Combination Of Variables Research Articles

Estimation of Biochemical Pigment Content in Poplar Leaves Using Proximal Multispectral Imaging and Regression Modeling Combined with Feature Selection.

Monitoring the biochemical pigment contents in individual plants is crucial for assessing their health statuses and physiological states. Fast, low-cost measurements of plants' biochemical traits have become feasible due to advances in multispectral imaging sensors in recent years. This study evaluated the field application of proximal multispectral imaging combined with feature selection and regressive analysis to estimate the biochemical pigment contents of poplar leaves. The combination of 6 spectral bands and 26 vegetation indices (VIs) derived from the multispectral bands was taken as the group of initial variables for regression modeling. Three variable selection algorithms, including the forward selection algorithm with correlation analysis (CORR), recursive feature elimination algorithm (RFE), and sequential forward selection algorithm (SFS), were explored as candidate methods for screening combinations of input variables from the 32 spectral-derived initial variables. Partial least square regression (PLSR) and nonlinear support vector machine regression (SVR) were both applied to estimate total chlorophyll content (Chla+b) and carotenoid content (Car) at the leaf scale. The results show that the nonlinear SVR prediction model based on optimal variable combinations, selected by SFS using multiple scatter correction (MSC) preprocessing data, achieved the best estimation accuracy and stable prediction performance for the leaf pigment content. The Chla+b and Car models developed using the optimal model had R2 and RMSE predictive statistics of 0.849 and 0.825 and 5.116 and 0.869, respectively. This study demonstrates the advantages of using a nonlinear SVR model combined with SFS variable selection to obtain a more reliable estimation model for leaf biochemical pigment content.

Retrieving freeze-thaw states using deep learning with remote sensing data in permafrost landscapes

The soil freeze–thaw (FT) cycle is a critical component of the terrestrial cryosphere and plays a significant role in hydrological, ecological, climatic, and biogeochemical processes within permafrost landscapes. The FT states can be monitored with in-situ field measurements, but these procedures are costly and limited to single chosen sites. Remote sensing data provides the opportunity to collect information repeatedly across extensive geographical areas. To explore a more effective way to monitor the FT states in the terrestrial cryosphere, in this study, we used microwave and optical remote sensing data and introduced the Deep Learning approach to simulate the soil FT states in the western part of Nunavik, Canada.Two networks, Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN), were trained and tested with over 35,000 and approximately 54,000 randomly selected data samples, respectively. The trained CNN networks outperformed the MLP networks, achieving the highest testing accuracy of 95.67% and the highest validation accuracy of 87.28% based on ground truth data from 32 measurement stations from all seasons across the year. This study proposed the reference periods concept for convenient labeling in data preparation and tested different combinations of influence variables to achieve better transferability of the method for future studies. Our findings offer a more effective way to monitor FT states in the terrestrial cryosphere, offering valuable insights into the consequences of climate change on permafrost landscapes. Moreover, the suggested deep learning approach can be easily expanded when additional input sources are accessible. This expansion has the potential to further improve the model's performance for the FT retrieval.

Dual-Polarimetric Radar Applications for Investigating Severe Thunderstorms in Northern Thailand during the Pre-Monsoon Season

Understanding the physical characteristics of severe pre-monsoon thunderstorms is crucial for mitigating the adverse impacts of natural disasters on people in tropical regions. On April 23, 2020, a severe storm affected the Chiang Khong District of the Chiang Rai Province in Northern Thailand, causing devastation to more than 500 houses with hail and strong winds. This study investigated the storm's physical characteristics using dual-polarimetric radar data from the Thai Meteo-rological Department (TMD). Using a combination of polarimetric radar variables, such as reflectivity (ZH), specific differential phase (KDP), differential reflectivity (ZDR), and copolar correlation coefficient (ρhv), fuzzy logic was utilized to classify the hydrometeor types. During the severe storm that affected the Chiang Khong District, hail and large raindrops were mixed in with the rain, according to the analysis. Two severe storms were observed in the unorganized mesoscale convective systems that were analyzed, with the second storm producing more intense effects due to the radar reflectivity shape of a bow echo that generated strong wind gusts. The Doppler radar data retrieved the wind field, indicating the convergence of intense local winds during the storm, which was consistent with the analysis results of synoptic-scale weather systems from ECMWF Reanalysis v5 (ERA5). In addition, a Hovmöller diagram revealed orographic enhancement of the convective cloud as the storm passed through mountainous regions prior to approaching the Chiang Khong District. The findings of this study can provide valuable information for TMD's near-real-time warning operation in order to minimize loss of life and property from future severe storms.

Predictors of Early Hospice or Death in Patients With Inoperable Lung Cancer Treated With Curative Intent

IntroductionTreatment for inoperable stage II to III non-small cell lung cancer (NSCLC) involves chemo-radiotherapy (CRT). However, some patients transition to hospice or die early during their treatment course. We present a model to prognosticate early poor outcomes in NSCLC patients treated with curative-intent CRT. Methods and MaterialsAcross a statewide consortium, data was prospectively collected on stage II to III NSCLC patients who received CRT between 2012 and 2019. Early poor outcomes included hospice enrollment or death within 3 months of completing CRT. Logistic regression models were used to assess predictors in prognostic models. LASSO regression with multiple imputation were used to build a final multivariate model, accounting for missing covariates. ResultsOf the 2267 included patients, 128 experienced early poor outcomes. Mean age was 71 years and 59% received concurrent chemotherapy. The best predictive model, created parsimoniously from statistically significant univariate predictors, included age, ECOG, planning target volume (PTV), mean heart dose, pretreatment lack of energy, and cough. The estimated area under the ROC curve for this multivariable model was 0.71, with a negative predictive value of 95%, specificity of 97%, positive predictive value of 23%, and sensitivity of 16% at a predicted risk threshold of 20%. ConclusionsThis multivariate model identified a combination of clinical variables and patient reported factors that may identify individuals with inoperable NSCLC undergoing curative intent chemo-radiotherapy who are at higher risk for early poor outcomes.

Понятие и содержание частноправового режима

Introduction. As an element of legal regulation, private legal regime is a legal category, the concept of which is regarded ambiguously in doctrine and depends on the general understanding of the category «legal regime». Socio-economic, political changes in society are accompanied by changes in the nature of social relations, requiring proper legal regulation in the area of private interest protection.
 Methods. On the basis of the analysis of definitions of legal regime formulated in the theory of law, a generalised approach to the understanding of private legal regime was developed. The application of formal legal method made it possible to reveal the concept and specific features of the private legal regime. The system-structural method was used to determine the significance of legal means (ways, methods, principles, types of legal regulation) in the structure of private legal regime. The application of modelling enabled to reveal the effectiveness of private legal regulation of public relations.
 Results. The concept and essential features of private legal regime are identified; the model of private legal regime as a variable combination of a complex of legal means determining the orientation of private legal regulation of property and personal non-property relations based on equality, autonomy of will and material independence of their participants is defined.

A machine learning approach to identifying non-parental caregivers' risk for harsh caregiving towards infants in daycare centers

BackgroundHarsh Caregiving behavior amongst daycare providers (i.e., non-parental Harsh Caregiving) negatively impacts children's development across a variety of domains. As prevalences of non-parental Harsh Caregiving appear to increase worldwide, identifying its predictors is crucial for screening and intervention. ObjectiveThe goal of this study was to identify a set of indicators and predictive rules that may accurately predict women's risk for Harsh Caregiving behavior in daycare environments. Participants and SettingThe study recruited 75 female non-parental caregivers, from the general population, who work with infants aged 0-1. Caregivers filled out self-report questionnaires including a Harsh Caregiving measure as well as a broad variety of potential predictors. MethodsTo elucidate combinations of input variables that are predictive of non-parental Harsh Caregiving, we used machine learning Decision Three Inference and CHAID algorithms. ResultsStudy results revealed a predictive model including 27 questions and four different prediction paths. For example, the first path indicated that women who reported low levels of attention deficit and hyperactivity problems and low levels of rigid-negative caregiving philosophies, had 100 % chance to report low levels of Harsh Caregiving behavior. Overall classification accuracy for "High Harsh Caregiving behavior" was 95.2 %. ConclusionsAfter replication in larger samples, the model can be used as a screening tool for women expressing their wish to work with infants. Women at risk can either be declined employment or alternatively receive targeted supervision throughout their work with small infants.

Agricultural subsidies on common prosperity: Evidence from the Chinese social survey

Based on the data from the Chinese Social Survey (CSS) of 13,359 interviewed farm households, the paper systematically evaluates the impact of agricultural subsidies on the farmers' common prosperity by measuring the development of common prosperity from the aspects of “comprehensive” prosperity and “universal” affluence. The research draws the following conclusions: (1) Agricultural subsidies significantly raises the per capita income level of rural households, reduces income inequality between households, contributes to “comprehensive” prosperity and “universal” affluence, and realizes the goal of common prosperity. Also, this paper uses 2SLS, LIML, GMM, replacement and combination of variables, simultaneous quantile regression, and exclusion of outliers to test the robustness of the baseline regression results, which remains robust after a series of tests. (2) The effect of agricultural subsidies on the farmers' common prosperity will show obvious heterogeneity phenomenon due to the differences in the natural conditions, economic development level, policy environment and agricultural business scale of the region, which need to be treated differently. (3) The transfer of agricultural land plays a mediating role. Agricultural subsidies will encourage farmers to enhance the transfer of agricultural land, thereby raising incomes and reducing income inequality, which will help farmers to realize common prosperity. Part-time farming plays a moderating role. The lower the degree of part-time farming, the stronger the effect of agricultural subsidies in promoting the common prosperity of farmers. Our study gives innovative experience and ideas for improving the agricultural subsidy policy system, optimizing the structure of agricultural subsidies, and then using agricultural subsidies to more comprehensively promote common prosperity in rural areas.

Integrated Design of Hydraulic Coupling Bolts for Large Shaft Systems

Hydraulic coupling bolts are used in a wide range of industrial sectors, where ensuring the structural integrity and reliability of shaft systems is of paramount importance. However, research on systematic design methods for these specialized bolts is still lacking. In this study, an integrated design was used on hydraulic coupling bolts with M72 specification, considering both the tightening and untightening processes. First, the second tightening load was determined based on material strength, stress concentration and safety factors. Subsequently, the upper and lower limits of the first tightening loads were calculated using the thick-walled cylinder theory. An appropriate first tightening load maintaining the tightening force in both the axial and radial directions was obtained using finite element analysis. Design variables for the adapter configuration and oil passage shape were selected, and their impact on structural integrity was assessed using finite element analysis. The optimal combination of these design variables was derived. Finally, operational experiments for hydraulic coupling bolts were conducted according to the design results. The test results demonstrated a successful tightening and untightening process, confirming the effectiveness of the proposed design method.

Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand

Energy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy dependency ratio is notably high. The goal of this study is to propose ensemble machine learning methods such as boosting, bagging, blending, and stacking with hyperparameter tuning and k-fold cross-validation, and investigate the application of these methods for predicting Türkiye’s energy demand. This study utilizes population, GDP per capita, imports, and exports as input parameters based on historical data from 1979 to 2021 in Türkiye. Eleven combinations of all predictor variables were analyzed, and the best one was selected. It was observed that a very high correlation exists among population, GDP, imports, exports, and energy demand. In the first phase, the preliminary performance was investigated of 19 different machine learning algorithms using 5-fold cross-validation, and their performance was measured using five different metrics: MSE, RMSE, MAE, R-squared, and MAPE. Secondly, ensemble models were constructed by utilizing individual machine learning algorithms, and the performance of these ensemble models was compared, both with each other and the best-performing individual machine learning algorithm. The analysis of the results revealed that placing Ridge as the meta-learner and using ET, RF, and Ridge as the base learners in the stacking ensemble model yielded the highest R-squared value, which was 0.9882, indicating its superior performance. It is anticipated that the findings of this research can be applied globally and prove valuable for energy policy planning in any country. The results obtained not only highlight the accuracy and effectiveness of the predictive model but also underscore the broader implications of this study within the framework of the United Nations’ Sustainable Development Goals (SDGs).

Reference Evapotranspiration Estimation Using Genetic Algorithm-Optimized Machine Learning Models and Standardized Penman–Monteith Equation in a Highly Advective Environment

Accurate estimation of reference evapotranspiration (ETr) is important for irrigation planning, water resource management, and preserving agricultural and forest habitats. The widely used Penman–Monteith equation (ASCE-PM) estimates ETr across various timescales using ground weather station data. However, discrepancies persist between estimated ETr and measured ETr obtained from weighing lysimeters (ETr-lys), particularly in advective environments. This study assessed different machine learning (ML) models in comparison to ASCE-PM for ETr estimation in highly advective conditions. Various variable combinations, representing both radiation and aerodynamic components, were organized for evaluation. Eleven datasets (DT) were created for the daily timescale, while seven were established for hourly and quarter-hourly timescales. ML models were optimized by a genetic algorithm (GA) and included support vector regression (GA-SVR), random forest (GA-RF), artificial neural networks (GA-ANN), and extreme learning machines (GA-ELM). Meteorological data and direct measurements of well-watered alfalfa grown under reference ET conditions obtained from weighing lysimeters and a nearby weather station in Bushland, Texas (1996–1998), were used for training and testing. Model performance was assessed using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2). ASCE-PM consistently underestimated alfalfa ET across all timescales (above 7.5 mm/day, 0.6 mm/h, and 0.2 mm/h daily, hourly, and quarter-hourly, respectively). On hourly and quarter-hourly timescales, datasets predominantly composed of radiation components or a blend of radiation and aerodynamic components demonstrated superior performance. Conversely, datasets primarily composed of aerodynamic components exhibited enhanced performance on a daily timescale. Overall, GA-ELM outperformed the other models and was thus recommended for ETr estimation at all timescales. The findings emphasize the significance of ML models in accurately estimating ETr across varying temporal resolutions, crucial for effective water management, water resources, and agricultural planning.

Noun Class and Classification in Tshiluba (L.31)

Languages have different ways of counting nouns, involvinga variable combination of pluralisation, combination withnumbers, and quantification. Established cross-linguisticliterature in this field like that of Chierchia (2019) suggestsa tripartite typological division between number-marking(e.g., English), classifier (e.g., Mandarin), and number-neutral (e.g., English) languages. In any case, the literatureargues for certain universals irrespective of type like thedivision of nouns into number-counting (e.g., pieces of meat)and kind-counting (e.g., pork and beef). In comparison withcross-linguistic typology and neighbouring languages likeLingala, Tshiluba does show affinities with the number-marking category with categories like fluid substancesneither able to change class nor combine directly withnumerals. However, there are other affinities with number-neutral languages like in the interpretation of quantifiers forthese fluid mass nouns, in this case a buunyi which can mean“many” bottles of water or “much” water. Ultimately, thetypological system is present but the motivation for it is morediscursive in that there can be countable and uncountableiterations of words like tshi-manu “wall” as opposed tocertain words being inherently (un)countable.

MIXED ESTIMATORS OF TRUNCATED SPLINE-EPANECHNIKOV KERNEL ON NONPARAMETRIC REGRESSION AND ITS APPLICATIONS

Research on innovations in the statistics and statistical computing program systems implemented in the health sector. The development of a mixed estimator model is an innovation of nonparametric regression analysis by combining two approaches in nonparametric regression, namely the truncated spline estimator and the Epanechnikov kernel. The urgency of this study is that there are often cases where there are different data patterns from each predictor variable. In addition, by using only one form of the estimator in estimating a multivariable regression curve, the result is that the estimator obtained will not match the data pattern. The research objective was to find a mixed estimator between the truncated spline and the Epanechnikov kernel and the estimator results were applied to Dengue Hemorrhagic Fever case data. The unit of observation is a province in Indonesia and This study relied on secondary data received from the Central Statistical Agency (BPS) and the Health Office. Based on the analysis results, it was found that the best model of nonparametric regression with a mixed estimator of the truncated spline and Epanechnikov Kernel is a model with 3 knots with a combination of variables. The coefficient of determination (R2) is 98.11%. We can conclude that the mixed estimator tends to follow actual data and represents a nonparametric regression model with a mixed estimator that can predict the number of Dengue Hemorrhagic Fever Cases in Indonesia

Dry sliding wear characteristics of Al7075 alloy‐reinforced with SiC and cenosphere particles

AbstractAluminum metal matrix composite (AMMC) is employed in all engineering applications, most notably as a substitute for conventional metals in the fields of defense, automotive, and aerospace. AMMC should have a wide variety of structural, mechanical, thermal, wear, and corrosion qualities that may require mutually exclusive features at an optimal level to achieve this. The current research focuses on the stir casting process for producing SiC and cenosphere reinforced Al7075 alloys. The response surface method's (RSM) face‐centered central composite design (CCD) was used to design the number of experimental trials, and a response surface technique was enforced to forecast the optimum combination of processing variables in the wear process. The SiC reinforcement improved the wear resistance of Al7075‐SiC‐cenosphere composites substantially. Overall, the experiments show that Al7075‐6 wt% SiC‐5 wt% cenosphere has excellent tribological properties. The hybrid Al7075/SiC/Cenosphere composites were shown to be effective, fit, and suitable in the ideal wear process parameters (load of 10 N, sliding distance of 1000 m, and sliding velocity of 1.5 m/s) for lowering wear rate (0.38035 × 10−3 mm3/m) at 6wt% SiC reinforced composite. This suggests that the combination of SiC and cenosphere improves the wear resistance of AMMC.

Blood circulating bacterial DNA in hospitalized old COVID-19 patients

BackgroundCoronavirus disease COVID-19 is a heterogeneous condition caused by SARS-CoV-2 infection. Generally, it is characterized by interstitial pneumonia that can lead to impaired gas-exchange, acute respiratory failure, and death, although a complex disorder of multi-organ dysfunction has also been described. The pathogenesis is complex, and a variable combination of factors has been described in critically ill patients. COVID-19 is a particular risk for older persons, particularly those with frailty and comorbidities. Blood bacterial DNA has been reported in both physiological and pathological conditions and has been associated with some haematological and laboratory parameters but, to date, no study has characterized it in hospitalized old COVID-19 patients The present study aimed to establish an association between blood bacterial DNA (BB-DNA) and clinical severity in old COVID-19 patients.ResultsBB-DNA levels were determined, by quantitative real-time PCRs targeting the 16S rRNA gene, in 149 hospitalized older patients (age range 65–99 years) with COVID-19. Clinical data, including symptoms and signs of infection, frailty status, and comorbidities, were assessed. BB-DNA was increased in deceased patients compared to discharged ones, and Cox regression analysis confirmed an association between BB-DNA and in-hospital mortality. Furthermore, BB-DNA was positively associated with the neutrophil count and negatively associated with plasma IFN-alpha. Additionally, BB-DNA was associated with diabetes.ConclusionsThe association of BB-DNA with mortality, immune-inflammatory parameters and diabetes in hospitalized COVID-19 patients suggests its potential role as a biomarker of unfavourable outcomes of the disease, thus it could be proposed as a novel prognostic marker in the assessment of acute COVID-19 disease.

Aggressive approach for spontaneous pneumothorax treatment in children with Marfan syndrome?

Marfan syndrome (MS) is a systemic disease of connective tissues consisting of a variable combination of anomalies. These patients have an increased risk of spontaneous pneumothorax (SP). However, there is a scarcity of pediatric literature on management, and no specific guidelines exist. Our aim was to analyze the management of spontaneous pneumothorax in children and adolescents with Marfan syndrome, comparing syndromic and non-syndromic patients. Retrospective analysis of pediatric patients (18 years) with SP diagnosed at our tertiary pediatric hospital (January 10-June 22), with special emphasis on diagnosis, treatment, and follow-up (FU). Sixty-six patients with SP were identified, with nine (13%) having MS. In terms of baseline, there were no significant differences between the groups (age, sex, asthma, symptoms, and side, first-line treatment and hospitalization length). Overall, Marfan patients had significantly more first-line treatment failures requiring additional surgery, as well as more contralateral occurrences and the need for surgery/chest drain during the follow-up. Instead, conservative management resulted in significantly more ipsilateral recurrences and the need for surgery/chest drain in Marfan patients than controls during the follow-up. Treatment failure, contralateral occurrence, ipsilateral recurrence, and the need for surgery/chest drain during follow-up make management of patients with Marfan syndrome and spontaneous pneumothorax more difficult. In patients with a diagnosed MS a more aggressive first-line management should be considered, bearing in mind the higher risks of this population.

Optimization of an edible film formulation by incorporating carrageenan and red wine lees into fish gelatin film matrix

The study aimed to use response surface methodology (RSM) to create and understand a novel edible film made from fish gelatin (FG). This film includes wine lees (WL) and carrageenan (CAR). The concentrations of WL (0, 1, 2, and 3 %) and CAR (0, 1, and 3 %) were considered independent variables. The process variable combinations for the optimal response functions were 1.926 % WL and 3 % CAR, forming soft and rigid films with low tensile strength (TS) and high elongation at break (EAB%). Based on the evaluation of each response, FG film had the highest TS value, FG/CAR(3 %) film had the maximum EAB, and FG/WL (3 %)/CAR (3 %) film had the lowest vapor permeability (WVP) and the highest opacity (OP). The incorporation of WL considerably improved the functional properties of these films, enabling strong antioxidant activity and high phenolic content. Characterization of the films with analytical techniques: Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD) analysis demonstrated a considerable interaction between WL and FG, indicating a high level of compatibility between the two substances. Our data suggest that the formulation of edible films can be adjusted to fit the specific requirements of the design.

Estimating the SPAD of Litchi in the Growth Period and Autumn Shoot Period Based on UAV Multi-Spectrum

The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables.

Machine learning for monitoring and classification in inverters from solar photovoltaic energy plants

The efficiency of solar energy farms requires detailed analytics and information on each inverter regarding voltage, current, temperature, and power. Monitoring inverters from a solar energy farm was shown to minimize the cost of maintenance, increase production and help optimize the performance of the inverters under various conditions. Machine learning algorithms are techniques to analyze data, classify and predict variables according to historic values and combination of different variables. The 140 kWp photovoltaic plant contains 300 modules of 255 W and 294 modules of 250 W with smart monitoring devices. In total the inverters are of type SMA Tripower of 25 kW and 10 kW. The 590 kWp photovoltaic plant contains 1312 Trina solar 450 W modules. In total the four inverters are SMA Sunny Tripower type of 110–60 CORE 2 with rated power of 440 kW were analyzed and several supervised learning algorithms were applied, and the accuracy was determined. The facility enables networked data and a machine learning algorithm for fault classification and monitoring was developed, energy efficiency was calculated and solutions to increase energy production and monitoring were developed for better reliability of components according to the monitorization and optimization of inverters.

What does the MAIHDA method explain?

Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) is a new approach to quantitative intersectional modelling. Along with an outcome of interest, MAIHDA entails the use of two sets of independent variables. These include group demographics such as race, gender, and poverty status as well as strata which are constructs such as Black female poor, Black female wealthy, and White female poor. These constructs represent the combination of the demographic variables. To operationalize the approach, an initial random intercepts model with strata as a level 2 context is specified. Then, another model is specified that includes the strata as well as the demographic variables as level 1 fixed effects. As such, it is argued that MAIHDA uniquely identifies the additive and intersectional effects for any given outcome. In this paper we show that MAIHDA falls short of this promise: the strata are an individual-level composite variable not a level 2 context. Rather than being analogous to neighborhoods as contexts, strata are analogous to socio-economic status which is a combination of individual-level demographic variables, albeit often presented as a group-level characteristic. The result is that the demographic variables are inserted in both level 2 and 1. This duplication across the levels in MAIHDA means that there is a built-in collinearity across the levels and that the models are mis-specified and, therefore, redundant. We conclude that single-level models with the demographic variables and interactions or with the strata as fixed effects are still the more accurate models for quantitative intersectional analyses.

Predicting chlorophyll and nitrogen content in rice using multiple regression models

Chlorophyll and nitrogen (N) content in rice at critical crop growth stages are important input parameters for crop growth and nutrient uptake models used to optimize N application. Hence, the study was conducted to predict chlorophyll and N content in rice using multiple regression-based models involving normalized difference vegetation index (NDVI), Soil Plant Analysis Development (SPAD), Customized leaf color chart (CLCC), chlorophyll and N content. An experiment was conducted with six varieties i.e. CR Dhan 312, CR Dhan 310, Lalat, Shatabdi, Swarna Shreya, CR Dhan 206 with four N levels (i.e. 0, 80, 100 and 120 kg N ha−1) in split plot design and replicated thrice. The NDVI, SPAD and CLCC readings were taken at 45, 49 and 54 days after transplanting (DAT). Plant sampling was done for estimating the chlorophyll and nitrogen content in laboratory following standard methodology. Multiple linear regressions were performed between NDVI, SPAD and CLCC readings and chlorophyll & N content in rice in different variables combinations. Multiple linear regression (MLR) models with two variables i.e. NDVI and SPAD predicted the chlorophyll and N content of rice varieties. The predicted values for nitrogen and chlorophyll were comparable to those obtained using models from individual varieties when combined data from six different varieties were used to create MLR models. The study concludes that variations in chlorophyll and N concentrations in rice can be estimated using data from optical sensors with notable accuracies.