High R2 Values Research Articles

PREPARATION AND CHARACTERIZATION OF MICROSPHERES UTILIZING RATE-CONTROLLING MEMBRANES FOR THE MANAGEMENT OF DIABETES MELLITUS

The present research work aimed at the formulation of film-coated microspheres incorporating glibenclamide drug and their evaluation for the management of diabetes mellitus (DM). Microspheres were prepared by solvent evaporation methodology by the usage of ethyl cellulose as polymer, ethanol and dichloromethane as solvents and Tween 80® as a non-ionic surfactant. The film-coated membrane was prepared by pan coating method, incorporating ethyl cellulose, isopropyl alcohol, diethyl phthalate and sodium lauryl sulfate. This film membrane was coated on microspheres with the help of a spray gun. The efficiency of entrapment of the film coated microspheres of F5* batch, among different formulations, is highest and comes out to be in the range of 76.65±0.58. The percentage yield was observed to be 73.32±0.14. Morphological studies conducted by scanning electron microscope show spherical microspheres of uniform size. In vitro drug release study conducted of the coated microspheres of glibenclamide shows the highest amount of release of 97.44% in the F5*batch. The best-fit model was determined by the highest R2 value. Further, the developed formulation helps in reduction in dose dumping, with better patient compliance, and also masks the bitter taste of the drug.

Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning

The persistent and increasing levels of sulfate due to a variety of human activities over the last decades present a widely concerning environmental issue. Understanding the controlling factors of groundwater sulfate and predicting sulfate concentration is critical for governments or managers to provide information on groundwater protection. In this study, the integration of self-organizing map (SOM) approach and machine learning (ML) modeling offers the potential to determine the factors and predict sulfate concentrations in the Huaibei Plain, where groundwater is enriched with sulfate and the areas have complex hydrogeological conditions. The SOM calculation was used to illustrate groundwater hydrochemistry and analyze the correlations among the hydrochemical parameters. Three ML algorithms including random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN) were adopted to predict sulfate levels in groundwater by using 501 groundwater samples and 8 predictor variables. The prediction performance was evaluated through statistical metrics (R2, MSE and MAE). Mine drainage mainly facilitated increase in groundwater SO42− while gypsum dissolution and pyrite oxidation were found another two potential sources. The major water chemistry type was Ca-HCO3. The dominant cation was Na+ while the dominant anion was HCO3−. There was an intuitive correlation between groundwater sulfate and total dissolved solids (TDS), Cl−, and Na+. By using input variables identified by the SOM method, the evaluation results of ML algorithms showed that the R2, MSE and MAE of RF, SVM, BPNN were 0.43–0.70, 0.16–0.49 and 0.25–0.44. Overall, BPNN showed the best prediction performance and had higher R2 values and lower error indices. TDS and Na+ had a high contribution to the prediction accuracy. These findings are crucial for developing groundwater protection and remediation policies, enabling more sustainable management.

An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques

The precise estimation of the bonding strength between concrete and fiber-reinforced polymer (FRP) bars holds significant importance for reinforced concrete structures. This study introduces a new methodology that utilizes soft computing methods to enhance the prediction of FRP bars' bonding strength. A significant compilation of experimental bond strength tests is assembled, covering various variables. Significant variables that affect bonding strength are found in the study of this database. The prediction process is optimized using soft computing methods, particularly Gene Expression Programming (GEP) and the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR).The proposed soft computing approaches accommodate complex relationships and optimize prediction accuracy depending on the input variables. Results demonstrate its effectiveness in predicting bond strength and comparing it with existing codes and other models from the literature. The results have shown that the MOGA-EPR and the GEP models have high R2 values between 0.91 and 0.94. The proposed new models enhance the reliability and efficiency of designing and assessing FRP-reinforced concrete.

Predicting river water quality: An imposing engagement between machine learning and the QUAL2Kw models (case study: Aji-Chai, river, Iran)

Rivers play an essential role in supplying high-quality water to diverse sectors. Understanding water quality indicators and systematic monitoring is crucial for water resources management and macro-level decision-making. In this context, the forthcoming article delves into the simulation of three crucial parameters, namely EC, SAR, and TDS, through a reach of 106 km length along the Aji-Chai River, Iran, encompassing stations from Markid, Khajeh, Akhola, and Serin Dizj. This simulation employs three advanced machine learning models: SVM, GEP, and MLP, in conjunction with the QUAL2Kw mathematical simulator. The study meticulously evaluates the performance of these models using four key indices: RMSE, MAE, R2, and DDR. The calculated results unequivocally establish the superiority of the SVM in simulating three essential water quality parameters across all stations. This is supported by consistently high R2 and DDR values, along with low RMSE and MAE values. While the mathematical model used in this study showed reasonable accuracy in simulating the parameters under investigation, it consistently performed less effectively than the SVM model. In summary, the SVM model with specific parameters (C = 68.5, ε = 4.55, and γ = 205) emerges as the optimal choice for accurately simulating river water quality parameters based on the conducted study.

Physics-guided deep learning for driving force estimation in synchronous tunnel boring machines under missing cylinders

To solve the problems of high-dimensional under-constraint and state unknowability in the segment assembly process, this study develops a physics-guided deep learning approach to estimate the hydraulic cylinder driving force and promote the system stability in the synchronous excavation and segment assembly tunnel boring machines (S-TBMs). The Newton's law and the Lagrange multiplier method are used to analyze the force boundary of the S-TBM, where the constraint equations are embedded into the deep learning model. The basic model adopts the multi-head attention deep neural network (MD) architecture, which can capture the extreme data of the cylinder through the attention mechanism. The results show that: (1) The proposed physics-guided deep learning can effectively predict driving force of the cylinder and generate high R2 values which is 0.977. (2) By incorporating the physical laws and simulation data, the model accuracy of R2, RMSE, MAE, and EVS is increased by 9.04%, 53.96%, 55.91%, and 9.89%, respectively. (3) The estimation approach has good robustness, and there is no obvious deviation in the estimation results for 16 cylinders. The novelty to be highlighted is the development of the cylinder driving forces estimation approach with the physical laws incorporated, which is helpful in effectively improving the stability during the excavation and segment assembly process.

Use of machine learning approaches for body weight prediction in Peruvian Corriedale Sheep

The goal of this study was to predict the body weight of Corriedale ewes using machine learning (ML) algorithms. Fourteen body measurements (BM) and six different machine learning models were used. Body weight (BW) and BM: wither height (WH), rump height (RH), thoracic perimeter (TP), abdominal perimeter (AP), foreshank length (FSL), fore-shank width (FSW), fore-shank perimeter (FSP), tail width (TW), tail perimeter (TPe), hip width (HW), loin width (LWi), shoulder width (SW), forelimb length (FL), and body length (BL), were collected from 100 Corriedale ewes between 1.5 and 2 years old from the Illpa Experimental Centre of the National University of Altiplano in Peru. The machine learning algorithms used to estimate body weight were Support Vector Machines for Regression (SVMR), Classification and Regression Trees (CART), Random Forest (RF), Model Average Neural Networks (MANN), Multivariate Adaptive Regression Splines (MARS) and eXtreme Gradient Boosting (XGBoost). The performance of the models was evaluated by the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Highly correlated predictors (r ≥ 075) were removed from the dataset. The remaining predictors were then subjected to variable selection procedures using the Boruta algorithm. Boruta results confirmed the importance of TP, LWi, BL, FSL, SW and HW as predictors of ewe weight. The ML models were then trained on those selected predictors. RF had the highest R2 values and lowest values of MAE, RMSE, and MAPE. In conclusion, the RF algorithm can be recommended for accurately estimating BW from body measurements of Corriedale sheep.

Hydrogen production potential from plastic pyrolysis oil: Experimental and economic insights

Large-scale H2 production conventionally relies on naphtha and natural gas steam reforming, contributing to global dependence on fossil fuels. This study investigates the potential of pyrolytic oil derived from waste personal protective kits as an alternative to fossil fuels for H2 production. Steam reforming was investigated using a three-level Box-Behnken design to optimize temperature, steam-to-carbon ratio (S/C), and weight-hourly space-velocity (WHSV) using Reformax-300 catalyst in a fixed-bed reactor. The generated experimental data aligned well with predicted models, yielding significant quadratic models with high R2 values. The optimal conditions suggested for a maximum 77.5% H2 yield and a 68.3% carbon conversion are 800℃, S/C=3.5, and WHSV of 0.5 h⁻¹. Techno-economic analysis suggested H2 production at $2.57/kg (₹214/kg) for a 250MT/yr plant with rate of return of 6.2% at 70% operational capacity, with feedstock price being the primary cost influencer as observed through sensitivity analysis.

Hybrid machine learning model and predictive equations for compressive stress-strain constitutive modelling of confined ultra-high-performance concrete (UHPC) with normal-strength steel and high-strength steel spirals

Stress-strain constitutive model for confined ultra-high-performance concrete (UHPC) plays a pivotal role in the design and modelling of UHPC structures. However, while extensive research exists on the stress-strain responses of unconfined UHPC, a notable dearth of studies focuses on the stress-strain constitutive model for confined UHPC. Accordingly, this study introduces a novel hybrid machine learning (ML)-based stress-strain constitutive model for UHPC confined with normal-strength steel (NSS) or high-strength steel (HSS) spirals. Given the limited experimental data available, a numerical model was developed and validated against experimental results of confined UHPC. Subsequently, it was employed to generate a comprehensive dataset of confined UHPC stress-strain responses, which were then used to train the hybrid ML model. Hyperparameters of the model are subsequently optimized using Optuna along with a cross-validation technique. The optimized hybrid ML model exhibited exceptional accuracy in predicting the stress-strain response of confined UHPC with NSS or HSS spirals, as demonstrated by high coefficients of determination (R2) values ranging from 0.905 to 0.999 and low error metrics across varying stress levels and datasets. Moreover, the capability of the model to simultaneously predict both stress and strain responses was further validated through canonical correlation analysis (CCA). The results of the analysis demonstrated robust predictive performance of the model, with high CCA scores that ranged between 93.3% and 99.4%. To facilitate the practical implementation of the model, an end-to-end interactive software tool is developed. In addition, this study proposes predictive equations for the peak and ultimate stress-strain responses of confined UHPC. The evaluation of the developed predictive equations demonstrates their efficacy in accurately predicting the stress-strain responses of confined UHPC, as evidenced by the high R2 values, which ranged from 83–91%.

Computational integration for antifungal 1,2,4-triazole inhibitors design: QSAR, molecular docking, molecular dynamics simulations, ADME/Tox, and retrosynthesis studies

Fungal infections are a growing public health problem worldwide. Despite the availability of several medicines, their efficacy is still constrained by fungal resistance. This research conducted the 2D/3D-QSAR analysis on twenty-nine triazole molecules previously evaluated for their antifungal activity. The HQSAR/B-H, CoMFA and CoMSIA models were built using twenty-three molecules in the training set. They show high Q2 values (0.646, 0.564 and 0.561, respectively) and important R2 values (0.764, 0.805 and 0.787, respectively). The predictive capacity of the established models was validated by external validation; they performed well. The contour maps derived from the HQSAR/B-H, CoMFA and CoMSIA models provide more detail to identify favorable and unfavorable groupings impacting the activity. Then, 4 proposed new triazole molecules with significant antifungal activity were suggested. In addition, the molecular docking results showed good binding energies and interactions of the proposed inhibitors in the active site of the receptor studied. The molecular dynamics and MM/PBSA methods confirmed and validated the molecular docking results. The new triazole molecules were evaluated for their oral bioavailability and toxicity using ADME/Tox properties. Finally, the retrosynthesis method created a synthetic pathway for the candidate inhibitor Z1.

Yield Predication by DSSAT Model of Wheat Crop: A Review

The DSSAT model predicts crop productivity under different crop management scenarios and fluctuating climate circumstances, necessitating the identification of a crop cultivar's genetic coefficient. The precision with which various parameters are calibrated and validated is essential to the effective application of crop models. The model was calibrated using the data on irrigation and nitrogen's impact on wheat yield, and it was validated using the date of sowing. The closer estimation of crop growth time, grain yields, and biomass yields was demonstrated by the model findings. The percentage error discrepancy between the simulated and actual wheat variety grain yields was 8.70% to 10.98%, respectively. There is a substantial correlation with a higher R2 value between the simulated and observed grain yields and crop duration during both the calibration and validation processes.

Preparation and utilization of Zn-La oxide nanocatalyst as a binary composite for photocatalytic degradation of methylene blue dye: Optimization through RSM-BBD

In this research, the photocatalytic degradation of methylene blue (MB) dye was explored by employing Zn-La (zinc-lanthanum) oxide nanocatalysts under UV irradiation. Preparation of Zn-La oxide nanocatalysts was executed utilizing the co-precipitation technique, and the surface and morphology of the fabricated nanocatalyst were examined via X-Ray diffraction analysis (XRD), Fourier-transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET), Barrett-Joyner-Halenda (BJH), Field emission scanning electron microscopy (FESEM), Transmission electron microscopy (TEM), and energy dispersive X-ray (EDX) methods. BET-BJH analysis demonstrated that the specific surface area of the Zn-La oxide nanocatalyst was 20.43 m2/g. Moreover, the response surface methodology (RSM) procedure was utilized to influence the operational factors i.e., catalyst amount, pH, H2O2 dose, and UV power. The quadratic model proposed via the RSM technique with a high R2 value of 0.89, demonstrates its reliability. Besides, the maximum percentage of MB dye removal reached 75.18% under optimal conditions, which include a catalyst amount of 12.05 mg, pH of 9, H2O2 dose of 0.44 mL, UV power of 23 W, and a reaction time of 45 min. Further, under the mentioned conditions, after 120 min, the MB degradation efficiency reached 98.8%. The kinetic study exhibited that the reaction rate constant was 0.0363 min−1. According to the outcomes, Zn-La oxides have a higher ability to remove MB. After 5 consecutive reuse rounds, the reduction in the yield of MB removal using Zn-La oxide nanocatalysts was 5.4%. This study demonstrates that the Zn-La oxide nanocatalyst can be promisingly utilized for the degradation of MB dye suspension solution.

Investigation on enhancement of filler dispersion and prediction of mechanical behavior of hexagonal boron nitride/epoxy nanocomposites through machine learning and deep learning models

AbstractTwo‐dimensional hexagonal boron nitride (hBN) based nanocomposites exhibit excellent mechanical and thermal properties for various electronics, automotive, and aerospace applications. The present work gives a novel approach to fabricating hexagonal boron nitride/epoxy nanocomposites using isopropanol and dimethyl ketone as dispersants in two different routes and to predict mechanical characteristics employing deep learning and machine learning models. Nanocomposites were fabricated by employing casting techniques with varying concentrations of hBN, spanning from 0.25 to 1 wt%, utilizing dispersing solvents. The nanocomposites were analyzed for mechanical behavior, highlighting a notable improvement in the mechanical properties at 0.5 wt% isopropanol dispersed hBN. It showcased 61% improvement in tensile strength, 38.41% increase in flexural strength, and 35.80% increase in flexural modulus respectively as compared to the pristine epoxy. The Halpin Tsai analytical model showed agreement with the elastic modulus calculated experimentally. The fractured SEM micrograph supported the improved dispersion of the hBN nanocomposite. Thermal stability of 0.5 wt% isopropanol dispersed hBN/epoxy nanocomposite revealed an improvement by 8°C at 50% degradation as compared to the pristine epoxy. Linear regression, random forest regression, support vector regression, and deep neural network (DNN) were employed to predict values. DNN proved better results by showcasing low prediction loss and high R2 values (0.99468–0.99966).Highlights hBN/epoxy nanocomposite with isopropanol and dimethyl ketone as dispersants. 0.5% hBN loading in isopropanol exhibited improved mechanical characteristics. The Halpin Tsai model was employed for evaluating theoretical elastic modulus. Improved thermal stability and filler dispersion by optimized hBN/epoxy combination. Deep neural network showed higher R2 value and lower prediction loss.

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics.

In this study, an Artificial Neural Network (ANN) model is used to solve the complex task of producing fresh cheese with the desired quality parameters. The study focuses on kombucha fresh cheese samples fortified with ground wild thyme, supercritical fluid extract of wild thyme, ground sage and supercritical fluid extract of sage and optimizes the parameters of chemical composition, antioxidant potential and microbiological profile. The ANN models demonstrate robust generalization capabilities and accurately predict the observed results based on the input parameters. The optimal neural network model (MLP 6-10-16) with 10 neurons provides high r2 values (0.993 for training, 0.992 for testing, and 0.992 for validation cycles). The ANN model identified the optimal sample, a supercritical fluid extract of sage, on the 20th day of storage, showcasing specific favorable process parameters. These parameters encompass dry matter, fat, ash, proteins, water activity, pH, antioxidant potential (TP, DPPH, ABTS, FRAP), and microbiological profile. These findings offer valuable insights into producing fresh cheese efficiently with the desired quality attributes. Moreover, they highlight the effectiveness of the ANN model in optimizing diverse parameters for enhanced product development in the dairy industry.

Optimizing Quality and Shelf-Life Extension of Bor-Thekera (Garcinia pedunculata) Juice: A Thermosonication Approach with Artificial Neural Network Modeling

This study investigated the quality characteristics of pasteurized and thermosonicated bor-thekera (Garcinia pedunculata) juices (TSBTJs) during storage at 4 °C for 30 days. Various parameters, including pH, titratable acidity (TA), total soluble content (TSSs), antioxidant activity (AA), total phenolic content (TPC), total flavonoid content (TFC), ascorbic acid content (AAC), cloudiness (CI) and browning indexes (BI), and microbial activity, were analyzed at regular intervals and compared with the quality parameters of fresh bor-thekera juice (FBTJ). A multi-layer artificial neural network (ANN) was employed to model and optimize the ultrasound-assisted extraction of bor-thekera juice. The impacts of storage time, treatment time, and treatment temperature on the quality attributes were also explored. The TSBTJ demonstrated the maximum retention of nutritional attributes compared with the pasteurized bor-thekera juice (PBTJ). Additionally, the TSBTJ exhibited satisfactory results for microbiological activity, while the PBTJ showed the highest level of microbial inactivation. The designed ANN exhibited low mean squared error values and high R2 values for the training, testing, validation, and overall datasets, indicating a strong relationship between the actual and predicted results. The optimal extraction parameters generated by the ANN included a treatment time of 30 min, a frequency of 44 kHz, and a temperature of 40 °C. In conclusion, thermosonicated juices, particularly the TSBTJ, demonstrated enhanced nutritional characteristics, positioning them as valuable reservoirs of bioactive components suitable for incorporation in the food and pharmaceutical industries. The study underscores the efficacy of ANN as a predictive tool for assessing bor-thekera juice extraction efficiency. Moreover, the use of thermosonication emerged as a promising alternative to traditional thermal pasteurization methods for bor-thekera juice preservation, mitigating quality deterioration while augmenting the functional attributes of the juice.

Linking the future likelihood of large fires to occur on mountain slopes with fuel connectivity and topography

AbstractIn the long run, ongoing climate change is expected to alter fuel production as well as the frequency and severity of fire weather, which may result in an unprecedented frequency of extreme fire events. In this paper we propose a simplified and spatially explicit method to assess the probability of experiencing large fires, based on topography (slope length) as well as extent and aggregation of the forested area (fuel connectivity). We considered 21 homogeneous pyroregions covering entire Switzerland as a study case and computed the length of the upslope paths within the forested areas, simulating ignition points on a systematic 100 × 100 m square grid. We then compared the obtained path lengths for each pyroregion with selected historical large forest fire statistics (e.g., mean area of the largest 5% of fires, maximum burnt area per fire) collected over the course of the last 30 years. This resulted in rather high R2 values, ranging from 0.558 to 0.651. The proposed approach was shown to allow for an easy identification and geo-localization of potential hotspots in terms of the likelihood for large fires to occur in mountainous regions, which is a prerequisite for a targeted planning of fire management measures aimed at preventing large fires and related post-fire gravitative natural hazards.

Effects of different pretreatments on thin-layer drying kinetics, vitamin A retention and rehydration of orange-fleshed sweet potato slices

Orange-fleshed sweet potato (OFSP) is a bio-fortified sweet potato with a high beta-carotene content. OFSP deteriorates rapidly due to its high moisture content. Conventional air drying is one of the most common methods employed but has the disadvantage of low rates of moisture removal, hence the longer drying time, which affects the quality of the final product. Pretreatments prevent quality deterioration and hasten the drying rate. This study investigated the effects of different pretreatments on thin-layer drying kinetics and the quality of OFSP slices. Three-mm-thick OFSP slices were pretreated using three different pretreatments: lemon juice, salt solution, and hot water blanching (HWB). Pretreated slices were dried in a solar dryer. Data collection (weight, temperature, relative humidity, and solar radiation) was done at 1-hour intervals. Four mathematical models (Page, Logarithmic, Modified Page, and Henderson and Pabis) were fitted to the experimental data. Also, the quality parameters of vitamin A and rehydration were investigated. The dryer inside temperatures and relative humidity ranged from 26.93–44.53 oC and 36.87%–65.30%, respectively. The moisture content of fresh OFSP slices for both treated and untreated samples was found to be in the range of 291.55–302.24% (dry basis, db), which reduced to 25.25–35.25% (db) after drying. The drying time ranged between 11 and 13 hours. The page model was found to be the best model for untreated samples, with R2 of 0.9948, RMSE of 0.0151, and χ2 of 0.0097, while the logarithmic model best described the drying behaviour of all the pretreated samples because it had the highest values of R2 and the lowest values of RMSE and χ2. Lemon juice-pretreated samples had the best quality parameters (vitamin A retention and rehydration ratio). A maximum rehydration ratio of 1.6765 was obtained for samples pretreated with lemon juice, and a minimum rehydration ratio of 1.1390 was obtained from HWB-pretreted samples. The results, therefore, indicate that lemon juice can be used as a pretreatment for thin-layer drying of OFSP slices. Pretreatments help minimise postharvest losses associated with OFSP, thus contributing to solving food and nutrition insecurity challenges.

Population Dynamics of Pod Borer <i>Helicoverpa armigera</i> (Hubner) on Chickpea

A field experiment was carried out to study the population dynamics of pod borer Helicoverpa armigera (Hubner) on chickpea at the Agronomy Farm, College of Agriculture, Swami Keshwanad Rajasthan Agricultural University, Bikaner during two consecutive seasons i.e. rabi 2021-22 and 2022-23. The incidence commenced from first and second week of December i.e. 49th and 50th Standard Metrological Week (SMW) and disappeared in the first fortnight of January. Incidence again started from the third week of January (3rd SMW) which increased gradually and reached peak (6.4 and 5.2 larvae/ m row) in the third and second week of February (8th and 9th SMW) during first and second season. These peaks coincided with the vegetative, flowering, and podding stages. The correlation studies showed that incidence has a positive significant correlation with temperatures (p<0.01) while, morning (r = -0.609 and -0.640, p<0.05) and evening relative humidity (r = -0.728, p<0.01 & -0.666, p<0.05) showed negative significant correlation. Further, stepwise linear regression model indicated that maximum temperature as single predicator independent variable had the strongest relationship with incidence based on high R2 value (61% and 63.40%). This forewarning regression model may help the farmers to take advance decision so as to minimize the damage caused by pod borer on chickpea.

Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, South-Central Chile, by analyzing time series of Landsat-8 OLI satellite images. The bio-optical parameters, i.e., chlorophyll-a (Chl-a, in mg·m−3) and turbidity (in NTU) were measured in situ during a satellite overpass to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process, reaching R2 values of 0.88 and 0.79 for Chl-a and turbidity, respectively. This underlies the importance of using inversion models, when processing water surfaces, to mitigate errors due to aerosols and the sun-glint effect. Subsequently, reflectance data derived from the ACOLITE model were used to establish correlations between various spectral indices and the in situ data. The empirical retrieval models (based on band combinations) yielding superior performance, with higher R2 values, were subjected to a rigorous statistical validation and optimization by applying a bootstrapping approach. From this process the green chlorophyll index (GCI) was selected as the optimal choice for constructing the Chl-a retrieval model, reaching an R2 of 0.88, while the red + NIR spectral index achieved the highest R2 value (0.79) for turbidity analysis, although in the last case, it was necessary to incorporate data from several seasons for an adequate model training. Our analysis covered a broad spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014–2022) characterized by a decline in Chl-a concentration in the lake, possibly attributable to governmental measures in the region for the protection and conservation of the lake. Additionally, the OLI imagery showed a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone, probably due to the heat island effect of the northern urban areas. The results of this study suggest a positive effect of recent local regulations and serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.

Hierarchical porous Pt/Hβ catalyst with controllable acidity for efficient hydrogenation of naphthalene

A series of hierarchical porous Pt/Hβ catalysts with different mesopores-acidity were synthesized without using secondary template. The pore structures of the catalysts were altered by constructing mesopores through alkali etching to reduce the mass transfer resistance. Moreover, the metal-support interaction (MSI) was optimized to improve the dispersion of the Pt metal particles. The smaller metal species could expose more active sites during the hydrogenation process, along with a more pronounced electron-deficiency effect. Furthermore, the effect of acid sites distribution on the hydrogenation reaction path and product distribution were also investigated. The strong acid-mesopores-metal factor (X) was defined to describe the relationship with the yield of decalin. The yield of decalin was well correlated with a high R2 value of 0.97. The Pt/Hβ-75 catalyst showed the best synergistic effect between acidity, mesopore and Pt species, leading to the optimal naphthalene conversion (96.7%) and higher yield of decalin (76.7%) at a relatively low temperature, which exhibited a better activity than other catalysts reported in literature for hydrogenation of naphthalene at a similar reaction condition. Besides, the hydrogenation reaction mechanism of naphthalene over Pt supported catalysts was further investigated by density functional theory, and the effect of Pt NPs size on the catalytic reaction process and activity was preliminarily revealed.

Electro-catalytic ozonation of contaminants in landfill leachate: Optimization by BBD, economic evaluation, mechanism, and reaction pathway

An electro-catalytic ozonation process with ZnO nano-granules as a catalyst (CFR-EC/O3-UV@ZnO process) was developed to treat raw landfill leachate. LC-MS analysis was applied to determine organic compounds. After CFR-EC/O3-UV@ZnO process, COD, TOC, BOD5, NH4+-N, P-PO4−3, color, and turbidity were reduced to 90.9%, 89.4%, 89.1%, 89.5%, 87.7%, 99.1%, and 99.7%, respectively under the optimum conditions of 10 g.hr−1 O3 dosage, pH 11.0, and 0.006 L.min−1 flowrate. At the same conditions removal efficiencies of heavy metals (HMs): Cr6+, Pb2+, As3+, Mg2+, B3+, Mn3+, Ni2+, Ba2+, Hg2+, Co2+, Fe2+, and Li+ were obtained 90%, 84.7%, 90%, 99%, 83.7%, 95%, 20%, 75%, 91%, 90%, 78% and 60%, respectively. In spite of high COD and BOD5 removal efficiencies, the process could not remove two organic substances including diphenylamine and 2,2,4-trimethyl-1,3-pentanediol. A high R2 value of 0.99 demonstrated the accuracy of the selected model. Economic evaluation depicted that the current process cost 0.95 $.m−3. Finally, this process is suggested for the treatment of landfill leachate with relatively high efficiency.