Regression Coefficients Research Articles

Effect of exogenous γ-aminobutyric acid on physiological property, antioxidant activity, and cadmium uptake of quinoa seedlings under cadmium stress.

Increasing cadmium (Cd) pollution has negative effects on quinoa growth and production. Gamma-aminobutyric acid (GABA) confers plants with stress resistance to heavy metals; however, the mechanism remains unclear. We explored the effects of exogenous GABA on the physiological characteristics, antioxidant capacity, and Cd accumulation of quinoa seedlings under Cd stress using hydroponic experiments. Partial least-squares regression was used to identify key physical and chemical indices of seedlings affecting Cd accumulation. Compared with those of the CK group, exposure to 10 and 25 µmol·L-1 Cd significantly reduced the photosynthetic pigment contents, photosynthesis, and biomass accumulation of quinoa seedlings; resulted in shorter and thicker roots; decreased the length of the lateral roots; decreased the activities of superoxide dismutase (SOD) and peroxide (POD); and increased H2O2 and malondialdehyde (MDA) contents. Exogenous GABA reduced the Cd content in the stem/leaves and roots of quinoa seedlings under Cd stress by 13.22-21.63% and 7.92-28.32%, decreased Cd accumulation by 5.37-6.71% and 1.91-4.09%, decreased the H2O2 content by 38.21-47.46% and 45.81-55.73%, and decreased the MDA content by 37.65-48.12% and 29.87-32.51%, respectively. GABA addition increased the SOD and POD activities in the roots by 2.78-5.61% and 13.81-18.33%, respectively, under Cd stress. Thus, exogenous GABA can reduce the content and accumulation of Cd in quinoa seedlings by improving the photosynthetic characteristics and antioxidant enzyme activity and reducing the degree of lipid peroxidation in the cell membrane to alleviate the toxic effect of Cd stress on seedling growth.

The circadian syndrome is a predictor for cognition impairment in middle-aged adults: Comparison with the metabolic syndrome.

Circadian syndrome (CircS) is considered a better predictor for cardiovascular disease than the metabolic syndrome (MetS). We aim to examine the associations between CircS and MetS with cognition in Chinese adults. We used the data of 8546 Chinese adults aged ≥40years from the 2011 China Health and Retirement Longitudinal Study. MetS was defined using harmonised criteria. CircS included the components of MetS plus short sleep and depression. The cut-off for CircS was set as ≥4. Global cognitive function was assessed during the face-to-face interview. CircS and MetS had opposite associations with the global cognition score and self-reported poor memory. Compared with individuals without the CircS and MetS, the regression coefficients (95%CI) for global cognition score were -1.02 (-1.71 to -0.34) for CircS alone and 0.52 (0.09 to 0.96) for MetS alone in men; -1.36 (-2.00 to -0.72) for CircS alone and 0.60 (0.15 to 1.06) for MetS alone in women. Having CircS alone was 2.53 times more likely to report poor memory in men (95%CI 1.80-3.55) and 2.08 times more likely in women (95%CI 1.54-2.81). In contrast, having MetS alone was less likely to report poor memory (OR 0.64 (0.49-0.84) in men and 0.65 (0.52-0.81) in women). People with CircS and MetS combined were more likely to have self-reported poor memory. CircS is a strong and better predictor for cognition impairment than MetS in Chinese middle-aged adults. MetS without short sleep and depression is associated with better cognition.

The progression of cognitive impairment and its influencing factors in older adults based on longitudinal item response theory.

Understanding the development of cognitive impairment and its influencing factors in older adults is crucial for formulating early intervention strategies. To identify the early dimensions of cognitive impairment and provide a comprehensive description of the trajectories of cognitive decline in older adults prior to death. Based on the data of 9883 older adults in the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018, a longitudinal item response theory (Longitudinal IRT) model including covariates was applied to estimate the following parameters. The items in which older adults encountered obstacles first had the least difficulty parameters (δ). The earlier the information curve of an item is lifted, the more information it provides in the early stages of cognitive impairment. Regression coefficient (β) represents the relative rate of cognitive decline. The cognitive impairment values estimated from the Longitudinal IRT were fitted to a mixed-effects model to examine cognitive impairment trajectories. 'Draw the figure on B Card' (δ = -0.816) was the most challenging item, followed by 'recalling 'clothes" (δ = 0.348) and 'recalling 'apples" (δ = 0.419), while 'name the 'pen" (δ = 4.402) was the simplest instruction for the old adults. The curves of the items in the recall dimension began to rise in the early stages of cognitive impairment. Cognitive impairment of older adults who were women (β = 0.061), elder (β = 0.111), smokers (β = 0.060), living in rural areas (β = 0.052), not participating in organised social activities (β = 0.092), suffering from hypertension (β = 0.022), hyperglycaemia (β = 0.035), dyslipidaemia (β = 0.314), low education levels (β = 0.128), manual labourers (β = 0.027), and eventual development of dementia (β = 0.212) exhibited a more accelerated progression. These individuals also had poorer cognitive trajectories. Recall is the earliest dimension of cognitive impairment. The subjects who were women, elder, smokers, living in rural areas, not participating in organised social activities, suffering from hypertension, hyperglycaemia, dyslipidaemia, low education, manual labourers, and eventually development of dementia, had a faster descending speed and poorer trajectories.

Effect of interpregnancy interval on the risk of gestational diabetes mellitus during a second pregnancy

BackgroundInterpregnancy interval (IPI) is associated with the risk of GDM in a second pregnancy. However, an optimal IPI is still need to be determined based on the characteristics of the population. This study aimed to analyze the effect of interpregnancy interval (IPI) on the risk of gestational diabetes mellitus (GDM) in the Chinese population.MethodsWe conducted a retrospective cohort study on female participants who had consecutive deliveries at Peking University Shenzhen Hospital from 2013 to 2021. The IPI was categorized into 7 groups and included into the multivariate logistic regression model with other confound factors. Analysis was also stratified based on age of first pregnancy, BMI, and history of GDM. Adjusted OR values (aOR) and 95% confidence intervals (CI) calculated. The regression coefficient of IPI months on GDM prediction risk was analyzed using a linear regression model.ResultsA total of 2,392 participants were enrolled. The IPI of the GDM group was significantly greater than that of the non-GDM group (P < 0.05). Compared with the 18–24 months IPI category, participants with longer IPIs (24–36 months, 36–48 months, 48–60 months, and ≥ 60 months) had a higher risk of GDM (aOR:1.585, 2.381, 2.488, and 2.565; 95% CI: 1.021–2.462, 1.489–3.809, 1.441–4.298, and 1.294–5.087, respectively). For participants aged < 30 years or ≥ 30 years or without GDM history, all longer IPIs (≥ 36 months) were all significantly associated with the GDM risk in the second pregnancy (P < 0.05), while any shorter IPIs (< 18 months) was not significantly associated with GDM risk (P > 0.05). For participants with GDM history, IPI 12–18 months, 24–36 months, 36–48 months, and ≥ 60 months were all significantly associated with the GDM risk (aOR: 2.619, 3.747, 4.356, and 5.373; 95% CI: 1.074–6.386, 1.652–8.499, 1.724–11.005, and 1.078–26.793, respectively), and the slope value of linear regression (0.5161) was significantly higher compared to participants without a history of GDM (0.1891) (F = 284.168, P < 0.001).ConclusionsLong IPI increases the risk of GDM in a second pregnancy, but this risk is independent of maternal age. The risk of developing GDM in a second pregnancy for women with GDM history is more significantly affected by IPI.

Pengaruh Kualitas Pelayanan terhadap Kepuasan Pelanggan Miniso Big Mall Samarinda

This study aims to test whether the Quality of Laundry Service affects Patient Satisfaction in RSUD Kab. Kediri. This study used a quantitative descriptive approach with a causal research design applied to determine the cause-and-effect relationship between the independent variable (Laundry Service Quality) and the dependent variable (Patient Satisfaction). The study population consisted of 224 non-medical employees of Kediri District Hospital. Non-probability sampling techniques with purposive sampling types are used to determine samples based on certain criteria. Using the Slovin formula and the 5% error standard, a sample of 34 employees was obtained. Data were collected using the Likert scale and analyzed to test hypotheses. The results showed that the calculated value for the Laundry Service Quality variable was 7.970, which was greater than the ttable of 2.037, with a significant value of 0.000 which was smaller than 0.050. This shows that the Quality of Laundry Service has a significant effect on Patient Satisfaction, so the hypothesis (Ha) is accepted. The regression coefficient value of 0.563 indicates that an increase of one unit in Laundry Service Quality will increase Patient Satisfaction by 0.563 units, while a decrease of one unit in Laundry Service Quality will decrease Patient Satisfaction by 0.968 units. An R² value of 0.665 indicates that 66.5% of the variation in Patient Satisfaction is explained by Laundry Service Quality, while the remaining 33.5% is influenced by other variables not studied in this study.

Effectiveness of nonpharmaceutical policy interventions in reducing population mobility during the COVID-19 pandemic

Abstract Nonpharmaceutical policy interventions (NPIs) are intended to reduce population mobility in mitigating the spread of COVID-19. This paper evaluates their effect on population mobility during the COVID-19 pandemic. State space models are applied to estimate the time-varying effects of NPI stringency on weekly pedestrian counts from location-based sensors installed before the pandemic. Different models are developed that evaluate compliance with NPIs over time, identify the most effective NPI, and identify regional differences. An efficient parsimonious alternative is proposed for the multivariate Seemingly Unrelated Time Series Equation model if full covariance matrices are of full rank. Kalman filter estimates of the regression coefficients show that NPI stringency initially had a negative effect on population mobility. The effect weakened during the pandemic, suggesting a reduced willingness to comply with regulations. Four of nine NPIs were identified as the most effective. The multivariate model confirmed the findings across federal states. This paper highlights how combining new data sources, routinely collected administrative data, and sound methodology fosters modern policy evaluation.

Motor dysfunction in Parkinson's patients: depression differences in a latent growth model.

This study aims to utilize latent growth model (LGM) to explore the developmental trajectory of motor dysfunction in Parkinson's disease (PD) patients and investigate the relationship between depression and motor dysfunction. Four-year follow-up data from 389 PD patients were collected through the Parkinson's Progression Marker Initiative (PPMI). Firstly, a univariate LGM was employed to examine the developmental trajectory of motor dysfunction in PD patients. Subsequently, depression levels were introduced as covariates into the model, and depression was further treated as a parallel growth latent variable to study the longitudinal relationship between motor dysfunction and depression. In the trajectory analysis of motor dysfunction, the fit indices for the quadratic growth LGM model were χ2 = 7.419, df = 6, CFI = 0.998, TLI = 0.997, SRMR = 0.019, and RMSEA = 0.025, indicating that the growth trend of motor dysfunction follows a quadratic curve rather than a simple linear pattern. Introducing depression symptoms as time-varying covariates to explore their effect on motor dysfunction revealed significant positive correlations (β = 0.383, p = 0.026; β = 0.675, p < 0.001; β = 0.385, p = 0.019; β = 0.415, p = 0.014; β = 0.614, p = 0.003), suggesting that as depression levels increase, motor dysfunction scores also increase. Treating depression as a parallel developmental process in the LGM, the regression coefficients for depression intercept on motor dysfunction intercept, depression slope on motor dysfunction slope, and depression quadratic factor on motor dysfunction quadratic factor were 0.448 (p = 0.046), 1.316 (p = 0.003), and 1.496 (p = 0.038), respectively. These significant regression coefficients indicate a complex relationship between depression and motor dysfunction, involving not only initial level associations but also growth trends over time and possible quadratic effects. This study indicates a quadratic growth trajectory for motor dysfunction in PD, suggesting a continuous increase in severity with a gradual deceleration in growth rate. The relationship between depression and motor dysfunction is complex, involving initial associations, evolving trends over time, and potential quadratic effects. Exacerbation of depressive symptoms may coincide with motor function deterioration.

INFLUENCE OF ADMINISTRATION, SUPERVISION AND CONTROL LOCAL PROPERTY AGAINST SECURITY REGIONAL PROPERTY OF PALEMBANG CITY

This study aims to analyze the effect of administration, supervision and control of regional property on the security of regional property. The subjects in this study were managers of Palembang city property totaling 62 people. The research was conducted in 29 regional apparatus organizations in Palembang City in April-July 2021. Data analysis using multiple linear regression. The results of the multiple linear regression equation obtained the results Y=51.340+0.361X1-0.912X2+0.298X3, showing that if the administrative and regulatory variables are improved, it will have a positive effect on the success of securing regional property, but on the contrary, if the supervisory variable is increased, it will negatively affect the success of securing regional property. Respondents came from 29 regional apparatus organizations consisting of agencies and agencies under the Palembang City Government and by adding variables for regulating regional property.

Ultrasensitive detection and sensitivity mechanism of SO2F2 in SF6 decomposition product based on TiO2-NiS heterojunction

SO2F2 is a typical fault product of SF6 electrical insulation equipment. Detecting SO2F2 can indicate whether the equipment is faulty. SO2F2 is a hazardous gas that can cause harm to the human body. This study reports on the development of the SO2F2 sensor using TiO2-NiS for the first time, and the investigation of its gas sensitivity to SO2F2 in SF6 background and air. The effective construction of the TiO2-NiS heterojunction was characterized via the XRD, SEM, and TEM. The TiO2-NiS heterojunction sensor for SO2F2 has a working temperature of 150°C in both SF6 and air environments with the response and recovery time of 6 s/2 s to 5 ppm SO2F2 in the air and 5 s/6 s in the SF6 environments, respectively. The sensor has a strong linear fit and a detection limit of 100 ppb. The long-term stability, selectivity, resistance to moisture, and responsiveness/recovery are features of this sensor. Furthermore, it was demonstrated that SO2F2 adsorption performance on the TiO2-NiS heterojunction outperformed that of TiO2 and NiS based on first principles, and density of states and differential charge density provided a more detailed description of the interaction between SO2F2 gas and the TiO2-NiS heterojunction. The oxygen competitive adsorption of SO2F2 and lattice oxygen molecules on the surface of TiO2-NiS heterojunction, as well as the interaction between adsorbed F2− on the TiO2-NiS heterojunction surface and SO2F2, may be used to characterise the sensing mechanism in the SF6. At the same time, the prepared SO2F2 sensor can effectively measure the concentration of SO2F2 through hardware, and give early warning when the concentration exceeds the safe value.

Characterizations of two-photon absorption process induced by defects in aluminum nitride using Z-scan method

In this work, we reported two-photon absorption (TPA) measurements for aluminum vacancies in Aluminum nitride single crystals. We measured the linear transmission and identified the defect levels. Using the Z-scan method, we measured the TPA coefficients of the transitions between defect levels from 380 nm to 735 nm. The transition occurs between the aluminum vacancies defect levels. Furthermore, the power dependence shows good linear fitting, confirming the TPA mechanism. These results will be helpful for the design and fabrication of ultra-low loss waveguides and integrated photonics in the ultraviolet spectral range.

Symmetry-Enhanced Fuzzy Logic Analysis in Parallel and Cross-Road Scenarios: Optimizing Direction and Distance Weights for Map Matching

This study addresses the challenges of setting segmentation points in the membership function and determining appropriate weights for different types of information within a fuzzy logic algorithm for map matching. We use linear fitting to derive an empirical formula for setting segmentation points for the information membership function. Furthermore, we evaluate the effects of various weights for direction and distance information in parallel and cross-road scenarios. The research identified the optimal distance that achieves the highest matching accuracy and provided insights into how the weights of connection, direction, and distance information affect this accuracy. The simulations confirmed the critical importance of precise segmentation point settings and weight determinations in enhancing the accuracy of fuzzy logic algorithms for map matching. The results underscore the potency of our tailored parameter-setting strategy and contribute to knowledge of symmetry, offering practical insights for implementing fuzzy logic in map matching with a particular emphasis on the principle of symmetry in algorithm design and information processing.

Design optimization of an assisted living facility to improve summer thermal comfort in warming climates

Assisted living facilities with optimal summer thermal comfort are necessary to ensure improved quality of life for the older individuals in warming climates. Older individuals are exposed to higher mortality risks during periods of excessive heat. This study aims to identify and optimize key design variables for a passive house certified assisted living facility in Belgium with high comfort expectations using EnergyPlus to improve indoor thermal comfort during extreme climates. The sensitivity analysis used the Standard Regression Coefficient method with Latin Hypercube Sampling for eighteen design variables under four categories: layout, envelope, operation, and system. The analysis identified flat roof and external floor insulation as the most influential parameters, whereas lighting gain and cooling setback temperature were the least influential parameters. Furthermore, optimizing the reference building with the most sensitive design variables using a genetic algorithm based on the NSGA-II method found ideal configurations that would ensure minimum thermal discomfort hours during extreme summers. The study findings provide an evidence-based approach for building engineers and designers for early-stage design of assisted living facilities that maintain optimal comfort in mixed humid climates of Europe.

Modeling of heat gain through green roofs utilizing artificial intelligence techniques

Green roofs have recently been popular in buildings for saving energy. A reliable model is required to include green roofs' role in the thermal performance of buildings. This study applies artificial neural networks (ANN) to estimate the buildings’ heat gain through green roofs. By comparing different ANN architectures (feedforward, recurrent, and cascade), the best model to estimate heat flux from four significant design factors (plant height, leaf area index of plant, soil depth, and overall heat transfer coefficient of support layer) is determined. The relevance test clarifies that the overall heat transfer coefficient of the support layer and the leaf area index of the plant have the highest correlation with the target variable. The modeling results prove that the multilayer perceptron (MLP) neural network with 4-5-1 topology and the logarithm sigmoid and liner activation functions in hidden and output layers has the best accuracy for the given task. This model predicts 2700 literature data with the regression coefficient = 0.9999, mean absolute errors = 0.025, and root mean squared errors = 0.035. Our topology-engineered MLP can analyze the effect of design factors on the heat flux through green roofs and help quickly determine its impact on building thermal performance.

Pengaruh media sosial dalam penyampaian informasi terhadap minat berkunjung Kembali di Museum H. M. soeharto

This study aims to determine how much influence social media as a forum for communication and information delivery has on the interest in revisiting HM Soeharto Museum in Yogyakarta. Through a quantitative approach, this study uses a survey method to collect data from randomly selected museum visitors. The population in this study were visitors to HM Soeharto Museum, while the sample was determined on tourists who had visited the museum before and were at least 14 years old. The data collection tool used was a closed questionnaire with a range of 1-5 Likert Scale scores. The results of data analysis showed a significant influence between the use of social media and interest in revisiting HM Soeharto Museum. The regression coefficient and correlation coefficient showed that any increase in social media usage was associated with a significant increase in interest in revisiting the museum. These findings have important implications in the context of museum promotion and marketing as well as cultural tourism activities. Visitors to HM Soeharto Museum are advised to follow their social media accounts to get the latest information. By making good use of social media, visitors can provide feedback to the museum about the experience of visiting. Visitors can also invite friends or relatives to visit together. This can contribute to increasing interest in visiting HM Soeharto Museum again and enriching visitors' experience in exploring Indonesia's historical heritage.

Application of improved glomerular filtration rate estimation by a neural network model in patients with neurogenic lower urinary tract dysfunction.

Previous studies have indicated that creatinine (Cr)-based glomerular filtration rate (GFR) estimating equations - including the new Chronic Kidney Disease Epidemiology creatinine (CKD-EPIcr) equation without race and the estimated glomerular filtration rate (eGFR) equation developed for the Chinese population - displayed suboptimal performance in patients with neurogenic lower urinary tract dysfunction (NLUTD), which limited their clinical application for detecting changes in GFR levels in all cohorts. To develop a neural network model based on multilayer perceptron (MLP) for evaluating GFR in Chinese NLUTD patients, and compare the diagnostic performance with Cr-based multiple linear regression equations for Chinese and the CKD-EPIcr equation without race. Single-center, cross-sectional study of GFR estimation from serum Cr, demographic data, and clinical characteristics in Chinese patients with NLUTD. A total of 204 NLUTD patients, from 27 different geographic regions of China, were selected. A random sample of 141 of these subjects was included in the training sample set, and the remaining 63 patients were included in the testing sample set. The reference GFR (rGFR) was assessed by the technetium-99m-labeled diethylenetriaminepentaacetic acid (99mTc-DTPA) double plasma sample method. A neural network model based on MLP was developed to evaluate GFR in the training sample set, which was then validated in the testing sample set and compared with Cr-based GFR equations. The MLP-based model showed significant performance improvement in evaluating the difference, absolute difference, precision, and accuracy of GFR estimation compared with the Cr-based GFR equations. Additionally, compared with the rGFR, we found that the MLP-based model provided an acceptable level of accuracy (greater than 85%, which was within a 30% deviation from the rGFR). The MLP-based model offered significant advantages in estimating GFR in Chinese NLUTD patients, and its application could be suggested in clinical practice.

Comparison of whole-blood sirolimus concentrations measured by EMIT-based Siemens Viva-ProE® System and LC-MS/MS in Chinese transplant patients

Sirolimus (SRL) is commonly used in transplant patients to prevent organ transplant rejection. The current guidelines recommend to perform SRL therapeutic drug monitoring regularly to improve treatment outcomes and avoid adverse effects. Consequently, a precise and accurate method for determining SRL is crucial in clinical practice. Currently, liquid chromatography–tandem mass spectrometry (LC-MS/MS) and immunoassays have been widely adopted for determining SRL concentrations. However, previous studies have shown that immunoassays exhibit a positive bias compared to LC-MS/MS. As the new updated version of the EMIT-based Viva-E® System (SVPS), this study aims to compare SRL blood concentrations measured by the SVPS and LC-MS/MS. The residual whole-blood samples obtained from transplant patients were simultaneously analyzed using the SVPS and LC-MS/MS, respectively. The correlation between the two assays was analyzed using the linear regression analysis and Deming linear regression. The Pearson correlation coefficient and Intraclass correlation coefficient (ICC) analysis were executed. The Paired Wilcoxon test and Bland-Altman analysis were performed to assess the concordance between the two methods. The SVPS considerably increased SRL concentration value by 46.62 % as compared to the LC-MS/MS method. When SRL concentrations measured by the SVPS were above 4.0 ng/mL, there was no significant difference between the corrected SVPS concentrations after using the Deming linear regression equation, indicating their interchangeability. Given the significant disparities observed between EMIT and LC-MS/MS, it is crucial to indicate the methodology and instruments in both TDM reports and future clinical guidelines. Our study also provides the conversion formulas between the SVPS and LC-MS/MS, which can be applied as a reference for different clinical centers.

Rapid monitoring of the spatial distribution of soil organic matter using unmanned aerial vehicle imaging spectroscopy

ABSTRACT An existing technique for quickly obtaining the spatial distribution data of soil organic matter (SOM) involves combining an unmanned aerial vehicle (UAV) platform and imaging spectroscopy technology; this technique is suitable for precision agricultural management and real-time monitoring. In this study, the average spectral data were first extracted via circular regions of interest (ROIs) of different radii and modelled using the measured SOM to determine the optimal extraction range for the spectral data. Partial least-squares regression (PLSR) and support vector machine (SVM) models were constructed based on the raw spectra, standard normal transform spectra, multiple scattering-corrected spectra, and first-order differential spectra. The difference index, ratio index, and normalized index were calculated and analysed for their correlation with SOM content, and the spectral indices were screened to establish the SOM prediction model. Finally, the accuracy of the model was evaluated, and we selected the optimal model to map the spatial distribution of SOM. The results of our study show that 1) the radius of the ROI significantly affects the model’s accuracy; 2) the accuracy of the nonlinear SVM model is much higher than that of the linear PLSR model; 3) the prediction model based on spectral indices is better than full-band model. This study can serve as a model for the application of UAV imaging spectroscopy technology to field-scale SOM estimations and rapid monitoring, as well as provide technical support for precision agriculture and climate change studies.

Comparative Biological Half-Life of Penthiopyrad and Tebufenpyrad in Angelica Leaves and Establishment of Pre-Harvest Residue Limits (PHRLs).

To prevent pesticides from exceeding maximum residue limits (MRLs) in crops during export and shipment, it is necessary to manage residue levels during the pre-harvest stages. Therefore, the Republic of Korea establishes pre-harvest residue limits (PHRLs) per crop and pesticide. This study was conducted to set PHRLs for penthiopyrad and tebufenpyrad in angelica leaves, where the exceedance rates of MRLs are expected to be high. The LOQ of the analytical method used was 0.01 mg/kg and it demonstrated good linearity, with a correlation coefficient of 0.999 or higher within the quantitation range of 0.005 to 0.5 mg/kg. The recovery and storage stability accuracy values were in the range of 94.5-111.1%, within the acceptable range (70-120%, RSD ≤ 20%). The matrix effect for both pesticides was in the medium-to-strong range, and it did not significantly impact the quantitative results as a matrix-matched calibration method was employed. Using the validated method, residue concentrations of penthiopyrad 20 (%) EC and tebufenpyrad 10 (%) EC were analyzed. Both pesticides exhibited a decreasing residue trend over time. In Fields 1-3 and their integrated results, the biological half-life was within 2.6-4.0 days for penthiopyrad and 3.0-4.2 days for tebufenpyrad. The minimum value of the regression coefficient in the dissipation curve regression equation was selected as the dissipation constant. The selected dissipation constants for penthiopyrad in Fields 1-3 and their integration were 0.1221, 0.2081, 0.2162, and 0.1960. For tebufenpyrad, the dissipation constants were 0.1451, 0.0960, 0.1725, and 0.1600, respectively. The dissipation constant was used to calculate PHRL per field. Following the principles of the PHRL proposal process, residue levels (%) on PHI dates relative to MRLs were calculated, and fields for proposing PHRLs were selected. For penthiopyrad, since the residue level (%) was less than 20%, the PHRL for Field 3 with the largest dissipation constant was proposed. For tebufenpyrad, as the residue level (%) exceeded 80%, the PHRL proposal could not established. It is deemed necessary to reassess the MRL and 'guidelines for safe use' for tebufenpyrad in angelica leaves.

Primary investigation of an innovative method for milk authenticity using two handheld spectrometers and chemometrics: Comparison with portable ATR-FTIR

This study advocates the use of handheld spectrometers, spanning the visible-near-infrared (Vis-NIR, 400–1000 nm) and near-infrared (NIR, 900–1700 nm) ranges, alongside a portable attenuated total reflection-Fourier transform infrared (ATR-FTIR) instrument, for discerning water adulteration in raw bovine milk. Samples with water adulteration levels ranging from 0 % to 30 % underwent direct analysis without additional preparation. Principal component analysis was utilized for differentiation, while quantification was achieved using partial least squares regression (PLSR) and radial basis function-artificial neural network (ANN) models. The ATR-FTIR instrument displayed remarkable performance, achieving regression coefficients of prediction (R2p) of 0.99 and 0.99, with root mean square error of prediction (RMSEP) values of 0.20 % and 0.53 % in PLS and ANN regression models, respectively. Similarly, handheld NIR spectrometer yielded robust results, with R2p values of 0.99 and 0.99 and RMSEP values of 1.28 % and 1.12 % in PLS and ANN models, respectively. Vis-NIR spectrometer also demonstrated commendable performance, with R2p values of 0.92 and 0.94 and RMSEP values of 4.53 % and 4.83 % in PLS and ANN models, respectively. ATR-FTIR showed the best performance followed by handheld NIR and Vis-NIR spectrometers. All instruments can be used potentially for screening and/or quality control in the dairy industry.

Assessment of activated carbon/alginate for the concurrent removal efficiency of paracetamol and caffeine from wastewater in their binary solutions

The increasing amount of pharmaceutical chemicals in wastewater is a concern for both public health and the environment. This research investigates the effectiveness of a new composite material made of alginate-activated carbon in removing caffeine and paracetamol from wastewater simultaneously. The composite material, produced using a simple technique, demonstrates excellent removal rates and exceptional adsorption properties for both medicinal chemicals. The structural stability and integrity of the composite are confirmed through comprehensive characterization methods including scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction, energy dispersive X-ray (EDX), and BET surface area analysis. The study uses batch adsorption experiments to assess the impact of various factors, such as pH, initial concentration, and contact time, on the efficiency of caffeine and paracetamol removal. The highest values for the adsorption process were 89.63% for Paracetamol and 97.32% for Caffeine, achieved under optimal experimental conditions (pH 7, 0.1 g dose of adsorbent at elevated temperature). Additionally, Kinetic studies and thermodynamic parameters such as ΔS°, ΔGº, and ΔH° were calculated, indicating that the adsorption process was endothermic and spontaneous for paracetamol, and exothermic and non-spontaneous for caffeine. Seven non-linear equilibrium isotherm models were utilized to fit the experimental data for paracetamol and caffeine at pH 7 showing maximum adsorption capacities (qmax) of 606.80 and 725.05 mg/g for Paracetamol and Caffeine, respectively with a high regression coefficient (R2) of 0.99 using alginate-activated carbon as an adsorbent, The results demonstrate the potential of alginate-activated carbon as an effective adsorbent for the simultaneous removal of various pharmaceutical pollutants, highlighting the synergistic adsorption effects of the composite material. The study also examines the safety of the composite material, particularly in the context of potential medical applications. The study emphasizes the potential for sustainable and repeated use of the activated carbon/alginate composite, demonstrating that it maintains both its structural integrity and adsorption effectiveness after multiple cycles of use. This study evaluated the suitability of the suggested analytical method using BAGI, a metric with a unique formula. The BAGI is a complementary tool to GAPI, Complex GAPI, AGREE, AGREE prep, and ESA. A major focus is on the practical elements of white analytical chemistry centered around "blue”.