High R2 Values Research Articles

Evaluating Methods for Mapping Historical Redlining to Census Tracts for Health Equity Research.

Neighborhood characteristics including housing status can profoundly influence health. Recently, increasing attention has been paid to present-day impacts of "redlining," or historic area classifications that indicated less desirable (redlined) areas subject to decreased investment. Scholarship of redlining and health is emerging; limited guidance exists regarding optimal approaches to measuring historic redlining in studies of present-day health outcomes. We evaluated how different redlining approaches (map alignment methods) influence associations between redlining and health outcomes. We first identified 11 existing redlining map alignment methods and their 37 logical extensions, then merged these 48 map alignment methods with census tract life expectancy data to construct 9696 linear models of each method and life expectancy for all 202 redlined cities. We evaluated each model's statistical significance and R2 values and compared changes between historical and contemporary geographies and populations using Root Mean Squared Error (RMSE). RMSE peaked with a normal distribution at 0.175, indicating persistent difference between historical and contemporary geographies and populations. Continuous methods with low thresholds provided higher neighborhood coverage. Weighting methods had more significant associations, while high threshold methods had higher R2 values. In light of these findings, we recommend continuous methods that consider contemporary population distributions and mapping overlap for studies of redlining and health. We developed an R application {holcmapr} to enable map alignment method comparison and easier method selection.

Abstract 335: BD®OMICS-Guard Sample Preservation Buffer preserves mRNA and cell surface epitopes for single-cell sequencing applications

Abstract Multiomics has emerged as a powerful tool to derive insights on the transcriptomic and proteomic data of human cancer samples and preclinical models of blood and tumor malignancies. Due to the sensitivity of sequencing techniques, sample quality and stability can potentially interfere with results. BD® OMICS-Guard Sample Preservation Buffer (SPB), a novel preservation solution, allows cells and bulk tissues to be stored for up to 72h at 4 °C, while reliably capturing the true biology of cells. This reagent gently preserves mRNA and protein integrity without the traditional cross-linking and harsh fixatives, increasing flexibility and expanding options for study designs. Here, we showcase the preservation properties ofthis reagent for multiomic readouts in human cells and mouse tissue across three different timepoints.BD® OMICS-Guard SPB’s preservation of mRNA from human PBMC and bulk mouse spleen tissues wasevidenced by WTA for differentially expressed genes in samples preserved for 24, 48 and 72h comparedto fresh/unpreserved controls. Protein epitope preservation was demonstrated via CITE-Seq assay withthe BD® AbSeq Human Immune Discovery Panel (human PBMC) or a 30-plex panel of anti-mouse BD®AbSeq Antibody-Conjugated Oligonucleotides (mouse splenocytes) and confirmed with multicolor flowcytometry. For both sample types, high correlation R2 values (>0.87) were calculated for the preservation time points versus fresh controls. Concomitant flow cytometry analysis of major cell types and surface protein expression was consistent with sequencing data for all samples. Specificity and sensitivity of major cell type markers and cell surface proteins across cell types and time is visualized in the BD®AbSeq Antibody-generated heatmap for each sample. Additionally, BD® OMICS-Guard SPB maintains mRNA integrity in targeted gene assays and VDJ full-length assays, suggesting robust preservation of select transcripts readily interrogated in single-cell profiling. Batch effect across time and technical replicates for both sample types was limited with the BD Rhapsody™ HT Xpress System and delineated by donor/sample specific tSNEs across time. We demonstrate that the BD® OMICS-Guard Sample Preservation Buffer effectively preserves both mRNA and surface proteins for single-cell experiments. BD® OMICS-Guard SPB allows the convenient preservation of samples such as tumor biopsies to be collected and stored for up to 72h at 4 °C while maintaining mRNA and protein integrity, allowing the flexibility for precious samples to be potentially shipped to a central sequencing site to ensure standardized sequencing workflows. For Research Use Only. Not for use in diagnostic or therapeutic procedures.BD and BD Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2023 BD. All rights reserved. Citation Format: Samuel X. Shi, Cynthia Sakofsky, Zhiqi Zhang, Samatha Vadrevu, Xiaoshan Shi, Hye-Won Song, Aruna Ayer. BD®OMICS-Guard Sample Preservation Buffer preserves mRNA and cell surface epitopes for single-cell sequencing applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 335.

Comparison of Water Quality Prediction for Red Tilapia Aquaculture in an Outdoor Recirculation System Using Deep Learning and a Hybrid Model

In modern aquaculture, the focus is on optimizing production and minimizing environmental impact through the use of recirculating water systems, particularly in outdoor setups. In such systems, maintaining water quality is crucial for sustaining a healthy environment for aquatic life, and challenges arise from instrumentation limitations and delays in laboratory measurements that can impact aquatic animal production. This study aimed to predict key water quality parameters in an outdoor recirculation aquaculture system (RAS) for red tilapia aquaculture, including dissolved oxygen (DO), pH, total ammonia nitrogen (TAN), nitrite nitrogen (NO2–N), and alkalinity (ALK). Initially, a random forest (RF) model was employed to identify significant factors for predicting each parameter, selecting the top three features from routinely measured parameters on the farm: DO, pH, water temperature (Temp), TAN, NO2–N, and transparency (Trans). This approach aimed to streamline the analysis by reducing variables and computation time. The selected parameters were then used for prediction, comparing the performance of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM models across different epochs (1000, 3000, and 5000). The results indicated that the CNN–LSTM model at 5000 epochs was effective in predicting DO, TAN, NO2–N, and ALK, with high R2 values (0.815, 0.826, 0.831, and 0.780, respectively). However, pH prediction showed lower efficiency with an R2 value of 0.377.

Assessing the Efficacy of UV-C Light for the Inactivation of Phytopythium and Phytophthora Species in Water: A Mathematical Modeling Approach Using an Ultraviolet Light-Emitting-Diode System

The use of untreated, recirculated irrigation water is a profitable practice, but it can harbor plant pathogens such as Phytophthora ( Pt) cinnamomi, Pt. nicotianae, Phytopythium ( Pp) vexans, and Pp. helicoides, posing disease risks in crop production. UV-C (240 to 290 nm) light-emitting diode (LED) irradiation was evaluated for inactivating the zoospores of oomycetes in water, providing potential for effective treatment. Zoospore suspensions were exposed to quantifiable UV-C doses under dynamic stirring, calculated by multiplying fluence rate (mW cm−2) and exposure time(s). UV-C irradiation effectively inactivated the tested pathogens following linear (logarithmic linear [ Pt. cinnamomi and Pt. nicotianae]) and nonlinear (Weibull and Weibull + tail [ Pp. vexans and Pp. helicoides]) kinetics as identified by parameters of goodness of model fit: high R2 and low root mean square error (RMSE) values. D10 values of Pt. cinnamomi and Pt. nicotianae calculated from the rate constants ( Kmax) of the log-linear models were 5.05 ± 0.38 mJ cm−2 ( R2 = 0.95, RMSE = 0.091, Kmax = 0.49 ± 0.03) and 4.73 ± 0.33 mJ cm−2 ( R2 = 0.96, RMSE = 0.091, Kmax = 0.49 ± 0.03), respectively. The Weibull + tail model best described the inactivation of Pp. vexans ( R2 = 0.97, RMSE = 0.099) and Pp. helicoides ( R2 = 0.96, RMSE = 0.083), and the δ and Nres values were determined to be 9.88 ± 0.12 and 2.06 ± 0.06 and 11.47 ± 0.38 and 2.45 ± 0.05, respectively. These study findings can be applied to develop water disinfection systems, either alone or in combination with other methods, for effective control of pathogens in irrigation systems. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

A novel CFD-ANN approach for plunger valve optimization: Cost-effective performance enhancement

This paper introduces a novel computational fluid dynamics-artificial neural network (CFD-ANN) approach that has been devised to enhance the efficiency of plunger valves. The primary emphasis of this research is to achieve an optimal equilibrium between hydraulic flow and geometric configuration. This study is a novel contribution to the field as it explores the flow dynamics of plunger valves using Computational Fluid Dynamics (CFD) and proposes a unique methodology by incorporating Machine Learning (ML) for performance forecasting. An artificial neural network (ANN) architecture was developed using a thorough comprehension of flow physics and the impact of geometric parameters acquired through computational fluid dynamics (CFD). Using optimization, the primary aspects of the Artificial Neural Network (ANN), including the learning algorithm and the number of hidden layers, have been modified. This refinement has resulted in the development of an architecture exhibiting a remarkably high R2 value of 0.987. This architectural design was employed to optimize the plunger valve. By utilizing Artificial Neural Networks (ANN), a comprehensive analysis comprising 1000 distinct configurations was effectively performed, resulting in a significant reduction in time expenditure compared to relying on Computational Fluid Dynamics (CFD). The result was a refined arrangement that achieved maximum head loss, subsequently verified using computational fluid dynamics (CFD) simulations, resulting in a minimal discrepancy of 2.66%. The efficacy of artificial neural networks (ANN) becomes apparent due to their notable cost-efficiency, along with their capacity to produce outcomes that are arduous and expensive to get through conventional optimization research utilizing computational fluid dynamics (CFD).

Utilization of response surface methodology to optimize the mechanical behaviour of flax/nano TiO2/Epoxy based hybrid composites under liquid nitrogen environment

Linum usitatissimum commonly known as flax fibers, emerges as a promising reinforcement phase for artificial polymer resins, boasting ecological benefits, low density, and easy accessibility. However, the mechanical behavior of such composites hinges crucially on factors such as fiber mat thickness, nanoTiO2 filler content, and the application of cryogenic treatment. Addressing this complex interplay, this study employs a hand lay-up technique for composite construction, subjecting nanocomposite plates to the challenging liquid nitrogen conditions at 77 K post-manufacture. Recognizing the need for an optimized approach, Response Surface Methodology (RSM) based on Box-Benhken designs is employed to enhance the mixing features of linum usitatissimum polymer composites. The study calculates anticipated mechanical strength values through rigorous ANOVA inferential analysis, uncovering the pivotal roles played by fiber mat thickness, nanofiller content, and cryogenic treatment in the two feature interactions (2FI) model components. The methodology proves robust with high R2 values (0.9670 for tensile, 0.9845 for flexural, and 0.9670 for interlaminar shear strength) consistently aligning with experimental findings. The study culminates in identifying optimized parameters for maximal mechanical properties—300 gsm flax fiber thickness, 5 wt.% nano TiO2 concentration, and a 15-min cryogenic treatment—a result that advances our understanding of fundamental factors influencing composite performance and provides practical guidelines for applications in fields requiring superior mechanical strength in challenging environments.

Study of color degradation in package prints: Analyzing kinetics with principal component analysis

AbstractColor degradation is a pressing issue in various fields like food and medicine packaging, as well as printed materials, leading to a loss or distortion of vital information. Therefore, understanding how the color of package prints evolves over time is essential for product quality assessment and effective product life cycle management. This study focuses on investigating color changes in prints on blister foils, widely used in the medicine and packaging industries. To assess fading characteristics, samples printed via the gravure process have been exposed to a Xenon Test Chamber (B‐SUN). Spectrophotometric measurements of the prints have been conducted using a spectroradiometer, and the data have been analyzed using principal component analysis (PCA). The fading nature of cyan and black prints aligns with Type IV, while magenta and yellow prints correspond to Type II, as described by Giles. Notably, cyan and black inks demonstrated resistance toward fading at the end of the exposure, with a minor exception. The investigation involved curve fitting, revealing a consistent first‐order kinetic model governing fading rates throughout the exposure time. High R2 values (0.96–0.99) and low root means square error values (0.02–0.08) are observed across all cases. While color fading involves diverse kinetics, the fading rate consistently adheres to a singular first‐order kinetic path. The study highlights that yellow ink is most sensitive to light, followed by magenta ink. These findings offer valuable insights for optimizing packaging materials and ensuring product quality in industries reliant on printed materials. By mitigating color degradation, manufacturers can enhance the longevity and appeal of their products, reinforcing consumer trust and satisfaction. Furthermore, the effective utilization of PCA as a data analysis tool offers a valuable approach for future investigations in color science and related fields.

Mango quality prediction based on near-infrared spectroscopy using multi-predictor local polynomial regression modeling

Background pH and total soluble solids (TSS) are important quality parameters of mangoes; they represent the acidity and sweetness of the fruit, respectively. This study predicts the pH and TSS of intact mangoes based on near-infrared (NIR) spectroscopy using multi-predictor local polynomial regression (MLPR) modeling. Herein, the prediction performance of kernel partial least square regression (KPLSR), support vector machine regression (SVMR), and MLPR is compared. Methods For this purpose, 186 intact mango samples at three different maturity stages are used. Prediction models are built using MLPR, KPLSR, and SVMR based on untreated and treated spectra. The best regression model for predicting pH is MLPR based on Gaussian filter smoothing spectra. Moreover, the TSS value is more accurately predicted using MLPR based on Savitzky–Golay smoothing. Results The findings reveal that MLPR is highly accurate in estimating the pH and TSS of mangoes, with mean absolute percentage error (MAPE) values less than 10 %. In addition, the MLPR model has the best predictive performance with the lowest Mean Squared error (MSE) and root mean squared error (RMSE) values and the highest R2 value. Conclusions The use of NIR spectroscopy in combination with multi-predictor local polynomial regression could provide a quick and non-destructive technique for predicting mango quality. Thus, the results of this study help support sustainable production as a sustainable development goal.

Synthesis of bimetallic Mn@ZIF–8 nanostructure for the adsorption removal of methyl orange dye from water

In this research, manganese-based Mn@ZIF-8 nanocomposite was prepared using solvothermal strategy. The resultant Mn@ZIF-8 nanostructure was assessed for the adsorption of methyl orange (MO) dye from water. The pristine ZIF-8 and Mn@ZIF-8 nanocomposite have demonstrated a rhombic dodecahedral morphology having a reasonable microporous structure. Besides, they have also displayed an outstanding specific surface area and porosity. The impact of contact time, concentration and variation in pH were analyzed in detail for the investigation of adsorption capacity. From the interpretation of the adsorption isotherms, it has been discovered that adsorption of MO over Mn@ZIF-8 follows the monolayer mechanism (Langmuir isotherm). Remarkable maximum adsorption capacity (qmax, 406 mg.g−1) has been recorded for the Mn@ZIF-8 which is sufficiently high as compared to pristine ZIF-8. The kinetic study explored that experimental data best fitted to the pseudo second order kinetics with relatively high R2 values (0.997) of Mn@ZIF-8. Thermodynamic study reconnoitered the adsorption process is spontaneous in nature. The synthesized Mn@ZIF-8 material also displays exceptional reusability capability of up to 92 % at 4th cycle compared to 1st cycle. Overall, the Mn@ZIF-8 could be a good contestant for the treatment of MO containing wastewater of industries.

Foliar applications improves grapevine plant cold hardiness

Understanding and enhancing the cold hardiness of vines is crucial for viticulture, as it directly impacts the sustainability and productivity of vineyards in diverse climatic regions. This study, therefore, meticulously examined grapevine responses to various treatments and sampling times, revealing nuanced dynamics in cold tolerance, physiological parameters, and growth outcomes. Our study found that treatments like K2SO4 significantly enhanced grapevine cold hardiness. Specifically, K2SO4 showed high R² values, indicating a substantial improvement in low-temperature hardiness for primary buds and in processes like bud burst and sprouting. Our results also revealed significant differences in cold hardiness across various treatments and sampling times, with K2SO4 leading to the highest LT50 value for primary buds. Additionally, in single-bud cuttings, K2SO4 application improved viability rates, root and shoot growth, and rooting rates, particularly observed in January. Growth parameters in single-bud cuttings further illustrated the impact of treatments, with the viability rate of primary buds being 61.5 % in the K2SO4 application and 62.0 % in the CaCl2 application. The highest root length of 2.5 cm and shoot length of 3.4 cm, along with a rooting rate of 61.0 %, root scale of 1.4, root number of 6.9, and basal callus rate of 24.6 % were obtained from the K2SO4 application. Our research found that the applications of K2SO4 and CaCl2 increased the cold hardiness of the buds and phloem tissue of the vine by approximately 1 °C compared to the control. It has been concluded that K2SO4 and CaCl2 applications during development can reduce the cold injury rate and economic losses in vines in areas with high risk of winter cold damaging events. The comprehensive findings of this study offer actionable insights that can significantly contribute to the refinement of vineyard management practices, promoting optimal grapevine performance and hardiness in the face of varying cold conditions.

Air Quality Changes under the Influence of Cut-Off Lows and Omega Blocking Patterns and Associated Health Risks in a Coastal Mediterranean Region: Case of Bizerte, Northern Tunisia

The study of the daily and diurnal variability of NO, NO2, surface O3 and PM10 was carried out in urban Bizerte (Northern Tunisia), from January to March, 2013. The main results showed three distinct periods depending on multi-scale sources and meteorology. The first period, (≈20% of total cases) influenced by moderately stable conditions, was characterized by a slight excess of NO due to traffic emissions. The second period, (≈ 60% of total cases) associated with moderately unstable conditions, was devoid of any significant impact of sources. The third period, (≈ 20% of total cases) distinguished by noteworthy excess of O3 and PM10, was attributed to a marked effect of succeeding Cut-off lows and Omega blocking patterns. Under these conditions, the scatter plots of NO, NO2, and O3 versus NOx for daytime and nighttime showed good polynomial fit curves with high R2 values for NO and NO2 only. Under both moderately unstable and stable conditions, significant negative correlation coefficients between O3 and NOx were found to be 0.53 (p < 0.05) and 0.50 (p < 0.05) for daytime and nighttime, respectively. Under Cut-off lows and Omega, an absence of any significant correlations between O3 and NOx was noticed. Under Cut-off lows, the good linear regression fits between OX (=O3 + NO2) and NOx illustrated high NOx independent contributions, possibly attributed to the biogenic VOCs effect. The investigation of the air quality index (AQI) for NO2, PM10 and O3 (running 8-h mean) highlighted the significant effect of both O3 and PM10 only under Cut-off lows and Omega which were retraced by poor to very poor air quality categories.

Estimation of in vivo body composition of Iberian pigs using bioelectric impedance and ultrasonography techniques

Iberian pigs are renowned for their high-quality products and distinctive characteristics, including high fat accumulation, low protein deposition rate, and a long productive cycle. The study aimed to assess in vivo body composition of purebred Iberian pigs using bioimpedance analysis (BIA) and ultrasonography. Accurate estimation of body composition in live animals is crucial for adopting decisions at the farm level. The experiment involved three groups of pure male Iberian pigs differing in body weight (BW; 60, 80 and 100 kg) with the same nutritional management. Body measurements, BIA and back fat and loin thickness (measured by ultrasonography) were obtained before slaughter. After slaughter pig carcasses were chemically analysed. A strong correlation between BIA measurements, specifically resistance (Rs) values, and body chemical parameters (total protein, lipids, ash, and water contents; p < 0.001 for all) was found. Reactance values (Xc), however, did not exhibit significant correlations. Regression analyses were conducted to predict carcass composition based on BIA measurements, BW, ultrasonography and linear corporal measurements. The prediction models achieved high R2 values for lipids, protein, total ash, water, and lean tissue (0.957, 0.968, 0.936, 0.961 and 0.976, respectively, p < 0.001 for all), indicating strong predictive power. These findings demonstrate the potential of non-invasive techniques such as BIA for estimating body chemical composition and quality of pig carcasses. However, it is important to acknowledge that the prediction models developed may not be applicable to other pig populations, as they were based on a specific sample of pigs.

Fabrication of a Fully Printed Ammonia Gas Sensor Based on ZnO/rGO Using Ultraviolet-Ozone Treatment.

In this study, a room-temperature ammonia gas sensor using a ZnO and reduced graphene oxide (rGO) composite is developed. The sensor fabrication involved the innovative application of reverse offset and electrostatic spray deposition (ESD) techniques to create a ZnO/rGO sensing platform. The structural and chemical characteristics of the resulting material were comprehensively analyzed using XRD, FT-IR, FESEM, EDS, and XPS, and rGO reduction was achieved via UV-ozone treatment. Electrical properties were assessed through I-V curves, demonstrating enhanced conductivity due to UV-ozone treatment and improved charge mobility from the formation of a ZnO-rGO heterojunction. Exposure to ammonia gas resulted in increased sensor responsiveness, with longer UV-ozone treatment durations yielding superior sensitivity. Furthermore, response and recovery times were measured, with the 10 min UV-ozone-treated sensor displaying optimal responsiveness. Performance evaluation revealed linear responsiveness to ammonia concentration with a high R2 value. The sensor also exhibited exceptional selectivity for ammonia compared to acetone and CO gases, making it a promising candidate for ammonia gas detection. This study shows the outstanding performance and potential applications of the ZnO/rGO-based ammonia gas sensor, promising significant contributions to the field of gas detection.

Nonlinear finite element and machine learning modeling of tubed reinforced concrete columns under eccentric axial compression loading

There is still insufficient data on the behavior of tubed-reinforced concrete columns (TRCCs) under the eccentric compression. Thus, this research work comprehensively examines the eccentric compression behavior of TRCCs using nonlinear finite element modeling and machine learning (ML). To do this, numerical simulation and parametric analysis based on existing investigations were conducted. In addition to the existing 22 specimens with limited test variables, additional 188 specimens were developed to cover a wide range of parameters, including the load eccentricity, transverse reinforcement spacing, columns’ slenderness ratio, yield strength of steel, and outer steel tube diameter. Additionally, six ML models were created to estimate the ultimate load results. The results indicated that increasing the outer steel tube yield strength and diameter, and reducing the load eccentricity, slenderness ratio, and spacing of the transverse reinforcement enhanced the load-carrying capacity of the columns. The Gaussian process regression model demonstrated superior performance metrics in comparison to other ML models, with the highest R2 values (0.998613 in training and 0.99823 in testing stages) and lowest root mean square error values (0.007213 in training and 0.008471 in testing stages). To save money, time, and resources compared to laboratory testing, an online-based prediction program is finally presented to predict the columns’ ultimate load.

Supervised intelligent prediction of shear strength of rockfill materials based on data driven and a case study

The rockfill materials (RFM) are emerging and regarded waste reuse product in construction and mining engineering. In this paper, six distinctive supervised machine learning (SML) models, namely artificial neural network (ANN), extreme learning machine (ELM), random forest (RF), relevance vector regression (RVR), support vector regression (SVR), and extreme gradient boosting (XGBoost), are adopted to predict the RFM shear strength using 165 data cases with 13 features. To improve model performance, an improved physics meta-heuristic algorithm, named snow ablation optimizer with Logistic mapping (LogSAO), is utilized to select the optimal hyperparameter combination of the proposed models. Four popular statistical indices, including coefficient of determination (R2), root mean squared error (RMSE), Willmott’s index (WI), and Scatter index (SI) are employed to quantify the model performance. The model evaluation results show that the LogSAO-SVR model is the best prediction model with the highest values of R2 and WI while the lowest values of RMSE and SI in both training and testing phases (0.9985, 0.0257, 0.9996, and 0.0380; 0.9802, 0.0812, 0.9950, and 0.1335). Normal stress (Ns) is the most important feature with the highest importance score of 0.925 in predicting the RFM shear strength. Interestingly, the negative contribution of Ns to the RFM shear strength prediction will be transformed into the positive contribution when coefficients of curvature (Cu) values are small. The validation results demonstrate that the predicted values of the proposed LogSAO-SVR model are highly consistent with the measured values from the direct shear tests based on a case study.

A biobased mixed metal oxide catalyst for biodiesel production from waste cooking oil: reaction conditions modeling, optimization and sensitivity analysis study

This study assessed the utilization of a heterogeneous catalyst derived from waste marble tiles and cow horn for waste cooking oil-derived biodiesel synthesis. The synthesized composite catalyst was prepared from waste marble tiles and cow horn through sequential calcination and wet impregnation. The catalyst and its precursors were characterized to analyze their composition, structural, and surface properties. The biodiesel production process was modeled and optimized via the response surface methodology (RSM) and artificial neural networks (ANN). Characterization of waste cooking oil revealed its suitability in biodiesel production. The catalyst had a surface area of 301.510 m2/g, while the pore volume and pore diameter were 0.165 cm3/g and 2.110 nm respectively, which contributed to the optimum biodiesel yield of 98% at a reaction time of 119.92 min, a catalyst concentration of 5.12 wt%, a reaction temperature of 74.86 °C, and methanol-to-oil ratio of 11.43:1. The catalyst was reusable for up to seven cycles while retaining significant activity. The optimized biodiesel sample had properties that met standards. RSM and ANN demonstrated adequate representation of the biodiesel yield as shown by their high R2 values although ANN was superior since it had higher R2 (0.9993) and adjusted R2 (0.9977) values. Global sensitivity analysis results showed reaction temperature as the most important input variable. This work has provided insights into the valorization of waste cooking oil for sustainable biodiesel production through heterogeneous catalysis.

Optimization of recycled rubber self-compacting concrete: Experimental findings and machine learning-based evaluation

This research aims to assess the rheological and mechanical characteristics of Self-compacting concrete (SCC) incorporating waste tire rubber aggregates (WRTA) as an interim substitute for coarse aggregates. However, the standard experimental modeling approach has significant obstacles when it comes to overcoming the nonlinearity and environmental susceptibility of concrete parts. Therefore, linear regression (LR) and extreme gradient boosting (XGBoost) were used as two standard single machine learning (ML) models to predict the aforementioned rubberized SCC features. In this study, conventional coarse aggregates were supplanted with WRTA at 0%, 5%, 10%, and 20% to uncover the optimal proportion of coarse aggregates substituting rubber. To find the optimum amount of WRTA to use as a substitute, the study follows the impacts of rubber on the self-compacting rubberized concrete's (SCRC) rheological and mechanical characteristics. The consequences on fresh properties were investigated by the slump flow, J-ring, and V-funnel tests, while compressive and splitting tensile strengths tests were conducted to assess mechanical properties. Increasing WRTA test outputs indicated a deterioration in workability and hardened qualities. While a 10% swapping ratio is deemed feasible for producing SCRC, optimal results were achieved by reducing environmental impacts and efficiently managing a significant volume of rubber tire waste with a 5% substitution of rubber within the coarse aggregates. The research findings indicated a noticeable decrease in fresh properties as the WRTA content increased. Notably, after 28 days, a 10% WRTA substitution led to a 34% reduction in compressive strength and a 28% decrease in splitting tensile strength, satisfying ACI standards. Furthermore, XGBoost demonstrated superior predictive performance with the highest R2 values, outperforming the LR model and affirming its efficacy in delivering more accurate predictions.

Investigating the performance of SDGSAT-1/GIU and NPP/VIIRS nighttime light data in representing nighttime vitality and its relationship with the built environment: A comparative study in Shanghai, China

In recent years, a substantial body of research has demonstrated the close relationship between the built environment and urban nighttime vitality. While scholars have established slightly varied evaluation systems for the built environment, most have utilized the open-source nighttime light (NTL) data (2012 ∼ ) from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite to characterize nighttime vitality. However, with the release of the high spatial resolution NTL data from Glimmer Imager for Urbanization (GIU) sensor on the Sustainable Development Goals Satellite 1 (SDGSAT-1) by the Chinese Academy of Sciences (CAS), more options are now available for indicating nighttime vitality. Yet, no studies have so far measured the representational capability of this new NTL data. In this study, we selected Shanghai, one of the most urbanized cities in Asia, as our research area. We utilized both GIU NTL data and VIIRS NTL data to represent nighttime vitality and collected multidimensional geospatial big data as indicators of the built environment. Various regression models were employed to explore the differences in explanatory power when using different NTL data. To obtain more reliable conclusions, we have also divided the study area according to urban functional zone categories and conducted experiments on both the overall scale (entire study area) and local scale (different urban functional zones). The results showed that regression models using GIU NTL data had higher R2 values both at overall scale(0.529 > 0.268) and local scale (0.704 > 0.641, 0.701 > 0.408, 0.537 > 0.512, 0.674 > 0.589), indicating stronger explanatory power of GIU NTL data. Furthermore, we found that the use of GIU NTL data revealed an amplification effect, where the impact of certain representative built environment factors (such as road density, building height, and vegetation coverage) on nighttime vitality was magnified, demonstrating more pronounced spatial heterogeneity. Our study investigated the performance of the new open-source SDGSAT1/GIU NTL data in representing nighttime vitality and shows its potential application in research on the relationship between the built environment and nighttime vitality. Additionally, our study also reveals the differences in the impact of the built environment on nighttime vitality across different urban functional zones. All these results will provide more valuable references for city managers aspiring to enhance the nighttime vitality and help them to make more rational planning plans.

UAV Imagery for Measuring Large Wood Volume and Distribution: Ground Truthing Results and Sources of Error

abstract: UAV imagery offers great potential in determining precise environmental measurements. This study identifies and quantifies errors and their causes/sources between UAV imagery- and field-derived measurements of large wood (LW) and cross-sectional valley topography in several forested headwater stream valleys in the Ozark Highlands, Missouri. UAV imagery was collected at six sites following best practices for producing high resolution orthophotos and elevation models through Structure-from-Motion photogrammetry. Field-based measurements of LW were completed along 5 m wide valley cross-sections in which all LW &gt; 1 m in length and 0.1 m in diameter were recorded. Topographic surveys were performed along the centerlines of the LW transects. The same assessments were performed using UAV imagery and ArcGIS. Our sites showed high (&gt;= 0.85) R2 values between field and UAV-GIS topographic surveys. Differences between measurements of LW dimensions tended to be larger with R2 ranging from 0.42 to 0.88. Overall 78 percent of standing trees and 60 percent of LW pieces were identified in the UAV imagery. The main source of detection and measurement error was due to vegetation/canopy cover. This study furthers our understanding of the applications and limitations of UAV derived data for environmental assessments.

Harnessing AI to unmask Copenhagen's invisible air pollutants: A study on three ultrafine particle metrics

Ultrafine particles (UFPs) are airborne particles with a diameter of less than 100 nm. They are emitted from various sources, such as traffic, combustion, and industrial processes, and can have adverse effects on human health. Long-term mean ambient average particle size (APS) in the UFP range varies over space within cities, with locations near UFP sources having typically smaller APS. Spatial models for lung deposited surface area (LDSA) within urban areas are limited and currently there is no model for APS in any European city. We collected particle number concentration (PNC), LDSA, and APS data over one-year monitoring campaign from May 2021 to May 2022 across 27 locations and estimated annual mean in Copenhagen, Denmark, and obtained additionally annual mean PNC data from 6 state-owned continuous monitors. We developed 94 predictor variables, and machine learning models (random forest and bagged tree) were developed for PNC, LDSA, and APS. The annual mean PNC, LDSA, and APS were, respectively, 5523 pt/cm3, 12.0 μm2/cm3, and 46.1 nm. The final R2 values by random forest (RF) model were 0.93 for PNC, 0.88 for LDSA, and 0.85 for APS. The 10-fold, repeated 10-times cross-validation R2 values were 0.65, 0.67, and 0.60 for PNC, LDSA, and APS, respectively. The root mean square error for final RF models were 296 pt/cm3, 0.48 μm2/cm3, and 1.60 nm for PNC, LDSA, and APS, respectively. Traffic-related variables, such as length of major roads within buffers 100–150 m and distance to streets with various speed limits were amongst the highly-ranked predictors for our models. Overall, our ML models achieved high R2 values and low errors, providing insights into UFP exposure in a European city where average PNC is quite low. These hyperlocal predictions can be used to study health effects of UFPs in the Danish Capital.