 years participated by convenience sampling method and based on the inclusion criteria. Data was collected through the Successful Aging Scale (SAS), Cognitive Flexibility Inventory (CFI), the Social Participation Scale, the Purpose in Life Scale, and the Technology Questionnaire. The data was analyzed using a path analysis correlation test using a bootstrapping method using AMOS 26 software. Findings: In this study, 53.5% of the participants were men and 46.5% were women, of whom 44.7% were between 60 and 65 years old and 3.7% were over 81 years old. The results showed that all the indicators of the model are in the optimal range and the model has a good fit with the data. The indicators of CFI=0.96, GFI=0.93, NFI=0.90 and RMSEA=0.075 have been reported. Also, the obtained results showed that all the obtained correlation coefficients were significant at the level of p<0.05 or p<0.01. Therefore, the proposed model showed that cognitive flexibility, social participation and purpose in life had a positive and significant relationship with successful aging through the mediation of technology use (indirectly). Conclusion: This study indicated that the use of technology facilitates the achievement of successful aging. These results can draw the attention of geriatrics specialists and technology experts to the need to improve access to compatible technology for elderly's conditions.

Correlation between productivity traits and protein percentage in pea seeds in the conditions of Donbass

The solution to the most urgent problem of stable and smooth production of vegetable protein in the agro industrial complex of Donbass is practically impossible without introducing new pea varieties into production, the main legume of the region. The main tasks set by the breeders are to improve productivity stability over the years and the overall productivity potential by developing and introducing new, more environmentally flexible and adapted to specific growing conditions genotypes into production. In this regard, there has been given a special role to improving a number of quantitative traits that directly or indirectly affect the overall productivity of plants. The purpose of the current research was to identify the closeness and direction of the correlation of traits that determine productivity and protein percentage in pea seeds, their stability and variability depending on weather changes during a vegetation period. The weather conditions of 2022–2024 were very contrasting, which allowed evaluating the significance of each productivity element in more detail. There has been found that close correlations between seed weight per plant and a number of seeds per plant (r = 0.79***…0.83***), as well as a number of beans per plant (r = 0.69**…0.80***), between a number of beans and seeds per plant (r = 0.79***…0.85***) were studied in relatively favorable years (2022 and 2023). Identification of valuable breeding material by indirect traits in extreme years (2024), when the HTC < 0.2, was significantly complicated. All the obtained correlation coefficients under these conditions were low and unreliable. The results of the correlation of protein percentage in pea seeds with yield structure elements did not reveal a clear correlation between these traits. The correlation between protein percentage in pea seeds and yield structure elements has not been identified due to low and unreliable correlation coefficients, which allows directing pea breeding towards increasing protein percentage in seeds without taking into account the effect of seed productivity.

Deformation mechanism and resultant microstructural characteristics in extruded dilute Mg-0.1Bi-0.1Sn alloy with bimodal-sized grain structure under compression

In this study, we examine in detail the hot deformation mechanism and dynamic recrystallization (DRX) behavior in a novel precipitate-free Mg-0.1Bi-0.1Sn alloy during hot compression conducted at 200–350 °C, with strain rate of 0.33–10 s−1. The obtained correlation coefficient is 0.98024, indicating that the present constitutive equation can adequately predict the hot deformation behavior of the studied alloy. The calculated average activation energy is 180.427 kJ/mol. According to the processing maps, the ideal processing areas correspond to the conditions of 325–350 °C/0.33–1 s−1. Under the low Z deformation condition, the basal slip dominates at the initial and later stages, while the pyramidal slip is dominate at the intermediate stage. Meanwhile, the continuous dynamic recrystallization (CDRX) and discontinuous dynamic recrystallization (DDRX) processes co-occur during hot compression. More specifically, in the initial stage, DDRX dominates over the DRX mechanism, and is accompanied by twinning induced dynamic recrystallization (TDRX). In the intermediate stage, CDRX, TDRX and DDRX prevail as the dominant DRX mechanisms. Throughout the strain stage, DDRX dominates over the DRX mechanism. The occurrence of TDRX and CDRX is beneficial to weakening the texture intensity and promoting the development of CD-tilted texture.

Structural Characteristics of the Pine Stands on Degraded Lands in the South-East of Romania, in the Context of Climate Changes

The present research was carried out in stands of Scots pine and black pine, pure or mixed with deciduous trees, installed on degraded lands from the Curvature Subcarpathian area, Romania, in a representative network of permanent research plots and followed the analysis of the structural diversity and stability indicators of these stands at different ages and in different conditions of degraded lands. The relationships between the quantitative variables with reference to the structure were established by analyzing the significance of the Pearson correlation coefficient (r) and also including datasets of slenderness indexes, which were classed into three domains of vulnerability to abiotic factors (like wind and snow). The compositional diversity of pine stands (pure or mixed with deciduous ones) is different in relation to age and is correlated with the structural diversity. The obtained correlation coefficients (r Pearson) express very strong and significant relationships between biometric parameters (h x Dbh, h x Lc%, Dc x Dbh, and Lc% x Dbh) of the structural diversity (r = 0.800–0.930), which is important for the analysis of the stability and vulnerability of pine forests. The strong correlation between the analyzed variables expresses a weak vulnerability to the action of harmful abiotic factors and the increase in the stability and resilience of the studied stands, especially of over 50 years old. In the old pine stands, the low-vulnerability domain (I < 0.80) is the best represented one, with an average of 64.01% from the total number of trees. At this age, trees with DBH > 22 cm fall into the low-vulnerability category. The explanation is that the stands were affected in their youth by the action of snow and wind, which, combined with the silvotechnical works performed, led to their compositional and structural diversification and increased stability. The young (<45 years) and pure-pine stands with higher consistency (>0.8) and even-aged structure are the most vulnerable to abiotic factors due to the fact that a large number of trees are passing gradually into the higher cenotic classes.

Integrated Wearable System for Monitoring Skeletal Muscle Force of Lower Extremities.

Continuous monitoring of lower extremity muscles is necessary, as the muscles support many human daily activities, such as maintaining balance, standing, walking, running, and jumping. However, conventional electromyography and physiological cross-sectional area methods inherently encounter obstacles when acquiring precise and real-time data pertaining to human bodies, with a notable lack of consideration for user comfort. Benefitting from the fast development of various fabric-based sensors, this paper addresses these current issues by designing an integrated smart compression stocking system, which includes compression garments, fabric-embedded capacitive pressure sensors, an edge control unit, a user mobile application, and cloud backend. The pipeline architecture design and component selection are discussed in detail to illustrate a comprehensive user-centered STIMES design. Twelve healthy young individuals were recruited for clinical experiments to perform maximum voluntary isometric ankle plantarflexion contractions. All data were simultaneously collected through the integrated smart compression stocking system and a muscle force measurement system (Humac NORM, software version HUMAC2015). The obtained correlation coefficients above 0.92 indicated high linear relationships between the muscle torque and the proposed system readout. Two-way ANOVA analysis further stressed that different ankle angles (p = 0.055) had more important effects on the results than different subjects (p = 0.290). Hence, the integrated smart compression stocking system can be used to monitor the muscle force of the lower extremities in isometric mode.

Intensity-Duration-Frequency Curves for Manicaragua city, Cuba

The Intensity-Duration-Frequency (IDF) curves are a way to visualize and represent extreme hydrometeorological rainfall events. In this article, an analysis of convective rainfall events recorded at the La Piedra Meteorological Station, Villa Clara, Cuba, was conducted. To develop IDF curves, the 2006- 2019 time series was analyzed. A partial duration series was generated, including intervals from 20 minutes to 4320 minutes, subjected to an outlier detection process. The series was divided into two categories: one for durations ≤ 720 minutes and another for durations > 720 minutes. The resulting series underwent nonparametric tests to assess their independence, randomness, homogeneity, and seasonality. Subsequently, they were fitted to the Generalized Pareto probability distribution and to a parametric equation of the Montana model, and then the curves were plotted for return periods of 10, 50 and 100. The Montana model led to obtaining correlation coefficients greater than 0.90 compared to the other methods used, significantly improving the quality of the fit in both categories. This research provides information to understand and plan the management of intense climatic phenomena and adequate risk management in an area where such studies are lacking, facilitating access to crucial data essential in the design and execution of hydraulic engineering projects in the region.

School Occupational Health and Safety Performance Scale: Validity and Reliability Study for Scale Development

The aim of this study is to develope a scale to evaluate the performance of occupational health and safety practices in schools. The form created for this purpose was presented to experts and the content validity rates of the items were determined in line with the feedback received. The items with a content validity ratio below .80 were excluded from the study. The 49-item trial form was applied via e-mail to 990 teachers working at primary, secondary, and high school levels in public and private schools in İstanbul, affiliated with the Ministry of National Education, and the data obtained were analyzed. With exploratory Factor Analysis (EFA), a two-dimensional structure consisting of 40 items in total revealed "Occupational Health and Safety Training Practices Applied in Schools" and "Occupational Health and Safety Practices Applied in Schools, Managerial Precautions and Precautions". It was determined that the goodness of-fit indices of the model were quite high. Confirmatory Factor Analysis (CFA) also confirmed the EFA results. The obtained correlation coefficients were examined. Item-total score correlation coefficients were between r = .878 and r = .650, and a significant relationship was observed at the p < .01 level. The internal consistency coefficient obtained for the entire scale was .92. There was a significant (p< 0.05) difference between the items and the total score between the 27% lower and 27% upper groups. It was determined that this difference was in favor of the upper 27% group. There was high reliability in the items on the scale, and the items were distinguishable on the basis of the characteristics the scorers of the scale were looking for.

Trace level quantification of N-nitrosorasagiline in rasagiline tablets by LC-TQ-MS/MS

Parkinson's disease is a chronic, progressive neurological disease that currently affects about more than 10 million population worldwide. Rasagiline is a selective, irreversible monoamine oxidase type B inhibitor used as monotherapy in early Parkinson's disease. Rasagiline tablets have been recalled from market due to the presence of unacceptable levels of nitrosamine impurity. European Medical Agency has set up very stringent limit 100ng/day of N-nitrosorasagiline (NSRG) in drug product based on its mutagenicity. The analytical methods need to be sufficiently sensitive in order to adequately detect and quantify trace levels of NSRG. A highly sensitive LC-MS/MS method for determination of NSRG in rasagiline tablet formulation was developed by effectively separating on zorbax eclipse XDB C18 column using 0.1% formic acid in mixture of water and acetonitrile (35:65 v/v) in an isocratic mode at 0.5mL/min flow rate. The measurement of NSRG was performed using triple quadrupole mass detection accompanied by electrospray ionization in the multiple reaction monitoring mode. The validation of the method was comprehensive, demonstrating strong linearity across the concentration spectrum of 2 to 200ng/mL for NSRG. The obtained correlation coefficient exceeded 0.998, signifying a robust relationship. Recoveries spanning from 80.0% to 120.0% for NSRG were deemed satisfactory. The developed method was able to detect and quantitate NSRG at a concentration level of 1 to 2ng/mL respectively (1 to 2ppm with respect to 1mg/mL of rasagiline tablet sample concentration). The developed and validated method can be employed for routine quality control testing of rasagiline tablets.

UIO-66-NH2 encapsulated polyoxometalate loaded on graphene oxide accommodated on mixed cellulose esters (MCE) paper for thin film microextraction of pesticides besides their quantification via high-performance liquid chromatography-ultraviolet detection

A POM@UIO-66-NH2/GO composite was designed and prepared via a facile in-situ synthesis method for microextraction purposes. The proper sizes (window size and pore size) of UIO-66-NH2 can effectively seal POM as a monomolecular active component and prevent POM leaching during the extraction. After that, a membrane containing POM@UIO-66-NH2/GO nanocomposite was synthesized and utilized in thin film microextraction (TFME) of various pesticides such as imidacloprid, tebuconazole, and propiconazole as the model analytes in different real samples such as cucumber, tomato, potato, head lettuce, and strawberry. It is worth mentioning that the introduction of GO can improve the electron transfer of the sorbent. The extracted model analytes were desorbed by a suitable organic solvent. The extracted analytes were quantified via high-performance liquid chromatography-ultraviolet detection (HPLC-UV). Under the optimized condition, the method's linear dynamic range (LDR) was obtained in the range of 1.0–200.0 μg L−1 for testing analytes. The obtained correlation coefficients (r2) were higher than 0.9918. The limits of detection (LODs) were also calculated to be between 0.31 and 0.34 μg L−1. The relative standard deviations (%RSDs) were obtained in the range of 2.06 to 4.11. The enrichment factor (EF) values for the studied analytes were also obtained in the range of 89–102. The obtained recoveries indicated that the presented method is useful for analyzing pesticides in various real samples.

Obstetric Violence and its Impact on the Mental Well-being of Ecuadorian women: A Quantitative Approach

Obstetric violence refers to certain practices and behaviors carried out by health professionals towards women that belittle, violate and oppress them during pregnancy, childbirth and puerperium, whether in public or private settings. This type of violence is interpreted as a form of gender discrimination and represents a violation of human rights that has a direct impact on women's mental well-being. It manifests itself mainly as an inequality of power between healthcare professionals and pregnant women, whether during childbirth or the puerperium. The World Health Organization has stressed the importance of eliminating these harmful medical practices, urging medical personnel to react appropriately through dialogue and support for pregnant women. A nationally representative sample of women aged 12 years and older from the 2018 National Health and Nutrition Survey was used. Multicollinearity tests were used to avoid redundant information in the models. Next, we used factor analysis to generate an index of obstetric violence and performed linear regressions to observe the correlation between obstetric violence and mental well-being of women in our sample. Finally, we addressed the heterogeneity observed in the proposed relationship by using quantile regression techniques to disentangle the heterogeneous relationship in the distribution of mental well-being and obtain correlation coefficients, with their 95% confidence intervals (95% CI). Based on our findings, we highlight the urgent need to raise awareness and sensitize health professionals about this specific problem, since our main result indicates that there is a misleading correlation between obstetric violence and women's mental well-being. It is crucial to modify those health practices that violate women's rights. Another significant measure to improve the current situation would be the implementation of programs that give visibility to gender violence in the health sector, as well as the promotion of research focused on obstetric violence and the implementation of interventions that strengthen the autonomy of users.

Spanish validation of the simple lifestyle indicator questionnaire: validity and reliability analysis.

It has been shown that lifestyle is a highly modifiable determinant having a direct effect on the health status. Therefore, short and simple questionnaires assessing the lifestyle of the general and clinical population are needed to create interventions on behavioral aspects that can improve the health status. The Simple Lifestyle Indicator Questionnaire (SLIQ) is a validated health scale in English that combines five lifestyle factors: diet, exercise, alcohol consumption, tobacco use, and stress level. The objective of this study was to validate the SLIQ questionnaire in Spanish by analyzing the scale's validity and reliability. Its discriminatory power of the scale was also examined by evaluating the differences in health outcomes according to the levels of adherence to a healthy lifestyle. The sample consisted of 745 participants with an average age of 39.94 (SD: 16.99). A transcultural adaptation process was carried out to validate the SLIQ questionnaire in the Spanish context, to determinate the structural equivalence of the Spanish version as compared to the English version, and to assess the psychometric properties of the scale. PREDIMED and IPAQ scales were used to analyze the convergent validity of the Spanish version of the SLIQ regarding to diet and exercise, and the questionnaires SF-12 and DASS-21 questionnaires were used to assess the capacity of the Spanish version of the SLIQ to discriminate health status related to different levels of reported lifestyles. Regarding validity, the results indicate significant correlations between the different dimensions of the SLIQ questionnaire and those used as a reference. As for reliability, the test-retest analyses reveal a high temporal consistency for the scores obtained on the questionnaire. Finally, the differences found in anxiety, depression, and quality of life, with regard to the different levels of adherence in the SLIQ questionnaire, suggest that the questionnaire's Spanish version has adequate discriminatory power. The obtained correlation coefficients between the SLIQ and the other standardized measures pointed out the adequate convergent validity of the instrument. Moreover, the test-retest results demonstrated the stability of the results obtained through this questionnaire. Finally, the lifestyle categories derived from the SLIQ showed a high ability to discriminate between participants' health profiles. Hence, it can be concluded that the Spanish version of the SLIQ questionnaire is a valid and reliable tool for the quick and effective assessment of lifestyle.

Application of machine learning in developing a quantitative structure-property relationship model for predicting the thermal decomposition temperature of nitrogen-rich energetic ionic salts.

While thermal decomposition temperature (T d) is one of the most important indexes for energetic materials, the most common way of determining and evaluating T d requires laboratory experiments that are complicated, time-consuming and expensive. In the present study, the quantitative structure-property relationship (QSPR) model of T d for 21 nitrogen-rich energetic ionic salts was built and used for T d prediction through 13 descriptors and principal component analysis. The relatively small dataset of 21 samples may lead to overfitting. In the case of small datasets, possible overfitting was reduced by the support vector machine to derive the non-linear QSPR model. The obtained correlation coefficient (R 2) of 96.31% and root-mean-square error (RMSE) of 15.72 indicate the relative reliability of the QSPR model developed in this work. Moreover, T d values of 6 newly designed nitrogen-rich energetic ionic salts were predicted using the new QSPR model. The predicted T d values range from 194 to 225 °C, which are better than that of 4-amino-3,5-dinitro-1H-pyrazole (LLM-116: 178 °C), and no. 1, 2 and 5 are comparable to that of the traditional explosive 1,3,5-trinitro-1,3,5-triazinane (RDX: 230 °C), indicating the excellent properties of the designed energetic ionic salts, which can be used for the preparation of potential energetic materials.

Trace level detection of melamine and cyanuric acid extracted from pet liquid food (milk) using a SERS Au nanogap substrate

This study reported an application of Au nanogap substrates for surface-enhanced Raman scattering (SERS) measurements to quantitatively analyze melamine and its derivative products at trace levels in pet liquid food (milk) combined with a waveband selection approach, namely variable importance in projection (VIP). Six different concentrations of melamine, cyanuric acid, and melamine combined with cyanuric acid were created, and SERS spectra were acquired from 550 to 1620cm−1. Detection was possible up to 200 pM for melamine-contaminated samples, and 400 pM concentration detection for other two groups. The VIP-PLSR models obtained correlation coefficient (R2) values of 0.997, 0.985, and 0.981, with root mean square error of prediction (RMSEP) values of 18.492 pM, 19.777 pM, and 15.124 pM for prediction datasets. Additionally, partial least square discriminant analysis (PLS-DA) was used to classify both pure and different concentrations of spiked samples. The results showed that the maximum classification accuracy for melamine was 100%, for cyanuric acid it was 96%, and for melamine coupled with cyanuric acid it was 95%. The results obtained clearly demonstrated that the Au nanogap substrate offers low-concentration, rapid, and efficient detection of hazardous additive chemicals in pet consuming liquid food.

A SHORTENED VERSION OF THE ACHIEVEMENT MOTIVE SCALE – MOP-20

This research was focused on reducing the scale measuring the general achievement motive MOP2002. The initial version of the instrument MOP2002 contains 55 items and is in the Likert- type scale format. The starting point of this research was based on the assumption about a four-factor structure of the general achievement motive established in our previous research. Both quantitative and qualitative studies were applied in the process of reduction. Each of these studies was conducted on a separate sample – exploratory factor analysis N = 2846, and confirmatory factor analysis N = 294 – and four focus groups, each including 15 respondents. Based on the obtained results, 20 items were selected, and they comprise a shortened version of the scale called MOP20. Designed in this way, the scale can also take the form of the observation protocol while assessing the general achievement motive. It was ascertained that the instrument had a unique object of measurement. The factor analyses results showed the stability of the four-factor structure of the achievement motive. The parameters of representativeness, reliability and homogeneity indicate that MOP20 possesses satisfactory psychometric properties. Instrument validity was checked by means of correlational analysis of the general achievement motive and dimensions of time perspective, self-efficacy and locus of control. The obtained correlation coefficients indicate a satisfactory validity of the scale MOP20, and are highly interpretable when explaining and considering the psychological space of the factors significant for achieving success in activities such as sports and entrepreneurship.

Stability Improvement of the TDLAS-Based CO Monitoring Module in a Coal Mine by Using a Spectral Denoising Algorithm Based on SVR

CO gas is not only lethal but also a significant forecasting indicator for the spontaneous combustion of coal mines. It is imperative that monitoring modules for CO gas that work well in the coal mine environment are available. A feasible solution is the detection of CO by using monitoring modules based on tunable diode laser absorption spectroscopy (TDLAS) over a mid-infrared waveband near 4.6 μm. However, in most cases, the mid-infrared TDLAS-based CO monitoring module tends to introduce severe interference fringe noise into the TDLAS spectral backgrounds which is difficult to filter out using traditional spectral filtering methods, reducing the detection performance of the module. In order to filter out the noise and improve the stability of the module in complex coal mine environments, this work proposed an algorithm based on support vector regression (SVR) to extract the TDLAS spectral backgrounds. Spectral analysis indicates that the TDLAS spectral background can be predicted over the entire scanning spectrum range by using this algorithm, and the noise in the spectral background can be effectively filtered out when calculating the absorbance spectrum based on the Lambert–Beer law. Compared to extracting spectral backgrounds using the traditional least square polynomial fit, the obtained correlation coefficients between regression models of spectral backgrounds and corresponding training point datasets were increased from below 0.998 to above 0.999. The peak-to-peak value of the obtained N2 absorbance spectrum was suppressed below 0.022 from nearly 0.045. The signal-to-noise ratio of the obtained 25 ppm CO absorbance spectrum was increased to 13.35 from 6.95. A CO monitoring module polluted by dust was used to conduct experiments to further test the SVR-based algorithm. The experiment results showed that after programming the SVR-based algorithm to the module, the estimated limit of detection of the module was reduced to 5.46 ppm from 29.08 ppm, and all the absolute measuring errors of the standard CO gases with different low concentrations were reduced to less than 4 ppm from a majority of the errors of more than 10 ppm, compared to least square polynomial fit. The CO monitoring module could still maintain the performance of high-precision quantitative detection when using the SVR-based algorithm even if it had been polluted severely. So, the CO monitoring module has good adaptability to harsh field environments, and its operation stability can be effectively improved by using the algorithm proposed in this work.

THE INFLUENCE OF COMPETENCE AND REWARD PROVISION ON EMPLOYEE PERFORMANCE EFFECTIVENESS

Employee performance is very necessary in working within the company, the importance of an employee's performance can affect the results of the work he does and also affect the goals of the company to be achieved. This study aims to analyze the effect of competence and rewards on the effectiveness of employee performance. The form of research used in this research is quantitative research with an approach. The research method used in this research is a quantitative research method with an association approach, the population in this study are PTPN III Medan HR employees with a total of 35 respondents, The primary data used was obtained by distributing questionnaires directly while the secondary data was obtained through a literature study. The data analysis method used is instrument test, classical assumption test, multiple linear regression analysis, and hypothesis testing. The results showed that competency and reward had an effect on Employee Performance Effectiveness and the results of hypothesis testing simultaneously, obtained correlation coefficient values (R) of .888, which means that there is a relationship between Competency and Reward variables on Performance Effectiveness of 88.8% so that the relationship between these variables can be categorized as very close. While the Adjusted R Square value is .775 or the value of the determinant coefficient shows that the Competency and Reward variables on Performance Effectiveness are 77.5%, while the remaining 22.5% is influenced by other factors in outside of this research.

Kinetic modelling for COD and nitrate-N removal from hatchery wastewater through biological approach

This study was conducted to determine the nutrient removal efficiency via the application of mixed cultures from the synthetic hatchery wastewater based on the first-order, second-order and Stover Kincannon models. The synthetic wastewater was prepared according to the characterization of the collected hatchery wastewater sample, and the collected mixed cultures from the pond sediment were acclimatized accordingly. The samples were tested for chemical oxygen demand (COD) and nitrate-N concentration using a Hach spectrophotometer to determine the removal value of the nutrients. The findings show that the highest removal percentage for COD was up to 31.35% on day 3. Meanwhile, the highest removal percentage for nitrate-N was obtained on day 4 at 43.48%. The obtained correlation coefficient, for COD through first-order, second-order, and Stover Kincannon models is 1, 0.6774, and 0.965, respectively, suggesting that the kinetics of COD removal can be described most properly by the first-order model. A similar model was also reported for nitrate-N with value of 1, 0.7563, and 0.8693 for the first-order, second-order, and Stover Kincannon models, respectively. Based on the findings, the acclimatized mixed culture used in this study could be a potent natural COD and nitrate-N removal in the hatchery wastewater.

Soft computing approaches for prediction of specific heat capacity of hybrid nanofluids

AbstractNanofluids are thermoelectric substance with a greater thermal transmission effectiveness than traditional thermal fluids. Nanofluid's specific heat capacity (SHC) is a crucial thermophysical parameter since it controls the fluid's heat transfer coefficient. Surprisingly few research investigations have been done on the prognostic modelling of the specific heat capacity of fusion nanofluids, despite the fact that numerous experiments have been conducted on the heat transfer capacitance of blended nanofluids. This study focuses on the use of algorithms based on machine learning procedures (MLP) to estimate the SHC of blended nanofluids. Numerous numbers of hybrid nanofluids are investigated in this investigation. Total of 984 samples of hybrid nanofluids for specific heat capacity has been collected from nine experimental research papers. Various MLP techniques were utilized in this analysis, including extreme boost gradient boost regression (XGB), support vector regression augmented with a genetic algorithm (support vector regression [SVR]‐genetic algorithm [GA]), grid search optimized based gradient boost regression algorithm (GBR) (GBR‐GSO), and voting ensemble procedure (VE). The obtained correlation coefficients of the SVR‐GA, XGB, VE and GBR‐GSO models for the testing dataset are 98.43%, 96.29%, 94.4% and 96.55% respectively. The SVR‐GA model showed a better predictive accuracy relative to other ML models. This SVR‐GA anticipated model could be used for quick and reliable prediction of the SHC of blended nanofluids which reduces the burden associated with experimental measurement possible future work includes applying a machine‐learning strategy to the problem of determining the diffusivity of hybrid nanofluids.

Correlation of satellite-based LAI and actual crop yield

The main objective of this article was to investigate the correlation between actual crop yield and Sentinel-2 Leaf Area Index (LAI) for the further possibility of predict model creating. To do so, the following steps have been done. Step 1 – the dataset of actual crop yield was collected for 2364 fields in Ukraine represented with maize, soy, sunflower, winter wheat, winter rapeseed and winter barley. Step 2 – the dataset of Sentinel-2 LAI was collected for 2016-2018 period according to the actual crop yield available. Step 3 – LAI preprocessing (spatial averaging, temporal interpolation/extrapolation to fill the time series gaps, smoothing time series dynamics, temporal averaging). In order to accomplish the process of filling the gaps for the LAI time series, the regular time series dynamics of LAI with a 1-day interval were created using 4 methods: linear interpolation, spline interpolation, LOCF (Last Observation Carried Forward) and ARIMA (AutoRegressive Integrated Moving Average). The time series smoothing process have been accomplish using the local polynomial regression (LOESS) function with different degrees of smoothing. The LAI dynamics preprocessing step did not strongly affect the improvement of the correlation coefficients. Thus, the smoothing process for the time series LAI dynamics at the 0.1 degree of smoothing according to the LOCF and ARIMA gap-filling methods of improved correlation coefficients by 0.01 on average. Step 4 – actual yield values were related to preprocessed satellite-based LAI (correlation of actual yields and LAI). A strong relationship was not indicated (with averaged by vegetation periods correlation coefficient of 0.4 for maize, 0.52 – soy, 0.39 – sunflower, 0.86 – winter barley, 0.54 – winter rapeseed and 0.5 – winter wheat). Since the reliability of obtained correlation coefficients also depends on how many observed data points were in the sample, the hypothesis test of the "significance of the correlation coefficient" has been performed and shows the significance level of p < 0.05 for all crops except winter barley (there is insufficient evidence to conclude that high correlation coefficient of 0.86 for this crop is significant). The average correlation coefficient for all crops is about 0.5 (p < 0.05) which is considered low/moderate. Thus, an attempt to create a linear crop yield prediction model using only Leaf Area Index (LAI) derived from Sentinel-2 will not be effective (based on the cases considered).

Improving ecological indicators of arid zone deserts through simulation

Ecological indicators, such as soil moisture content and gross primary production (GPP), play important roles in the management of ecosystems and water resources and within climate change research. However, studies on monitoring and simulation of ecological indicators of arid zones remain limited. This study simulated soil moisture (SM), latent heat (LH), and gross primary productivity (GPP) for the arid Yarkant River Basin in China using the High-Resolution Land Data Assimilation System and Deep learning. Simulation of hydrology over a large spatial scale is difficult due to a lack of observed data and the difficulty in parameterizing models to represent complicated ecological mechanisms. A comparison of Noah Multi-Parameterization (Noah-MP) simulations to multiple datasets at an annual scale obtained correlation coefficients exceeding 0.8. The model was able to replicate the broad temporal dynamics of GPP and LH over the Yarkant River Basin. Point- and regional-scale assessments across the Yarkant River Basin obtained the optimal model performance. The temporal trends in simulated SM anomalies were consistent with observations across the different sub-basins. The simulations of the Noah-MP model driven by Global Land Data Assimilation System (GLDAS) and ERA5 data provided accurate representations of most hydrological variables, except for in the upper reaches of Yarkant River Basin, likely due to the extensive freeze-thaw activity in this transitional region. The results of this study can fill hydrological data gaps in arid zones areas.