Maximum R2 Value Research Articles

Fabrication of CNC-AC bionanosorbents from the residual mass of Magnolia champaca l. Bark after methanol extraction for wastewater treatment: Continuous column adsorption study

In this current study, a new class of multifunctional biobased ecofriendly nanosorbents namely crystalline nanocellulose-activated char (CNC-AC) nanosorbents were fabricated by employing a much more innovative and beneficial solvent evaporation induced phase separation (EIPS) technique. While the crystalline nanocellulose (CNC) were extracted from a very much new, innovative, and beneficial agrowaste source namely banana tree (M. oranta) rachis fibers by conducting a series of chemical treatments like scouring, alkali treatment, bleaching, and acid hydrolysis reactions. Additionally, the biochar were synthesized from the residual mass of Magnolia champaca L. barks after methanol extraction and functionalized by 30 % H3PO4 to improve their overall properties. Besides these the fixed-bed continuous column adsorption study were carried out by optimizing the various influential factors such as preliminary concentration and flow rates of the inlet wastewater solution, along with the nanosorbent bed heights into the applied column. For better understanding the overall physicochemical, thermomechanical, and morphostructural behavior of the newly fabricated polyfunctional CNC-AC bionanosorbents the samples were characterized by conducting FTIR-ATR, FESEM, BET, XRD, TGA analysis. Meanwhile the treated and nontreated water specimens were examined by conducting AAS and UV–vis-NIR techniques. The obtained results recommended that the newly produced CNC-AC nanosorbents have contained a quite number of active edges/binding sites along with substantial sp. surface area (around 316.95 m2/g). Additionally, they possessed a crystallinity index about 59.98 ± 0.027 %, greater thermal steadiness up to 600 °C, and outstanding 2D honeycomb-like mesoporous peripheral surface microstructure with a promising spherical shapes and smaller size nearly 5–10 nm. The highest removal capacity were found at 538.91 mg/g and 455.70 mg/g for Pb2+ and CR respectively. Additionally, for better understanding the experimental breakthrough curves (BTC) were evaluated by several well established column models while the maximum R2 value was found around 0.999 for the Thomas model and reduced chi squire (χ2) value was around 0.0001.

Enhancing biogas production from vinasse through optimizing hydraulic retention time and added load using the response surface methodology

Vinasse, a byproduct of ethanol production from sugarcane, is a rich organic matter and poses environmental challenges due to its high pollutant content. Effective biomethane production from vinasse can mitigate its environmental impact by converting organic matter into a useful energy source while reducing its pollutant load. The biomethane production by anaerobic digestion (AD) process of the vinasse byproduct was examined on a laboratory scale. In this regard, several loads from 0.5 to 7 g VS/L were investigated to assess AD performance and methane production. This study investigated how two separate factors, namely the load and hydraulic retention time (HRT), affect both cumulative methane production (CMP) and methane yield (YCH4). This investigation utilized a response surface methodology known as the central composite design (RSM-CCD). Statistical analysis of variance (ANOVA) was employed to evaluate the effectiveness of the model generated. Thus, the model's fit, YCH4 has a maximum R2 value of 0.9759. The results revealed an astounding level of agreement between the experimental data and the proposed model. The RSM results revealed maximum CMP and YCH4 values of 409.82 ml and 178.95 ml/g VS respectively, obtained for optimum load values of 2.17 g VS/L and HRT of 15 h. The results emphasize the environmental and economic significance of AD, providing a sustainable waste management solution that helps reduce greenhouse gas emissions and organic pollution. Additionally, it generates valuable biogas and biofertilizers, presenting economic opportunities through renewable energy production and resource recovery. This approach not only alleviates the environmental burden of vinasse but also enhances the economic viability of ethanol production by creating additional revenue streams.

Advanced prediction of rice yield gaps under climate uncertainty using machine learning techniques in Eastern India

The current study focuses on applying machine learning approaches to forecast future Kharif rice yield gaps in eastern India while accounting for climate change implications. To achieve the United Nations Sustainable Development Goals (SDGs), food security must be prioritized. Rice yield has been predicted using Cubist, GBM, MARS, RF, SVM, and XGB machine learning methods, considering six factors: elevation, soil moisture, precipitation, temperature, soil temperature, and actual evapotranspiration. Climatic change scenarios were generated using the latest climatic Coupled Model Intercomparison Project Phase 6 (CMIP6 MIROC6) Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP5-8.5 datasets between 1990 and 2030. Finally, machine learning algorithms were used to identify rice yield gaps to achieve sustainable agricultural intensification. The rice yield validation was completed with 1889 field-based farmer observation records. The results suggest that Murshidabad and Purba Bardhaman districts had very high rice yields (5.60–3.45 t/ha) when using the Cubist model compared to another model. The findings also reveal a poor rice-yielding zone (1.44–0.39 t/ha) in the western region (Purulia) and a northwestern region (half of the west of Birbhum). The Cubist and RF models' maximum and minimum R2 values were 0.73 and 0.72, respectively. The R2 values were also negligible for the XGB, GBM, SVM, and MARS, machine learning models. Projections for rice production in 2030 indicate that the northern and north-eastern regions (Murshidabad and Purba Bardhaman) as well as the southeastern areas (Jhargram and Paschim Medinipur) have the highest yields, categorized as extremely very high (5.56–3.49 t/ha) and high (3.49–2.49 t/ha). A significant rice yield gap (50-40 %) was found in the center and south-east areas (Bankura, Jhargram, and Paschim Medinipur), the northern region (Murshidabad and Birbhum), and the western region (Purulia). In 2030, the north-western region (Birbhum), as well as the middle and south-eastern regions (Bankura, Jhargram, and Paschim Medinipur districts), had the highest proportion of high (50%–40 %) and very high (60%–50 %) rice yield gap. Our findings can contribute to a new viewpoint on agricultural planning and management for sustainable growth in the face of changing climate circumstances.

Interpretable machine learning-based analysis of coal fly ash flotation by conditioning process intensification

ABSTRACT Conditioning process intensification is necessary for improving the fly ash flotation effect. Understanding the relationship between conditioning parameters and flotation results is crucial for optimizing the conditioning-flotation process. This study developed a machine learning framework for predicting the yield, loss on ignition, and removal rate of unburned carbon using data from conditioning-flotation experiments. Feature variables of conditioning speed, impeller type, stirred tank diameter, collector dosage, and frother dosage were employed to develop five machine learning models. After tuning the hyper-parameters with GridSearchCV technique, it was found that Random Forest Regression model, eXtreme Gradient Boosting Regression model, and Gradient Boosting Regression model demonstrated good agreements between the predicted data and test data for the yield, loss on ignition, and removal rate of unburned carbon. The maximum R2 values were 0.9184, 0.8339, and 0.9434, respectively, while the minimum MSE values were 3.30, 5.82, and 20.59, respectively. The interpretable analysis using SHapley Additive exPlanations (SHAP) method indicated that conditioning speed was the most significant feature variable affecting the yield, loss on ignition, and removal rate of unburned carbon, while impeller type had a secondary impact. This investigation is expected to reveal the important role of conditioning process intensification in optimizing fly ash flotation.

A model for inversion of hyperspectral characteristics of phosphate content in mural plaster based on fractional-order differential algorithm

The Dunhuang murals, a significant part of Chinese heritage, have suffered deterioration primarily due to environmental and chemical factors, notably salt damage. This study proposes a sophisticated method that synergizes Fractional Order Differentiation (FOD) and Partial Least Squares Regression (PLSR) to accurately invert the phosphate content in the mural plaster of the Dunhuang paintings. The focal points of the research include: (1) To address the issue of information loss and reduced modeling precision caused by integer-order differentiation algorithms, the FOD method is employed for preprocessing spectral data. This approach ensures the fine spectral differences in the phosphate content of the mural plaster are precisely captured, (2) Utilizing PLSR, the study models the spectral bands identified at a significance level of 0.01 with measured conductivity values, thereby enabling the precise prediction of the phosphate content in the murals. The research outcomes reveal: (1) The FOD method can elucidate the nonlinear characteristics and variation patterns of the mural samples in the spectral data. As the order increases from zero to two, the number of spectral bands meeting the 0.01 significance test initially decreases and then increases. The highest absolute value of the positive correlation coefficient is observed at the 1.9 order, corresponding to the 2077 nm band, (2) For predicting the phosphate content in the murals, the model at the 1.9 order is most suitable for inversion. This model, after cross-validation, achieves a maximum R2 value of 0.861. This study created an efficient FOD-based model for estimating phosphate in mural plaster.

Predictive study of drying process for limonite pellets using MLP artificial neural network model

Due to the decline in high-grade iron ore production, the utilization of low-grade iron ore, such as limonite, has become necessary. Limonite contains a significant amount of bound water, which requires a drying process prior to use. Excessive heat stress caused by the evaporation of bound and free water during the drying of limonite pellets can lead to pellet disintegration and adversely affect gas-solid reactions. In recent years, artificial neural network (ANN) has been developing continuously in the fields of modeling and intelligent control, and has been widely used. Many predecessors used artificial neural network model to study the drying process of natural organic matter, and analyzed the factors affecting the drying rate of organic matter. In this study, we employed big data analysis, specifically Multilayer Perceptron (MLP) artificial neural networks, to analyze the drying process of limonite pellets and successfully established a predictive drying model applicable to limonite pellets. The MLP artificial neural network demonstrated excellent fitting between predicted and experimental values, with a maxi-mum R2 value of 0.999. The artificial neural network for drying developed in this study provides technical guidance for industrial material drying, reduces the workload of manual measurements, and minimizes energy consumption.

A Novel Hyperspectral Salt Assessment Model for Weathering in Architectural Ruins

Abstract. The Dunhuang murals, a significant part of Chinese heritage, have suffered deterioration primarily due to environmental and chemical factors, notably salt damage. This study proposes a sophisticated method that synergizes Fractional Order Differentiation (FOD) and Partial Least Squares Regression (PLSR) to accurately invert the phosphate content in the Mural Plaster of the Dunhuang paintings. The focal points of the research include: 1) To address the issue of information loss and reduced modeling precision caused by integer order differentiation algorithms, the FOD method is employed for preprocessing hyperspectral data. This approach ensures the fine spectral differences in the phosphate content of the Mural Plaster are precisely captured, 2) Utilizing PLSR, the study models the spectral bands identified at a significance level of 0.01 with measured conductivity values, thereby enabling the precise prediction of the phosphate content in the murals. The research outcomes reveal: 1) The FOD method can elucidate the nonlinear characteristics and variation patterns of the mural samples in the hyperspectral curve.As the order increases from zero to two, the number of spectral bands meeting the 0.01 significance test initially decreases and then increases. The highest absolute value of the positive correlation coefficient is observed at 1.9 orders, corresponding to the 2077 nm band, 2) For predicting the phosphate content in the murals, the model at 1.9 orders is most suitable for inversion. This model, after cross-validation, achieves a maximum R2 value of 0.783. This study created an efficient FOD-based model for estimating phosphate in mural plasters.

A novel study on power consumption of an HVAC system using CatBoost and AdaBoost algorithms combined with the metaheuristic algorithms

Efficient power management is fundamental for organizations and systems to conserve resources, reduce costs, and improve environmental sustainability. To achieve the mentioned purposes, it is vital to perceive power consumption patterns and trends to make informed decisions about energy usage. By evaluating power consumption, inefficiencies and areas for improvement can be identified. In this regard, this paper presents a comprehensive study to predict power usage in a non-residential building HVAC system. The study employs the integration of machine learning models and optimization algorithms to achieve accurate predictions. The results demonstrate the effectiveness of the approach, with the CatBoost-AO model depicting superior performance in comparison with other models across a range of statistical evaluation metrics including NSE, JSD, RMSE, MAPE, VAF, KLD, as well as R2 and runtime. Remarkably, the CatBoost-AO model achieves the maximum R2 value of 0.91, indicating strong predictive capability. Overall, the presented study highlights the potential of employing machine learning and optimization techniques to improve power management and resource efficiency in HVAC systems, contributing to more sustainable and cost-effective operations in non-residential buildings.

Parametric study on thermal performance augmentation of TiO2/water nanofluids flowing a tube contained with dual counter twisted-tapes

The study of compound heat transfer enhancement technique using dual counter twisted tapes (DCTs) together with TiO2/water nanofluids was carried out. The dual twisted tapes in form of counter-swirl tape arrangement were utilized at three twist ratios (y/w = 1.5, 2.0 and 2.5). TiO2/water nanofluids having three different volume concentrations (φ = 0.05, 0.10 and 0.15 %) were employed as the testing fluids. The results indicated that heat transfer rate, friction factor and thermal enhancement index rose with decreasing tape twisted ratio and elevating nanofluid concentration. The TiO2/water nanofluids applied in the present work offered higher Nu than pure water (the base fluid) by 7.3–10.0 %. The application of DCT with the smallest y/w of 1.5 and TiO2/water nanofluids with the largest φ of 0.15 vol% led to the highest thermal enhancement index (TEI) of 1.53, under the same pumping power criteria. Additionally, the k-nearest neighbor (k-NN) and artificial neural network (ANN) were developed to predict the thermal enhancement index (TEI). It was found that the k-NN and ANN models provided maximum R2 values of 0.919 and 0.992 f, respectively.

Synergizing LED Technology and Hydropriming for Intelligent Modeling and Mathematical Expressions to Optimize Chickpea Germination and Growth Indices

The influence of hydropriming and Light Emitting Diodes (LED) on germination and growth indices, followed by optimizing and validation via artificial intelligence-based models was carried out in this research. White LEDs (W-LEDs) were more effective by yielding the most effective growth indices, such as mean germination time (MGT) (1.11 day), coefficient of variation of germination time (CVt) (20.72%), mean germination rate (MR) (0.81 day−1), uncertainty (U) (0.40 bit), and synchronization (Z values) (0.79); the optimum MGT (1.09 day), CVt (15.97%), MR (0.77 day−1), U (0.32 bit), and Z (0.55) values were found after 2 h of hydropriming, which was responsible for all efficient growth indicators. W-LEDs with 1 h hydropriming proved to be the ideal LED and hydropriming combination. Results on growth indices for in vitro seedlings were completely different from those on germination indices, and the most desirable germination indices were linked to red LEDs (R-LEDs). Whereas 4 h hydropriming was most effective for the post-germination process. Pareto charts, normal plots, contour plots, and surface plots were created to optimize the input variables. Finally, the data were predicted using Arificial Neural Network (ANN) inspired multilayer perceptron (MLP) and machine learning-based random forest (RF) algorithms. For both models, plant height was correlated with maximum R2 values. Whereas, all output variables had relatively low mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) scores, indicating that both models performed well. The results of this investigation disclosed a link between certain LEDs and hydropriming treatment for in vitro germination indices and plant growth.Graphical Graphical presentation of actual and predicted values for germination indices in chickpea

H2S Removal from Biogas using Steel Wool (Fe2O3) Adsorption Combined with Water Absorption: Experimental and Modelling via RSM

Hydrogen sulfi de (H2S) is a hazardous gas commonly found in biogas systems; it causes problems in biogas utilization because H2S is highly corrosive to engines, gas storage tanks, and generators. The purpose of this research was to study the biogas upgrading by removing H2S using SCM440 type steel wool (Fe2O3) adsorption combined with water absorption. The biogas upgrading column was operated at a biogas fl ow rate of 0.5–3 l/min, with 10–50 g of treated Fe2O3 adsorbent packed in the middle of the column, and 60–300 ml of water contained at the bottom of the column. The optimal conditions obtained by the RSM for maximum H2S removal were 1 l/min gas fl ow rate, 40 g Fe2O3 and 240 ml water, can be H2S removal of 50.79 g/m3 biogas. Statistical, ANOVA analysis showed that Fe2O3 was the most signifi cant factor followed by water and gas fl ow rate. RSM models showed acceptable prediction of experimental data with maximum R2 value of 0.9804. The Fe2O3 regeneration conditions were examined at air fl ow rates of 1–4 l/min, regeneration times of 1–4 h, and 1–4 repeated uses for H2S removal. The optimum condition was found at a 2 l/min air fl ow rate for 2 h, resulting in a maximum H2S removal of 97.39%. Hence, the Fe2O3 adsorbent combined with water absorption technique has high potential for environmentally friendly and low-cost removal of H2S from biogas at an industrial level.

Sustainable energy enhancement strategy from Chlorella vulgaris microalgae biomass resource

ABSTRACT Growing demand for renewable and sustainable energy sources has prompted research into bioethanol production as a viable alternative to hydrocarbon fuel for automobile engines. Based on their high carbohydrate content, Chlorella vulgaris microalgae were chosen for this study as a feasible biomass for bioethanol synthesis. After conducting microwave-based acid hydrolysis with varying acid concentrations of sulfuric acid (H2SO4) for 1–15 min, this study found that 3% H2SO4 for 5 min yielded the highest yield of reducing sugar (6.77 g/L). In this work, the RSM approach of central composite design (CCD) was utilized for modeling and optimization of ethanol fermentation and the MATLAB Simulink approach was employed for the simulation of ethanol fermentation. This study compared the effects of using Saccharomyces cerevisiae yeast for fermentation at different fermentation times (12 to 48 h), temperatures (28 to 32°C), and size of inoculum (0.5–1.5 g/L). Based on the actual and predicted results, the best conditions for fermentation were 36 h of time, 30°C of temperature, and 1.5 g/L of inoculum size. This gave the maximum ethanol concentration of 3.31 g/L. Furthermore, the RSM method provides minimum error and maximum R2 value results for ethanol production compared to MATLAB simulation prediction (MSP). In addition, residue biomass from biofuel production is evaluated for its suitability for a further form of biofuel production. This research explores the opportunities for zero-waste biomass utilization in the development of biofuels.

Solar drying of oregano leaves (Plectranthus amboinicus, Lour): Analysis of experimental performance under a tropical climate

Despite the multiple applications of the oregano ( Plectranthus amboinicus (Lour)), an important herbaceous plant because of its medicinal and culinary properties, limited research has been conducted on this species’ solar drying. Thus, the experimental drying of oregano leaves in a tropical climate was performed using direct and indirect solar drying and an electric oven in this research work. Also, mathematical modeling of the drying process, moisture diffusivity, activation energy, and colorimetric analysis (as a quality parameter) were accomplished to complement the experimental evaluation and understand the drying process behavior. The results show a shorter drying time obtained with the electric oven with 210 min reached at 65°C; however, the minimum solar drying time (250 min) was achieved with a maximum drying rate of 0.025 g water/g dry matter per minute and minimum final moisture between 0.28 and 0.13 g water/g dry matter was obtained with indirect solar dryer. Moreover, the models that better represented the experimental data were Modified Page, Page, and Logarithmic, with a maximum value of R2 obtained of 0.9980 corresponding to the Logarithmic model. Furthermore, a maximum effective diffusivity value of 1.81E-9 was obtained with direct solar dryer natural convection, while for the electric oven, were 1.14E-10, 2.280E-10, and 1.026E-09 for 45, 55, and 65°C, respectively. The activation energy for water diffusion in oregano leaves was 55.66 and 97.78 kJ/g mol for solar dryers and electric ovens, respectively, which were values very close to those reported in the literature. Finally, the colorimetric analysis exhibited a minimum color change value of 17.1 obtained for the indirect solar dryer, allowing better oregano preservation and maintaining its quality for the market, which makes it ideal for drying oregano in tropical climates and promotes solar drying use for sustainable growth of the region.

Research on cooling load estimation through optimal hybrid models based on Naive Bayes

Cooling load estimation is crucial for energy conservation in cooling systems, with applications like advanced air-conditioning control and chiller optimization. Traditional methods include energy simulation and regression analysis, but artificial intelligence outperforms them. Artificial intelligence models autonomously capture complex patterns, adapt, and scale with more data. They excel at predicting cooling loads influenced by various factors, like weather, building materials, and occupancy, leading to dynamic, responsive predictions and energy optimization. Traditional methods simplify real-world complexities, highlighting artificial intelligence’s role in precise cooling load forecasting for energy-efficient building management. This study evaluates Naive Bayes-based models for estimating building cooling load consumption. These models encompass a single model, one optimized with the Mountain Gazelle Optimizer and another optimized with the horse herd optimization algorithm. The training dataset consists of 70% of the data, which incorporates eight input variables related to the geometric and glazing characteristics of the buildings. Following the validation of 15% of the dataset, the performance of the remaining 15% is tested. Based on analysis through evaluation metrics, among the three candidate models, Naive Bayes optimized with the Mountain Gazelle Optimizer (NBMG) demonstrates remarkable accuracy and stability, reducing prediction errors by an average of 18% and 31% compared to the other two models (NB and NBHH) and achieving a maximum R2 value of 0.983 for cooling load prediction.

Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha.

Performance analysis of indirect solar dryer with natural heat energy retention substances for drying red chilli

The world is grappling with substantial food waste after cultivation, mostly due to increased moisture contents. Therefore, the objective of this study concentrates on developing an innovative indirect methodsolar dryer designed to eliminate moisture content by incorporating glass pieces and paraffin wax as Natural Heat Energy Retention Substances (NHERS) in the solar collector. Three distinct dryer setupshave been designed to assess the effectiveness of the suggested innovation namely a collector without NHERS, a collector with glass pieces as NHERS and a collector with combined glass pieces and paraffin wax as NHERS for drying red chilli. Along with these three different dryer setups, open solar drying was done. From the experimental observations, the drying kinetics, energy, exergy and colour analyses were conducted. Red chilli's wetnesscontent dropped from 83.8% (wet basis) in the beginning to 7.3% at the end in 28, 44, 54 and 72 h under the dryer with combined glass pieces and paraffin wax as NHERS, dryer with glass pieces as NHERS, dryer without NHERS and traditional sun drying respectively. The maximum thermal efficiency of the solar collector is determined to be 57.9% and the highest dryer efficiency is 67.2% in the same setup. The maximum exergy efficiency in the dryer with combined glass pieces and paraffin wax as NHERS is 53.7%. Moreover, ten drying models were employed to forecast the MR and the optimal model to describe red chillies drying properties wasthe logarithmic model with the lowest χ2 (0.0008843) and maximum R2 (0.9682) value. The colour of dried red chilli was best in the dryer with combined glass pieces and paraffin wax as NHERS. From these investigations, the developed indirect solar dryer with combined glass pieces and paraffin wax NHERS indicates sustainable drying time and improved product quality.

Intercomparison of leaf area index observed by destructive sampling, plant canopy analyzer and tracing radiation and architecture of canopies in paddy fields

ABSTRACT Leaf area index (LAI) is a canopy structure parameter used to characterize vegetation growth. It is increasingly important to obtain continuous and accurate ground LAI measurement data for the validation of remote-sensing products. In this study, direct destructive sampling method and indirect optical measurement including LAI-2200 plant canopy analyser (PCA) and Tracing Radiation and Architecture of Canopies (TRAC) were used for long-term continuous observation of paddy fields in southern China, to explore the variation of LAI during the growth period of paddy, and to quantitatively analyse the errors of indirect optical measurement. The results show that (1) LAI and Plant area index (PAI) increased first and then decreased in the growing period. The proportion of green leaf area gradually decreased at reproductive growth stage (less than 50%). There was a linear relationship between the proportion of green leaf area and PAI at this stage, with a good correlation (R2 = 0.73). This linear relationship can be used to convert PAI into LAI more conveniently. (2) Effective PAI (PAIe) calculated by the two methods (2000 method and Lang method) from PCA showed good consistency between each other in the growing season (the maximum value of R2 is 0.99). With the increase of zenith angle, the consistency between 2000 method and Lang method decreases (R2 = 0.83). Multiple scattering has an effect on PCA measurement, and this effect is more obvious at high zenith angle. Lang method is more sensitive to the change of zenith angle. (3) Compared to the destructive values, both 2000 method and Lang method are able to compute accurate LAI using four ring sensors (4 R), while TRAC underestimates the LAI and PAI from destructive sampling method. This study provided a new idea for reducing the impact of ground reference data errors on LAI product validation.

Assessment of linkage disequilibrium and haplotype block structure in indigenous cattle populations of Tamil Nadu, India usingwhole genome sequence data

Understanding the nature of linkage disequilibrium (LD) between molecular markers reflects the degree of non-random association between their alleles, which is essential for genome-wide association studies. The main objective of this study was to assess the chromosome-wise linkage disequilibrium and haplotype block structure in indigenous cattle breeds of Tamil Nadu, India. A total of 79 animals belonging to five indigenous cattle breeds were considered as single indigenous cattle population and three samples per breed were sequenced using Illumina NovaSeqTM 6000 and Illumina HiSeq 2500. The LD parameter (r2) was estimated for a total of 62,095,778 pair- wise SNPs for all autosomes. The chromosome-wise number of SNP pairs, mean r2±standard deviation and median r2 values were 2,141,234, 0.484±0.246 and 0.493, respectively. Chromosome-wise mean r2 of different distance bins were calculated and it showed the maximum r2 value of 0.839 at 31-70 kb distance for Chromosome 11 and minimum r2 value of zero for Chromosome 7, 11, 15, 20 and 29, respectively at a distance 71-100 kb. This study also revealed a total of 413, 277 haplotype blocks which covered 2.34% of the autosomal genome. There was a total of 1,589,118 SNPs distributed within the haplotype blocks, covering a total length of 53.62 mb. The results of the study suggest the need for breed-specific reference populations for indigenous cattle breeds, which have a greater density of molecular markers of economic significance and thereby to identify breed-specific haplotypes in future.

A study on urban block design strategies for improving pedestrian-level wind conditions: CFD-based optimization and generative adversarial networks

Urban block layout design presents a critical and challenging task for urban planners, as block layout directly influences the physical structure of the urban area, the interaction between people and their environment, and the microclimate in urban areas. Thus, developing appropriate design strategies plays a decisive role in this process. This research uses the generative adversarial networks (GAN) technique to explore block layout design strategies. The GAN technique can effectively generate real and diverse urban blocks based on learning existing morphological properties. Therefore, it can be a powerful approach to urban block layout research. Genetic algorithms not only help to identify the ideal design solution but also enable the summary of key design strategies by numerous evolutionary periods. The study presents a CFD-based optimization framework that utilizes a genetic algorithm and 3D block models generated by the GAN technique to improve the urban wind conditions at the block scale. By comparing solutions, the study successfully optimized three objectives by 68.36%, 51.74%, and 41.83%. Ridge regression models examined the relationship between objective functions and urban morphological indices. The maximum R2 value of the ridge regression models reached 0.801, indicating that the models can effectively predict wind conditions by morphological indicators. Design strategies for wind-friendly blocks were developed by regression models and validated by case studies. The design strategies suggest that architects should prioritize the building layout, the building height at boundary areas, and the building shape, as these factors significantly affect the urban outdoor wind conditions.

Precise control mode for concrete vibration time based on attention-enhanced machine vision

In concrete construction, vibration is an essential process used to ensure the structural soundness and long-term durability of concrete structures. Automating concrete vibration monitoring enables far more precise control compared to subjective human judgments. In this paper, a control mode for concrete vibration time used on the vibrating robot is proposed. The control mode utilizes YOLOv8 model to recognize the best-vibrating position and eliminate the rebar part of concrete surface image and employs attention-enhanced squeeze-and-excitation neural network to regress the vibrating completion degree. After deployment to the embedded system of vibrating robot, experimental results demonstrate superior performance of the proposed method over state-of-the-art algorithms with the highest recognition rate and maximum R2 value. The control mode enhances vibration quality while liberating workers from manual tasks. A commercial mall construction project validates the practicality of the presented control model.