R2 Values Research Articles

Machine learning based prediction of biogas generation from municipal solid waste: A data-driven approach

This study aims to imply different machine learning (ML) (artificial neural network (ANN), linear regression, XGBoost, random forest (RF), support vector machine (SVM)) models to predict and optimize biogas production yield from organic fractions of municipal solid waste (MSW). The data set for six key input variables, including moisture content, C/N ratio, lignocellulose content and MSW age was analyzed and processed using ML models. Further, data exploratory analysis (EDA) analysis was performed using various steps such as data preprocessing, feature selection, train-test-validation splitting, model testing and evaluation. The results revealed that XGBoost and RF regression model exhibited superior performance efficacy. Both the models achieved a higher R2 values of 0.88 and 0.68, and low root mean square error (RMSE) values of 305 and 496, respectively. Furthermore, feature importance analysis was performed to identify the relative significance of input variables in biogas prediction. SVM model revealed significant contributions of moisture content (20.86 %), cellulose (60.21 %), hemicellulose (34.91 %), lignin content (62.84 %), and MSW age (17.23 %) in predicting biogas production. The findings of the study can be a resource for techno-crates/researchers to develop an efficient ML based decision-making tools for accurate predictions and process optimization.

Computational analysis of diameter eccentricity based and hyper diameter eccentricity based indices for linear saturated monocarboxylic acids

Chemical graph theory deals with chemical graphs, which are used to represent chemical systems. Topological indices of molecular graphs are an important area of research in chemical graph theory. These indices are numerical values associated with compounds that aim to establish connections between chemical structures and physical attributes, chemical reactivity, and biological activity. Many graph polynomials have been proposed to compute various topological indices. Quantitative structure-property relationship (QSPR) analysis of chemical compounds involves several regression methods that rely on these topological indices. In this article, we utilize the newly introduced DƐ-polynomial to calculate the eccentricitybased indices such as the diameter eccentricity based and hyper diameter eccentricity based index values for nineteen linear saturated monocarboxylic acids. Quantitative structureproperty relationship (QSPR) analysis is also performed for the seven thermodynamic properties of monocarboxylic acids. The relationship between thermodynamic properties and topological indices is explored using linear, quadratic and cubic regression models. The best fit curvilinear regression models were determined based on the R2 and RMSE values of the models. To further investigate the statistical significance of our models, we also conducted chi-square goodness-of-fit tests to identify the best fit models.

Ensemble machine learning models for compressive strength and elastic modulus of recycled brick aggregate concrete

This study delivers an in-depth analysis of predicting the compressive strength and elastic modulus of recycled brick aggregate concrete (RBAC). It assembles an extensive database of 633 compression test results and develops five ensemble learning models—random forest (RF), gradient boosting regression trees (GBRT), extreme gradient boosting (XGB), light gradient boosting machine (LGBM) and stacking (St). These models are evaluated against existing empirical formulas with respect to their effectiveness. The findings reveal that machine learning (ML) models outperform existing formulas: for compressive strength prediction, traditional models achieve a maximum determination coefficient R² of 0.38, whereas ML models attain an R² range of 0.91–0.94. For elastic modulus, the highest R² values are 0.44 for traditional models and 0.97 for ML models. Notably, the LGBM and St models excel in predicting compressive strength and elastic modulus, respectively. The study also identifies critical parameters influencing RBAC’s compressive behavior and highlights their impact trends. Remarkably, while the mass-weighted water absorption of coarse aggregates and the replacement ratio of recycled brick aggregates (RBAs) have less impact on compressive strength compared to the effective water-to-cement ratio, they become more influential for elastic modulus. Additionally, the decrease in elastic modulus due to higher RBA replacement ratios or increased mass-weighted water absorption of coarse aggregates exceeds the corresponding reduction in compressive strength. This research not only deepens the understanding of RBAC’s mechanical properties but also provides valuable predictive tools for civil engineering applications.

The relationship between bank lending and economic factors in the regions of Kazakhstan

Understanding the impact of economic factors on bank lending is crucial in Kazakhstan’s modern economy, characterized by volatile inflation and fluctuations in real wages. This paper aims to investigate the link between bank lending and economic factors such as inflation, real wages, and consumer expenditure in a regional context. Data from the Bureau of National Statistics and the National Bank, covering the period from 2012 to 2022, were used to uncover how economic factors influence bank lending. For the analysis, various economic indicators were integrated through normalization and averaging. Analysis reveals significant regional disparities in real wages and consumer expenditures, which impact the demand for bank credit. The results of the correlation matrix showed that both real wages (P-value < 0.001) and inflation (P-value < 0.001) significantly impact bank lending, with an R² value of 0.998, indicating that the model explains 99.8% of the variation in bank lending. The regression analysis highlights that regions with higher real wages, such as Astana, Almaty, and Atyrau, provide the most favorable conditions for banking sector growth, demonstrated by a strong relationship between wages and bank lending. In contrast, regions with lower wage levels, such as Turkestan and Zhambyl, show a significantly weaker connection (around 0.65), reflecting their lower attractiveness for banking investment and emphasizing the need for policies to address social inequality. The Durbin-Watson test confirmed no autocorrelation in residuals (DW = 1.89), although heteroscedasticity was detected, suggesting the need for further model adjustments. The study emphasizes the importance of developing economic policies that can balance regional development and improve financial stability. AcknowledgmentsThis research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant “Development of mechanisms for reducing social inequality and improving the welfare of the population of Kazakhstan” AP19174744).

Elucidating synergistic effects and environmental value enhancement in infrared-Assisted Co-Pyrolysis of coal and polyvinyl chloride

Co-pyrolysis of high-alkali coal and polyvinyl chloride (PVC) through infrared heating is a promising approach for managing escalating PVC waste and converting low-grade coal resources efficiently. The synergistic effects during co-pyrolysis and thermal degradation of PVC were studied through thermogravimetric analysis (TG-FTIR) and spectral analysis. Furthermore, chlorine migration and chemical transformation integral to the process, along with catalytic interactions, were explored through chlorine mass balance assessment, employing X-ray photoelectron spectroscopy and ion chromatography to establish a groundbreaking understanding of these phenomena. The results of products exhibited a trend of initially increasing and then decreasing with increasing temperatures and mixing ratios. The maximum oil yield was 14.62 %, achieving at 600 °C and 20 %. Meanwhile, the content of aromatic hydrocarbons remained high level through the synergistic interaction of Lewis and Brønsted acid sites, nearly exceeding 60 %. The results showed that the Artificial Neural Network model had a high prediction accuracy with an R2 value of 0.99, while the decarboxylation effect dissociated the metal, resulting in an increase in the fixation of inorganic chlorine from 1 % to 66 % and a decrease in the chlorine content in the liquid phase. Innovatively, the study employs an ANN model for predicting the behavior of co-pyrolysis, exemplifying high accuracy in understanding complex thermal conversions of PVC and coal. Notably, the work challenges existing constraints by applying sophisticated analytical methods to elucidate the thermodynamics and kinetics of the co-pyrolysis processes, thereby enhancing the energetic and environmental value of the products.

Predictive models for properties of hybrid blended modified sustainable concrete incorporating nano-silica, basalt fibers, and recycled aggregates: Application of advanced artificial intelligence techniques

The main objective of this work is to improve the compressive strength of concrete, specially in sustainable construction is to develop more precise predictive modeling techniques. The compressive strength prediction of basalt fiber reinforced concrete filled with nano-silica and recycled aggregates can be done using a hybrid deep learning model suggesting the use of the combination of Convolutional Neural Networks and Long Short-Term Memory networks. The CNN captures microstructural features from SEM images, while the LSTM models temporal dependencies from sequential curing data samples. To enhance the prediction accuracy, PCA was performed on feature dimensionality reduction and GA optimized hyperparameters both for the model as well as the concrete mix design for improved strength with cost effectiveness. With an R² value of 0.92–0.95, the performance results of the presented model came out better than the baseline models, as well as reducing the MAE by 20 %. Besides, there existed a 5–8 % better compressive strength in GA-optimized mix designs. Robustness comes into play with the model that shows steady strength predictions, regardless of conditions of curing under multiple conditions and at different material composition levels. Furthermore, the reutilization of recycled aggregates and nano-silica gives a real environmental benefit as less waste is produced but the material performance is maximized. This kind of outcome indicates how the proposed model can be practically applied in optimizing concrete design in terms of strength and sustainability features, thus providing an accessible instrument for decision-making in the construction field. It is an effective tool to improve the performance of concrete while minimizing environmental and material wastes.

Effects of Variety and Growth Stage on UAV Multispectral Estimation of Plant Nitrogen Content of Winter Wheat

The accurate estimation of nitrogen content in crop plants is the basis of precise nitrogen fertilizer management. Unmanned aerial vehicle (UAV) imaging technology has been widely used to rapidly estimate the nitrogen in crop plants, but the accuracy will still be affected by the variety, the growth stage, and other factors. We aimed to (1) analyze the correlation between the plant nitrogen content of winter wheat and spectral, texture, and structural information; (2) compare the accuracy of nitrogen estimation at single versus multiple growth stages; (3) assess the consistency of UAV multispectral images in estimating nitrogen content across different wheat varieties; (4) identify the best model for estimating plant nitrogen content (PNC) by comparing five machine learning algorithms. The results indicated that for the estimation of PNC across all varieties and growth stages, the random forest regression (RFR) model performed best among the five models, obtaining R2, RMSE, MAE, and MAPE values of 0.90, 0.10%, 0.08, and 0.06%, respectively. Additionally, the RFR estimation model achieved commendable accuracy in estimating PNC in three different varieties, with R2 values of 0.91, 0.93, and 0.72. For the dataset of the single growth stage, Gaussian process regression (GPR) performed best among the five regression models, with R2 values ranging from 0.66 to 0.81. Due to the varying nitrogen sensitivities, the accuracy of UAV multispectral nitrogen estimation was also different among the three varieties. Among the three varieties, the estimation accuracy of SL02-1 PNC was the worst. This study is helpful for the rapid diagnosis of crop nitrogen nutrition through UAV multispectral imaging technology.

Spatial-temporal seasonal and annual rainfall trends and variability assessment in the Pangani Basin, East Africa

Although in situ rainfall data remains the most accurate, the gauge network density in East Africa is sparse. It lacks continuity, thus making it inadequate to assess the spatial and long-term rainfall trend and variability accurately. As such, rainfall remote sensing data are normally used instead of ground station data. This study evaluates the capabilities and limitations of remote sensing data compared with ground-based observations in Tanzania's Pangani Basin in assessing the seasonal and annual rainfall trends and variability. Data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and twenty-three ground stations were analyzed, comprising a time series from 1990 to 2022. Trend analysis was conducted using the Mann-Kendall test, while the spatial distribution of precipitation was determined using Sen's slope method. CHIRPS annual rainfall showed good agreement with station rainfall data, with NSE, R2, slope, Pbias, MAE, and RMSE values of 0.84, 0.92, 0.92, 7.55%, 297.4, and 397.1, respectively. The coefficient of variation from station data indicated extreme variability, exceeding 30% for annual rainfall, while CHIRPS data showed moderate variability, ranging from 20% to 30%. Both station data and remote sensing data showed an increasing trend for annual and seasonal rainfall at least 10 stations. However, stations like Maji Moshi, Kiungu Primary School, Segera C. Tank, and Kibong'oto exhibited an increasing trend for vuli rainfall with CHIRPS data, while station data indicated a significant decreasing trend.The study highlights the necessity for calibration and validation to avert misinterpretations in climate trend analyses, especially at the basin level.

A novel feature screening algorithm for low-resolution LIBS spectrum elemental quantification

This study presents an innovative approach that integrates Laser-Induced Breakdown Spectroscopy (LIBS) with chemometrics for the quantitative analysis of Si, Ca, Al, and Mg in geological samples. Given the spectral redundancy in low-resolution LIBS devices, the study employs pre-processing techniques, such as AirPLS, Wavelet Transform (WT), and normalization to mitigate spectral noise. Enhanced feature threshold searching is achieved by incorporating SHapley Additive exPlanations (SHAP) and LightGBM into the Boruta algorithm, substantially improving quantitative analysis models based on Support Vector Regression (SVR) and Partial Least Squares Regression (PLSR). The modified Boruta-SVR model demonstrated remarkable robustness, with R2 values of 0.9862, 0.9873, 0.9882, and 0.9916, and RMSE values of 0.8099, 0.324, 0.1378, and 0.2382, respectively, for Si, Ca, Al, and Mg. The results confirm that the Boruta-based feature selection method, when applied to low-resolution LIBS spectra, outperforms traditional methods, capturing unique sample features under mixed spectral peak conditions, thereby enhancing the robustness of quantitative analysis models.

Quantitative Raman analysis of microplastics in water using peak area ratios for concentration determination

This study presents a novel analytical method for the quantitative and qualitative analysis of microplastics (MPs) in deionized (DI) water using Raman spectroscopy. Raman peak area ratios of 1295 cm−1 for polyethylene (PE) and 637 cm−1 for polyvinylchloride (PVC) to the broad H2O peak were utilized to establish a calibration model for the concentration of MPs dispersed in DI water at 0.1 wt% to 1.0 wt%. The calibration model demonstrated R2 values of 0.98537 for PE and 0.99511 for PVC, indicating high linearity between the peak area ratio and concentration. The calibration model was validated using mixed PE and PVC samples to confirm its applicability to real-world water bodies, where multiple types of MPs are present. The calculated standard error of calibration (SEC) and relative standard error of calibration (%RSEC) values further confirmed the accuracy of the predictions, providing a robust approach for detecting and quantifying MPs in aquatic environments.

Multimodal adversarial informer for highway vehicle lane-changing trajectory prediction

To enhance the perception of vehicle trajectory information and lane-changing decision-making capabilities in intelligent connected vehicles during multivehicle interaction scenarios, we propose a novel method based on a Multimodal Adversarial Informer (MAI) for highway multivehicle lane-changingrajectory prediction. This method achieves spatiotemporal features of target and surrounding vehicles through graph learning of temporal features and spatial adjacency matrices. Considering the heading angle and vehicle local X-axis displacement, the vehicle trajectory samples are categorized for training and validation of the multimodal Informer. A multi-criterion discriminator is utilized to judge whether the generated trajectory fits the requirements of accuracy and rationality. After adversarial learning, the optimal vehicle lane-changing trajectory prediction is obtained using the proposed MAI. Experiments conducted with the NGSIM dataset demonstrate the comparative performance of baseline models on three different noise-added testing datasets using MAE, RMSE, and R² metrics. The MAI model consistently outperforms the others, achieving the lowest MAE and RMSE and the highest R² values across all datasets, indicating superior predictive accuracy and fit. Furthermore, the results show that the proposed MAI framework maintains a relatively low prediction error over both short-term and long-term horizons compared to baseline models.

Simultaneous detection of myostatin-targeting monoclonal antibodies in dried blood spots and plasma using liquid chromatography-tandem mass spectrometry with field asymmetric ion mobility spectrometry

Transforming growth factor-β superfamily members, such as myostatin, growth/differentiation factor 11, and activin A, negatively regulate skeletal muscle mass. Inhibitors targeting these cytokines or activin receptor type IIB have the potential to treat muscular diseases and enhance physical performance. However, because of their effects on muscle mass and potential misuse, they are strictly prohibited in sports. Given the high potential for misuse as a doping agent in sports, effective analytical methods for these prohibited antibodies targeting these specific cytokines or their receptor are critically needed. In this study, we aimed to develop and validate a multitarget method to detect the prohibited transforming growth factor-β superfamily-targeting monoclonal antibodies, such as landogrozumab, domagrozumab, and the activin receptor type IIB-targeting antibody, bimagrumab, in human plasma and dried blood spot (DBS) samples using liquid chromatography-tandem mass spectrometry. Antibodies were purified from both the DBS and plasma samples using protein G magnetic beads and field-asymmetric ion mobility spectrometry (FAIMS) to minimize interference, followed by liquid chromatography-tandem mass spectrometry analysis. The validation process included tests for specificity, selectivity, linearity, limit of detection (LOD), limit of identification, precision, recovery, carryover effect, and matrix effect. The LODs for the target antibodies were identical in both DBS and plasma samples at 0.1 µg/mL for landogrozumab heavy and light chains, as well as 0.25 µg/mL for the domagrozumab light chain and 0.25 µg/mL for the bimagrumab heavy chain. However, the heavy chain of domagrozumab exhibited an LOD of 0.5 µg/mL in DBS and 1 µg/mL in plasma. The analytical method demonstrated strong linearity, with R² values greater than 0.99 for both plasma and DBS, and no carryover effect. Precision (CV%) was below 15 % at both middle (1 or 5 µg/mL; specific to the heavy chain of domagrozumab in plasma) and high (10 µg/mL) concentrations and was less than 20 % at the LOD. The selectivity and specificity indicated no interference in the analysis of target mAbs in different blood samples. Recovery was 31.6–49.8 % for DBS and 51.4–85.3 % for plasma, with no significant matrix effect. This study provides an effective method for doping analysis and novel protein detection.

Geoinformatics and Modeling Approaches for Estimating Above-Ground Biomass in the Moist Deciduous Forests of Uttara Kannada, Karnataka, India

The present study aims to estimate Above-Ground Biomass (AGB) in the moist deciduous forests of Mundgod Taluk, Uttara Kannada district, Karnataka, India using a combination of field sampling and remote sensing techniques. AGB was assessed using Sentinel-2A satellite imagery with a spatial resolution of 10 meters. Field data were collected using the Point-Centered Quarter (PCQ) method, it includes measuring tree girth at breast height (GBH), tree height, and canopy density. AGB was calculated using artificial form factor and specific gravity of tree species and further modelled using the Normalized Difference Vegetation Index (NDVI) derived from satellite data. The results showed a significant correlation between canopy density and biomass, with very dense forests exhibiting the highest AGB values. The field-measured AGB was 436.96 t/ha for very dense forests, 259.89 t/ha for moderately dense forests, and 161.67 t/ha for open forests. A linear regression model developed between AGB and NDVI showed a high R² value of 0.91 for open forests, 0.87 for moderately dense forests, and 0.85 for very dense forests. The total area-weighted biomass for the study area was estimated at 8.87 million tons, with 4.79 million tons in very dense forests, 3.44 million tons in moderately dense forests, and 0.76 million tons in open forests. The regression model validation indicated low Root Mean Square Error (RMSE) values, with the moderately dense forests 8.81 t/ha and the in very dense forests 7.54 t/ha. This study highlights the effectiveness of integrating field measurements with remote sensing for rapid, reliable AGB estimation. The approach offers valuable insights into biomass distribution and carbon sequestration potential, providing a scalable method for carbon inventories at state and national levels, contributing to forest management and climate change mitigation efforts.

Enhancing Unconfined Compressive Strength prediction in geotechnical engineering: a novel approach using LSZO and LSWG optimization

ABSTRACT The Unconfined Compressive Strength (UCS) measures a rock’s ability to withstand axial loads without lateral support, crucial for engineering applications. Accurate prediction of UCS is critical for the stability and safety of various civil engineering projects, such as foundation design, mining, and tunneling. The ability to predict UCS reliably ensures better project planning and execution, ultimately enhancing the safety and efficiency of engineering applications. The paper aims to develop an innovative method for predicting the UCS of rocks using a combination of the Zebra Optimisation Algorithm (ZOA) and the Wild Geese Algorithm (WGA) integrated with the Least Square Support Vector Regression (LSSVR) predictive model. The novelty of the paper lies in the integration of ZOA and WGA optimizers with the LSSVR model to address the limitations of conventional methods, which often face challenges like slow achieving convergence and being stuck in local minima. The combination of these advanced algorithms results in significantly improved accuracy and convergence speed in UCS predictions, demonstrating a robust and reliable approach that advances the field of geotechnical engineering. The results show that combining ZOA and WGA optimizers with LSSVR enhances UCS prediction accuracy and speed convergence. The LSZO models achieve a high level of precision, with a low RMSE value of 2.206 and an R2 value of 0.993. The safety and effectiveness of civil engineering projects are improved by this model, which provides a strong and trustworthy method for UCS prediction.

INTERACTIONS WITH POPULACES’ PERCEPTIONS OF THE POLICY FOR SOLVING PROBLEMS OF THE PM2.5 DUST TOXIC POLLUTION ON HEALTH AND THE IMPACT CONTAMINATION TO AFFECTED RESPIRATORY SYSTEM DISEASES

Science 2008, keep an eye on the Particulate Matter (PM 2.5) toxic dust situation including any chemical, physical, or biological agent mixture of solid and liquid particles suspended in the air. It has affected the health of Thai people with over 10 million patients each period in a year. The Thai government needs to solve the problem of PM2.5 dust pollution at its sources seriously. The effects of global and climate change are affected to impact the threat to human health by human activities in short-middle-and long terms: Spiration System Affected, Lung Cancer, Carcinogenicity, Brain Threats, Heart Hazards diseases. Creative the Document Research Methodology data was reviewed, the Qualitative Research method data was interviewed and observed, and the independent and dependent variables were associated with 400 populaces who used to be sick with PM2.5 dust in 2023. Creating The 36-item Questionnaire on Populaces and PM2.5 Affected Interaction (QPPAI) in six scales; each scale consisted of 6 items in five options (Never-Always); and the 10-item Attitudes on Solving-Problem PM2.5 Toxic Dust (ASPTD). These instruments are valid and reliable. The participants’ responses reveal that they know how to protect themselves from PM2.5 dust and get support from the government (KPSG), they understand the effects of PM2.5 toxic dust, which can be inhaled deep into the respiratory tract and lungs (UETD), their receiving care from the government for being a patient with PM2.5 dust pollution (RCGP), most of the participants are understanding of effects of PM2.5 toxic dust (UETD), however, their awareness of news from the media or government organizations for groups at risk of danger from PM2.5 dust, receiving advice on care and treatment (AGAT), and this situation has in shock because they don’t yet understand the health effects of PM2.5 dust on the respiratory system and lungs (UHER), respectively. The results have found that the R2 value indicates that 61% of the variance in populaces’ perceptions of their attitudes to the government policies was attributable to their perceptions of the health effects of the PM2.5 toxic dust pollution in the air environment. They are supported significantly by government policies.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0773/a.php" alt="Hit counter" /></p>

Predictive Model for O3 in Shanghai Based on the KZ Filtering Technique and LSTM

In this study, a Kolmogorov-Zurbenko （KZ） filter was proposed to decompose the original ozone （O3） sequence to improve the accuracy of ozone long-term series prediction and select relevant meteorological features. Furthermore, the enhanced maximal minimal redundancy （mRMR） feature selection technique was combined with the support vector regression （SVR） approach to select the most illuminating meteorological features. Subsequently, from May to August 2023, during high ozone concentration periods, a long short-term memory network （LSTM） was utilized to assess and predict high ozone concentration periods at the monitoring stations of Jingan （urban area）, Pudong-Chuansha （suburban area）, and Dianshan Lake （suburban area） in Shanghai. The results showed that pressure, temperature, humidity, boundary layer height, and wind direction were the best combinations of O3 baseline and short-term components, as chosen by feature screening. The R2 values for Jingan Station, Pudong-Chuansha Station, and Dianshan Lake Station were 0.86, 0.83, and 0.85, respectively. The RMSE values were 18.26, 18.74, and 20.02 μg·m-3, respectively. These findings suggest that decomposing the original O3 sequence improved the prediction accuracy of ozone concentrations. Additionally, as indicated by the R2 and RMSE values found for every monitoring station, feature screening preserved the model's predictive performance.

Resonant Cell-Based 1f Photoacoustic Gas Analyzer Immune to Light Power Fluctuation and Frequency Mismatch.

To overcome the light power fluctuation and frequency mismatch in photoacoustic spectroscopy (PAS), we proposed a self-corrected 1f-only resonant cell-based PAS (1f-RCPAS) gas analyzer. Based on the theoretical analysis of the 1f signal, a signal processing algorithm considering laser power-current nonlinearity is proposed. The 1f-only algorithm is well-tailored for the resonant systems, requiring no time-division multiplexing. The algorithm is further improved to extend the dynamic range. The T-type resonant cell incorporating a graphene sticker is utilized for effectively amplifying the acoustic signals from both the gas and solid to achieve normalization. No optical path alignment is needed. For the low resonance frequency, a digital orthogonal-vector lock-in amplifier is used, further simplifying the system setup. The gas analyzer is used to measure methane (CH4) with the near-infrared absorption peak at 1651 nm. The experiments demonstrated immunity to fiber coupling loss, laser power drift over time, and frequency mismatch caused by property differences between air and standard gases. The R2 value in the concentration calibration reaches 0.99995, and the minimum detection limit given by the Allan variance reaches 3.5 ppb at an average time of 105 s.

Spatial Chlorophyll Estimation from Rice Field using Drone-derived Spectral Indices

Background: Precision farming has significantly advanced the agricultural sector by enabling real-time canopy monitoring and crop condition evaluation, thus facilitating precise management strategies to enhance yields. This study investigated the use of drone-derived vegetation indices (VIs) for assessing spatial variability in crop conditions, offering a more cost effective and practical alternative to satellite data. Methods: The field experiment was conducted during the Kuruvai season (July - November 2023) on a short-duration rice variety CO 55. Several vegetation indices viz., BGI, CI, EVI, GNDVI, MCARI, MSAVI, NDRE and NDVI were calculated to predict chlorophyll content and correlated with the measured SPAD values. Result: The results showed that indices like MCARI, GNDVI, and NDVI had strong positive correlations with SPAD values, with MCARI exhibiting the highest correlation (R = 0.914) and an R² value of 0.836. The findings underscore the effectiveness of using drone-derived indices for precise chlorophyll estimation, which is crucial for variable rate fertilizer application in precision agriculture.

A Remote-Sensing-Based Method Using Rockfall Inventories for Hazard Mapping at the Community Scale in the Arequipa Region of Peru

Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the creation of rockfall hazard rating maps. However, the only rockfall inventory available for Arequipa contains limited data of large, discrete events, which is insufficient for characterizing rockfall hazards at the community scale. A more comprehensive inventory would result in a more accurate rockfall hazard rating map—a significant resource for hazard mitigation and development planning. This study addresses this need through a remote method for rockfall hazard characterization at a community scale. Three communities located in geographically diverse areas of Arequipa were chosen for hazard inventory and characterization, with a fourth being used for validation of the method. Rockfall inventories of source zones and rockfall locations were developed using high-resolution aerial imagery, followed by field confirmation, and then predictions of runout distances using empirical models. These models closely matched the actual runout distance distribution, with all three sites having an R2 value of 0.98 or above. A semi-automated method using a GIS-based model was developed that characterizes the generation and transport of rockfall. The generation component criteria consisted of source zone height, slope angle, and rockmass structural condition. Transport was characterized by rockfall runout distance, estimated rockfall trajectory paths, and hazard ratings of corresponding source zones. The representative runout distance inventory model of the validation site matched that of a nearby site with an R2 of 0.98, despite inventorying less than a third of the number of rockfalls. This methodology improves upon current approaches and could be tested in other regions with similar climatic and geomorphic settings. These maps and methodology could be used by local and regional government agencies to warn residents of rockfall hazards, inform zoning regulations, and prioritize mitigation efforts.

In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin

In the production of polymeric drug delivery devices, dissolution profile and mechanical properties of the drug loaded polymeric matrix are considered important Critical Quality Attributes (CQA) for quality assurance. However, currently the industry relies on offline testing methods which are destructive, slow, labour intensive, and costly. In this work, a real-time method for predicting these CQAs in a Hot Melt Extrusion (HME) process is explored using in-line NIR and temperature sensors together with Machine Learning (ML) algorithms. The mechanical and drug dissolution properties were found to vary significantly with changes in processing conditions, highlighting that real-time methods to accurately predict product properties are highly desirable for process monitoring and optimisation. Nonlinear ML methods including Random Forest (RF), K-Nearest Neighbours (KNN) and Recursive Feature Elimination with RF (RFE-RF) outperformed commonly used linear machine learning methods. For the prediction of tensile strength RFE-RF and KNN achieved R2 values 98% and 99%, respectively. For the prediction of drug dissolution, two time points were considered with drug release at t = 6 h as a measure of the extent of burst release, and t = 96 h as a measure of sustained release. KNN and RFE-RF achieved R2 values of 97% and 96%, respectively in predicting the drug release at t = 96 h. This work for the first time reports the prediction of drug dissolution and mechanical properties of drug loaded polymer product from in-line data collected during the HME process.