Application of machine learning and deep learning techniques in modeling the associations between air pollution and meteorological parameters in urban areas of tehran metropolis.

Tehran, the most crowded city in Iran, suffers from severe air pollution, particularly during the cold months. This research endeavored to examine the statistical relationships between criteria air pollutants (CO, NO2, SO2, O3, PM10, and PM2.5) and meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), as well as assess and compare the efficacy of six different algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF), gradient boosting machine (GBM), and deep learning (DL)) in modeling pollutants and climatic factors responsible for variations in Tehran's air pollution levels from 2001 to 2021 using R 4.3.2 software. The results of this study showed that O3 was strongly affected by weather conditions, while other pollutants were mainly influenced by each other than by meteorological parameters and more extensive research is required to pinpoint the precise impact of human activity on these pollutant levels in Tehran. Also based on the predictive model performance evaluation and concerning the principle of parsimony, in half of the cases, the MLR outperformed other models, despite its seeming simplicity and principal assumptions dependence. In other situations, the GAM was a good substitute.

Investigation of Rheological Properties of Molten Materials for Dripping Pills Based on Imaging Monitoring.

INTRODUCTION: Rheological properties, as critical material attributes (CMAs) of solid dispersion drugs such as dripping pills, affect the melting, dispersion, and solidification. Therefore, characterization and assessments of rheological properties in the pharmaceutical process are important in enhancing drug stability and bioavailability. OBJECTIVES: The study aimed to develop a method for analyzing the rheology of molten materials, assessing their consistency and how rheological properties affect the dripping process and pills quality. MATERIALS AND METHODS: The rheological behavior of molten materials composed of Ginkgo biloba leaf extract (GBE) and polyethylene glycol (PEG) 4000 was characterized. Batch consistency of molten materials was evaluated. Image monitoring technology was utilized to capture and process images of the droplet formation process. We established the relationship between the rheological properties of molten materials and various attributes. RESULTS: The quality consistency of molten materials was evaluated, with 12 batches showing similarity above 0.8. The MLR models showed strong correlations (R2 > 0.80) between rheological properties and evaluation attributes. The rheological properties, including consistency coefficient, flow index, and viscosity at 80°C, were identified as critical rheological properties of the molten materials. Rheological property differences of molten materials have an impact on the morphology of droplet and quality performance. CONCLUSION: A rheological method was established, enabling quality consistency evaluation of molten materials in dripping pills. This study revealed the influence of rheological properties on droplet formation process and dripping pills quality, providing a reference for researches on material attributes control of other traditional Chinese medicine dripping pills.

Dominant role of soil iron and organic matters in arsenic transfer from soil to plant in a mine area in Hunan Province, Central China.

The transfer of arsenic (As) from soil to plant could be significantly influenced by soil parameters through regulating soil As bioavailability. To distinguish the bioavailable As provided by soil and the As uptaken by plants, herein two different soil bioavailable were defined, namely potential soil bioavailable As (evaluated through the bioavailable fraction of As) and actual soil bioavailable As (assessed through plant bioaccumulation factor, BF, and BFavailable). To identify the dominant soil parameters for the two soil bioavailable As forms, soil and plant samples were collected from a former As mine site. The results showed that the potential bioavailable As only accounted for 1.77 to 11.43% in the sampled soils, while the BF and BFavailable in the sampled vegetables ranged from 0.00 to 1.01 and 0.01 to 17.87, respectively. Despite a similar proportion of As in the residual fraction, soil with higher pH and organic matter (OM) content and lower iron (Fe) content showed a higher potential soil bioavailable As. Correlation analysis indicated a relationship between the soil pH and potential soil bioavailable As (r = 0.543, p < 0.01) and between the soil Fe and actual soil bioavailable As (r = - 0.644, p < 0.05, r = - 0.594, p < 0.05). Stepwise multiple linear regression (SMLR) analysis was employed to identify the dominant soil parameters and showed that soil pH and phosphorus (P) content could be used to predict the potential soil bioavailable As (R2 = 0.69, p < 0.001). On the other hand, soil Fe and OM could be used to predict the actual soil bioavailable As (R2 = 0.18-0.86, p < 0.001-0.015, in different vegetables). These results suggest that different soil parameters affect potential and actual soil bioavailable As. Hence, soil Fe and OM are the most important parameters controlling As transfer from soil to plant in the investigated area.

A novel DRL-guided sparse voxel decoding model for reconstructing perceived images from brain activity.

BACKGROUND: Due to the sparse encoding character of the human visual cortex and the scarcity of paired training samples for {images, fMRIs}, voxel selection is an effective means of reconstructing perceived images from fMRI. However, the existing data-driven voxel selection methods have not achieved satisfactory results. NEW METHOD: Here, a novel deep reinforcement learning-guided sparse voxel (DRL-SV) decoding model is proposed to reconstruct perceived images from fMRI. We innovatively describe voxel selection as a Markov decision process (MDP), training agents to select voxels that are highly involved in specific visual encoding. RESULTS: Experimental results on two public datasets verify the effectiveness of the proposed DRL-SV, which can accurately select voxels highly involved in neural encoding, thereby improving the quality of visual image reconstruction. COMPARISON WITH EXISTING METHODS: We qualitatively and quantitatively compared our results with the state-of-the-art (SOTA) methods, getting better reconstruction results. We compared the proposed DRL-SV with traditional data-driven baseline methods, obtaining sparser voxel selection results, but better reconstruction performance. CONCLUSIONS: DRL-SV can accurately select voxels involved in visual encoding on few-shot, compared to data-driven voxel selection methods. The proposed decoding model provides a new avenue to improving the image reconstruction quality of the primary visual cortex.

A Novel Modeling Optimization Approach for a Seven-Channel Titania Ceramic Membrane in an Oily Wastewater Filtration System Based on Experimentation, Full Factorial Design, and Machine Learning.

This comprehensive study looks at how operational conditions affect the performance of a novel seven-channel titania ceramic ultrafiltration membrane for the treatment of produced water. A full factorial design experiment (23) was conducted to study the effect of the cross-flow operating factors on the membrane permeate flux decline and the overall permeate volume. Eleven experimental runs were performed for three important process operating variables: transmembrane pressure (TMP), crossflow velocity (CFV), and filtration time (FT). Steady final membrane fluxes and permeate volumes were recorded for each experimental run. Under the optimized conditions (1.5 bar, 1 m/s, and 2 h), the membrane performance index demonstrated an oil rejection rate of 99%, a flux of 297 L/m2·h (LMH), a 38% overall initial flux decline, and a total permeate volume of 8.14 L. The regression models used for the steady-state membrane permeate flux decline and overall permeate volume led to the highest goodness of fit to the experimental data with a correlation coefficient of 0.999. A Multiple Linear Regression method and an Artificial Neural Network approach were also employed to model the experimental membrane permeate flux decline and analyze the impact of the operating conditions on membrane performance. The predictions of the Gaussian regression and the Levenberg-Marquardt backpropagation method were validated with a determination coefficient of 99% and a Mean Square Error of 0.07.

Prediction techniques of movie box office using neural networks and emotional mining.

Box office prediction is of great significance for understanding investment risks, class construction, promotion and distribution, and theater scheduling. However, due to the insufficient selection of influencing factors of movie box office, the currently existing prediction model restricts the prediction accuracy. A total of 34 influencing factors in 11 categories, such as heat index, movie types, release date, creators, first-day box office, were selected to study the prediction technology of movie box office. The Word2vec algorithm is used to construct a feature thesaurus for nouns in movie domain; adjectives and verbs with emotional coloring are used to construct an emotional dictionary based on the movie domain; and the TF-IDF algorithm is integrated to calculate the emotional scores of movie comments. A prediction method based on comments and Multivariate Linear Regression (MLR) is designed to analyze the relationship between the influencing factors and the movie box office, which provides an important basis for the prediction of the total box office, and also provides a decision-making reference for the movie industry and the related management departments. Incorporating comments as feature values to improve the accuracy, a prediction model based on comments and Convolutional Neural Network (CNN) is constructed. The results show that the average prediction accuracy of the MLR without comments, Back-Propagation Neural Network (BPNN), and CNN is 63.4%, 68.3%, and 71.9%, respectively, and after integrating the comments, the average prediction accuracy of the MLR and CNN is improved by 16.1% and 11.8%, respectively, and the prediction accuracy is significantly improved.

A cross-sectional study on predictors of patients' tinnitus severity.

Objective: To identify factors that influence the severity of tinnitus via a hierarchical multiple linear regression model. Methods: The study was a retrospective cross-sectional analysis. The study included 331 patients experiencing tinnitus as their primary concern, who visited Shanghai Changzheng Hospital of the Navy Medical University between 2019 and 2021. Data on general health status and disease characteristics were collected from all patients. With their consent, participants underwent audiological evaluatons and completed questionnaires to analyze the characteristics of their tinnitus and the factors influencing its severity. Results: The correlation analysis showed a positive relationship between tinnitus frequency, tinnitus loudness, SAS scores, and PSQI scores with THI scores (P < 0.05) among nine examined variables (gender, handedness, employment status, age, BMI, tinnitus frequency, tinnitus loudness, SAS scores, and PSQI scores). The variables that were extracted from the multiple regression were; for the constant; ß = -51.797, t = -4.484, P < 0.001, variable is significant; for the tinnitus loudness; ß = 0.161, t = 2.604, P < 0.05, variable is significant; for the tinnitus frequency; ß = 0.000, t = 1.269, P = 0.206, variable is not significant; for the SAS scores; ß = 1.310, t = 7.685, P < 0.001, variable is significant; for the PSQI scores; ß = 1.680, t = 5.433, P < 0.001, variable is significant. Therefore, the most accurate model for predicting severity in tinnitus patients is a linear combination of the constant, tinnitus loudness, SAS scores, and PSQI scores, Y(Tinnitus severity) = ß 0 + ß 1 (Tinnitus loudness) + ß 2 (SAS scores) + ß 3 (PSQI scores). ß 0, ß 1, ß 2, and ß 3 are -51.797, 0.161, 1.310 and 1.680, respectively. Conclusion: Tinnitus severity is positively associated with loudness, anxiety levels, and sleep quality. To effectively manage tinnitus in patients, it is essential to promptly identify and address these accompanying factors and related symptoms.

Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.

OBJECTIVES: To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models. MATERIALS AND METHODS: A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared. RESULTS: Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck. CONCLUSIONS: When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods.

Exploration of moisture activated dry granulation for the development of gastroretentive tablets aided by SeDeM diagram.

Moisture activated dry granulation (MADG) is an attractive granulation process. However, only a few works have explored modified drug release achieved by MADG, and to the best of the authors knowledge, none of them have explored gastroretention. The aim of this study was to explore the applicability of MADG process for developing gastroretentive placebo tablets, aided by SeDeM diagram. Floating and swelling capacities have been identified as critical quality attributes (CQAs). After a formulation screening step, the type and concentration of floating matrix formers and of binders were identified as the most relevant critical material attributes (CMAs) to investigate in ten formulations. A multiple linear regression analysis (MLRA) was applied against the factors that were varied to find the design space. An optimized product based on principal component analysis (PCA) results and MLRA was prepared and characterized. The granulate was also assessed by SeDeM. In conclusion, granulates lead to floating tablets with short floating lag time (<2 min), long floating duration (>4 h), and showing good swelling characteristics. The results obtained so far are promising enough to consider MADG as an advantageous granulation method to obtain gastroretentive tablets or even other controlled delivery systems requiring a relatively high content of absorbent materials in their composition.

Toxicity reference values (TRVs) for force health protection: Gap identification and TRV prediction.

The mission of the Force Health Protection (FHP) program of the U.S. Air Force (USAF), sustaining the readiness of warfighters, relies on determinations of acceptable levels of exposure to a wide array of substances that USAF personnel may encounter. In many cases, exposure details are limited or authoritative toxicity reference values (TRVs) are unavailable. To address some of the TRV gaps, we are integrating several approaches to generate health protective exposure guidelines. Descriptions are provided for identification of chemicals of interest for USAF FHP (467 to date), synthesis of multiple TRVs to derive Operational Exposure Limits (OpELs), and strategies for identifying and developing candidate values for provisional OpELs when authoritative TRVs are lacking. Rodent bioassay-derived long-term Derived No Effect Levels (DNELs) for workers were available only for a minority of the substances with occupational TRV gaps (19 of 84). Additional occupational TRV estimation approaches were found to be straightforward to implement: Tier 1 Occupational Exposure Bands, cheminformatics approaches (multiple linear regression and novel nearest-neighbor approaches), and empirical adjustment of short term TRVs. Risk assessors working in similar contexts may benefit from application of the resources referenced and developed in this work.

Toxicity reference values for force health protection: Provisional occupational exposure guidelines.

Force Health Protection programs in the U.S. Air Force endeavor to sustain the operational readiness of the warfighters. We have previously identified hundreds of chemical substances of interest and toxicity reference value (TRV) knowledge gaps that constrain risk based-decision-making for potential exposures. Multiple approaches to occupational TRV estimation were used to generate possible guideline values for 84 compounds (18% of the substances of interest). These candidate TRVs included values from international databases, chemical similarity (nearest neighbor) approaches, empirical adjustments to account for duration differences, quantitative activity relationships, and thresholds of toxicological concern. This present work describes derivation of provisional TRVs from these candidate values. Rodent bioassay-derived long-term worker Derived No-Effect Levels (DNELs) were deemed presumptively the most reliable, but only 19 such DNELs were available for the 84 substances with TRV gaps. In the absence of DNELs, the quality of the approaches and consistency among candidate values were key elements of the weight of evidence used to select the most suitable guideline values. The use of novel nearest-neighbor approaches, empirical adjustment of short term TRVs, and occupational exposure bands were found to be options that would allow occupational TRV estimation with reasonable confidence for nearly all substances evaluated.

Research on the development relationship between safety production indicators and economic and social indicators in China.

In order to study the relationship between China's safety production indicators and economic and social indicators, the development trend of indicator data in the past 20 years was statistically analyzed, and qualitative and quantitative research was conducted using grey relational analysis and multiple linear regression analysis methods. In the past two decades, there has been a significant improvement in the number of deaths, work-related injuries, and occupational patients in China's safety production, and the country's three categories of 14 economic and social indicators have achieved rapid development. Using the grey relation analysis method, the grey correlation degree between the number of deaths, work-related injuries, and occupational patients in China over the past twenty years and 14 economic and social indicators was obtained. The ranking of economic and social indicators that affect the number of deaths, work-related injuries, and occupational patients varies greatly. A multiple linear regression model was established for the number of deaths, work-related injuries, occupational diseases, and 14 economic and social indicators. The rationality of the model was verified from four aspects: R2, F-value, P-value, and deviation between actual and fitted values. Provide guidance for the development of safety production indicators and economic and social indicators in China through research.

Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods.

BACKGROUND: Lung disease is a severe problem in the United States. Despite the decreasing rates of cigarette smoking, chronic obstructive pulmonary disease (COPD) continues to be a health burden in the United States. In this paper, we focus on COPD in the United States from 2016 to 2019. OBJECTIVE: We gathered a diverse set of non-personally identifiable information from public data sources to better understand and predict COPD rates at the core-based statistical area (CBSA) level in the United States. Our objective was to compare linear models with machine learning models to obtain the most accurate and interpretable model of COPD. METHODS: We integrated non-personally identifiable information from multiple Centers for Disease Control and Prevention sources and used them to analyze COPD with different types of methods. We included cigarette smoking, a well-known contributing factor, and race/ethnicity because health disparities among different races and ethnicities in the United States are also well known. The models also included the air quality index, education, employment, and economic variables. We fitted models with both multiple linear regression and machine learning methods. RESULTS: The most accurate multiple linear regression model has variance explained of 81.1%, mean absolute error of 0.591, and symmetric mean absolute percentage error of 9.666. The most accurate machine learning model has variance explained of 85.7%, mean absolute error of 0.456, and symmetric mean absolute percentage error of 6.956. Overall, cigarette smoking and household income are the strongest predictor variables. Moderately strong predictors include education level and unemployment level, as well as American Indian or Alaska Native, Black, and Hispanic population percentages, all measured at the CBSA level. CONCLUSIONS: This research highlights the importance of using diverse data sources as well as multiple methods to understand and predict COPD. The most accurate model was a gradient boosted tree, which captured nonlinearities in a model whose accuracy is superior to the best multiple linear regression. Our interpretable models suggest ways that individual predictor variables can be used in tailored interventions aimed at decreasing COPD rates in specific demographic and ethnographic communities. Gaps in understanding the health impacts of poor air quality, particularly in relation to climate change, suggest a need for further research to design interventions and improve public health.

Which Variables Are Associated with the Magnitude of the Physical Fitness Response in Older Adults? An Analysis of Their Development and Influence.

Regular physical exercise has proven to be an effective strategy for enhancing the health and well-being of older adults. However, there are still gaps in our understanding of the impacts of exercise on older adults with different health conditions, as well as in the customization of training programs according to individual capabilities. This study aimed to analyze the variables that influence the response of physical capabilities in older adults, considering their development over the aging process, with the goal of assisting professionals in creating personalized training programs. To achieve this, we conducted a cohort study involving 562 previously inactive adults and older adults who underwent anthropometric assessments, blood pressure measurements, and comprehensive physical tests. These assessments were conducted before and after a 14-week training program. Results indicated no significant variations in variables such as waist circumference (p = 0.0455, effect size = 0.10), body mass index (p = 0.0215, effect size = 0.15), systolic (p < 0.0001, effect size = 0.35) and diastolic blood pressure (p < 0.0001, effect size = 0.25) pre- and post-intervention. Strength tests, agility, the 6 min walk test (6MWT), and the back scratch test (BS) showed significant improvements post-intervention, with p-values all below 0.0001 and effect sizes ranging from 0.30 to 0.50. Multiple linear regression analyses revealed that lower initial values in physical capabilities were associated with more significant improvements during training (R2 = 0.73, p < 0.001). These results underscore that individualized guidance in training can lead to clinically meaningful improvements in physical performance and health among older adults, with effect sizes indicating moderate-to-large benefits (effect size range = 0.30 to 0.50). Therefore, personalized training programs are essential to maximize health benefits in this population.

Correlation analysis between potential hepatotoxicity and ingredient content of polygoni multiflori caulis.

Polygoni Multiflori Caulis (PMC) is commonly used in clinical practice. While the adverse reactions of Polygoni Multiflori Radix (RPM) are well-known, the potential adverse reactions of PMC are often neglected. This article aims to clarify the relationship between hepatotoxic components in PMC and its various producing areas. This study provides a qualitative and quantitative analysis of PMC from various regions, which can serve as a basis for safe usage.

Predictive modelling of laser powder bed fusion of Fe-based nanocrystalline alloys based on experimental data using multiple linear regression analysis.

This study harnessed bivariate correlational analysis, multiple linear regression analysis and tree-based regression analysis to examine the relationship between laser process parameters and the final material properties (bulk density, saturation magnetization (M s ), and coercivity (H c )) of Fe-based nano-crystalline alloys fabricated via laser powder bed fusion (LPBF). A dataset comprising of 162 experimental data points served as the foundation for the investigation. Each data point encompassed five independent variables: laser power (P), laser scan speed (v), hatch spacing (h), layer thickness (t), and energy density (E), along with three dependent variables: bulk density, M s , and H c . The bivariate correlational analysis unveiled that bulk density exhibited a significant correlation with P, v, h, and E, whereas M s and H c displayed significant correlations exclusively with v and P, respectively. This divergence may stem from the strong influence of microstructure on magnetic properties, which can be impacted not only by the laser process parameters explored in this study but also by other factors such as oxygen levels within the build chamber. Furthermore, our statistical analysis revealed that bulk density increased with rising P, h, and E, while decreased with higher v. Regarding the magnetic properties, a high M s was achievable through low v, while low H c resulted from high P. It was concluded that P and v were considered as the primary laser process parameters, influencing h and t due to their control over the melt-pool size. The application of multiple linear regression analysis allowed the prediction of the bulk density by using both laser process parameters and energy density. This approach offered a valuable alternative to time-consuming and costly trial-and-error experiments, yielding a low error of less than 1 % between the mean predicted and experimental values. Although a slightly higher error of approximately 6 % was observed for M s , a clear association was established between M s and v, with lower v values corresponding to higher M s values. Additionally, a further comparison was conducted between multiple linear regression and three tree-based regression models to explore the effectiveness of these approaches.

[Analysis of Heavy Metal Sources in Farmland Soil of Sewage Irrigation and Industrial Complex Area Based on APCS-MLR and PMF].

The contents of eight heavy metals （Cr, Ni, Cu, Zn, Cd, Pb, As, and Hg） were determined based on the surface soil samples of sewage irrigation and industrial complex in Kaifeng City. The absolute factor analysis-multiple linear regression （APCS-MLR） model and positive matrix factorization （PMF） model were used to analyze the sources and contribution rates of heavy metals in soil combined with correlation analysis and systematic cluster analysis. The results showed that： ① The average values of ω（Cr）, ω（Ni）, ω（Cu）, ω（Zn）, ω（Cd）, ω（Pb）, ω（As）, and ω（Hg） in the study area were 52.19, 25.00, 42.03, 323.53, 1.79, 53.45, 9.43, and 0.20 mg·kg-1, respectively, and Cr, Ni, and As are lower than the background values of tidal soil. Cu, Zn, Cd, Pb, and Hg are higher than the background values of the tidal soil. ② There were four sources of the eight heavy metals： natural sources, agricultural sewage irrigation sources, industrial atmospheric sedimentation sources, and transportation sources. Cr and Ni were mainly from natural sources； Cu, Zn, Cd, and Pb were mainly from agricultural sewage irrigation and transportation sources； As was mainly from natural sources and agricultural sewage irrigation； and Hg was mainly from industrial atmospheric sedimentation. ③ The APCS-MLR and PMF source analysis results indicated that industrial and agricultural activities were the main sources of heavy metals in the soil of the study area. The average contribution rates of APCS-MLR in the nine sampling areas of the research area were 76.01% （natural sources and agricultural sewage irrigation sources）, 22.71% （industrial atmospheric sedimentation sources and transportation sources）, and 1.28% （unknown sources）. The average contribution rates of PMF were 59.66% （natural sources and agricultural sewage irrigation sources） and 40.34% （industrial atmospheric sedimentation sources and transportation sources）. The source analysis results of the LZ, XZ, NLT, PT, YLZ, and BC models were basically consistent, and WL was better in the APCS-MLR model, whereas SG and QT were better in the PMF model. The research results can provide a scientific basis for the prevention and control of soil heavy metal pollution and environmental remediation.

Hyperspectral Characteristics and SPAD Estimation of Wheat Leaves under CO2 Microleakage Stress.

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model's accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a "red shift" of the green peak and a "blue shift" of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops.

Resolve the species-specific effects of iron (hydr)oxides on the performance of underlying zerovalent iron for metalloid removal: Identification of their key properties.

Despite the importance of surface iron (hydr)oxides (Fe-(hydr)oxides) for the decontamination performance of zerovalent iron (ZVI) -based technologies has been well recognized, controversial understandings of their exact roles still exist due to the complex species distribution of Fe-(hydr)oxides. Herein, we re-structured the surface of ZVI using eight distinct Fe-(hydr)oxides and analyzed their species-specific effects on the performance of ZVI for Se(IV) under well-controlled conditions. The kinetics-relevant performance indicators (Se(IV) removal rates, Fe2+ release rates, and the utilization ratio of ZVI) under the effect of each Fe-(hydr)oxide roughly followed the order: δ-FeOOH > Fe5HO8·4H2O > α-FeOOH > ß-FeOOH > Î³-FeOOH > Î³-Fe2O3 > Fe3O4 > α-Fe2O3. Multiple linear regression analysis shows that the large pore volume and size (instead of specific surface area), low open-circuit potential, and low electrochemical impedance are key positive properties for kinetics-relevant performance. Besides, for electron efficiency of ZVI, only Fe3O4 increased the value to 50.0%, due to the contribution of its ferrous components, while others did not change it (∼20%). Additional experiments with commercial ZVI covered by individual Fe-(hydr)oxides confirmed the observed species-specific trends. All these results not only provide new basis for mechanism explanation but also have practical implications for the production or modification of ZVI.

The application of fractional calculus in economic growth modelling: An approach based on regression analysis.

This paper establishes a fractional-order economic growth model to model the gross domestic product (GDP). The fractional-order model consists of a differential equation of integer and fractional orders, where the GDP is a function of several exploratory variables. An empirical application is adopted using Malaysia's GDP data from 1956 to 2018, incorporating exploratory variables such as total population, crude death rate, production of logs, gross fixed capital formation, exports of goods and services, general government final consumption expenditure, private final consumption expenditure, and the impact of investment. Extensive comparisons were carried out to evaluate the modelling performance of the full and reduced fractional-order multiple linear regression models with the benchmark models, namely full and reduced integer-order multiple linear regression models. Results indicate that the reduced fractional-order model with six exploratory variables, excluding the crude death rate and production of logs, predominates other models for the in-sample model fitting based on the Akaike information criterion, coefficient of determination and other criteria. Furthermore, the fractional-order model offers the best-of-sample forecasts evaluated based on the root mean square forecast error and mean absolute forecast error. The application of the Diebold-Mariano test also serves to confirm the superior performance of the suggested fractional-order model, revealing a significant difference in forecasting ability between the fractional-order and integer-order models.