R2 Values Research Articles

High-Quality Multi-Emitter LED-Based Retrofits for Incandescent Photometric A Illuminant Reliability of R2 Evaluation

This research deals with the design problems of LED-based spectrally tunable light sources (LSTLSs). The study aims to assess the reliability of popular models for the spectral modeling of LED radiation and a typically used curve-fitting criterion (R2) in the development of high-quality multi-emitter LED retrofits for incandescent photometric illuminant. The research methodology involves modeling each LED channel using Lorentz and Gaussian functions and combining multiple channels to approximate the desired spectral power distribution (SPD). After the optimization, 20 various LED sets were designed, which allowed us to replicate the SPD of CIE illuminant A with a very high R2 value. Two sets were constructed and measured to recognize the reliability of the simulation approach. The results suggest that for planning the LSTLS for photometric applications, these models are unreliable as they do not reflect the real effect of changes in the characteristics of the components nor reveal the share of various spectral ranges. Therefore, the decisions made on these criteria may not be the best solutions in the context of specific applications.

Research on Outgoing Moisture Content Prediction Models of Corn Drying Process Based on Sensitive Variables

Accurate prediction of outgoing moisture content is the key to achieving energy-saving and efficient technological transformation of drying. This study relies on a grain drying simulation experiment system which combined counter and current drying sections to design corn kernel drying experiments. This study obtains 18 kinds of temperature and humidity variables during the drying process and uses Uninformative Variable Elimination (UVE) method to screen sensitive variables affecting the outgoing moisture content. Subsequently, six prediction models for the outgoing corn moisture content were developed, innovatively incorporating Multiple Linear Regression (MLR), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM). The results show that eight sensitive variables have been screened to predict the moisture content of outgoing corn. The sensitive variables effectively reduced the redundancy and multicollinearity of data in the MLR model and improved the coefficient of determination (R2) of ELM and LSTM models by 0.02 and 0.05. The MLR prediction model established based on the full set of temperature and humidity data has an R2 of 0.910 and a root-mean-square error (RMSE) of 0.881%, while the UVE-ELM and UVE-LSTM prediction models achieve a better fitting effect and prediction accuracy. The UVE-LSTM model is set with a batch size of 30, a learning rate of 0.01, and 100 iterations. For the training set of UVE-LSTM, the R2 value is 0.931 and the RMSE value is 0.711%. The UVE-ELM model, with sigmoid as the activation function and 14 neurons configured, runs fast and has the best prediction accuracy. The R2 values of UVE-ELM training set and validation set are 0.943 and 0.946, respectively, and the RMSEs are 0.544% and 0.581%. The models proposed in this study provide data reference and technical support for process optimization and automation control of the corn drying process.

Analysis of Factors Influencing the Transformation of the Workforce from the Agricultural Sector to the Non-Agricultural Sector in Bagik Polak Village, West Lombok Regency

This study aims to analyze the influence of income, education, age and gender on the transformation of the workforce from the agricultural sector to the non-agricultural sector in Bagik Polak Village. The type of research used in this study is the associative quantitative method. The test was carried out on 94 respondents spread across 7 hamlets, namely Karang buncu lauq, Karang buncu Barat, Karang buncu daye, Rerot, Karang kebon timur, Karang kebon Barat and Enjak. Data was obtained by distributing questionnaires and sampling research using the purposive sampling method. The analysis tool used is logistic regression (logit) which has three tests including Assessing the Overall Model Fit, Testing the Feasibility of the Regression Model (Goodness of Fit Test) and Determination Coefficient (Nagelkerke's R Square). As well as statistical tests, namely the Likelihood Ratio Test (Simultaneous Test F) and the Wald Test (Partial Test t). The results of the analysis show that purely of the four variables used in this study, there are two variables that have a positive and significant effect, namely income with a significant value of 0.045< 0.05; and education with a significant value of 0.019 < 0.05. Meanwhile, the age variables with a significant value of 0.796 > 0.05 and gender with a significant value of 0.851 > 0.05 did not have a significant effect on the transformation of the workforce from the agricultural sector to the non-agricultural sector in Bagik Polak Village. Meanwhile, simultaneously that together, the independent variable (X) has an effect on the transformation of the workforce from the agricultural sector to the non-agricultural sector with a significant chi-square value of 0.004 < 0.05. Meanwhile, Nagelkerke's R2 value shows that all independent variables are able to explain the dependent variable by 42.7% and the remaining 57.3% are explained by other variables that are not included in the model.

Studies on adsorptive dehydration of pasteurised milk by filled polymeric IPN hydrogel

ABSTRACT Adsorptive dehydration utilising polymeric hydrogels presents an effective technique for removing excess water from pasteurised milk. This study investigated three Interpenetrating Polymeric Network (IPN) hydrogels comprising Polyvinyl Alcohol (PVOH) and Hydroxyethyl Cellulose (HEC) at mass ratios of 90:10, 70:30 and 50:50, respectively, with Maleic Anhydride (MAn) as a cross-linker at concentrations of 2% 4%, and 8% based on total polymer weight. It was observed that the rate of dehydration increased with higher HEC content in the hydrogels. However, the stability decreased due to changes in the structural alignment of polymer chains and weak cross-linking. Among the tested hydrogels, the one with a composition of 70:30 for PVOH and HEC, respectively, cross-linked with 8% MAn (0.096gm), exhibited the best adsorption (569% Swelling) with minimal changes in pH (<10%) and conductivity (<10%) and was, therefore, selected for further study. This selected hydrogel composition was further enhanced by incorporating a hydrophilic nanofiller (Bentonite) at proportions of 2%, 4% and 6% based on total polymer weight during hydrogel synthesis. The hydrogel with 4% nanofiller (0.081 gm) showed the best results in terms of swelling and stability although the 6% filled hydrogel exhibited superior swelling but lower stability. Finally, the dehydration process was optimised using Type 1 fuzzy logic (T1FL), achieving an R2 value of 95.13%.

Rheological behavior predictions of non-Newtonian nanofluids via correlations and artificial neural network for thermal applications

Nanofluids possess enhanced viscous and thermal features that can be utilized to improve the heat transfer performance of several applications involving sustainable manufacturing and industrial ecology, such as in heating/cooling systems, electronics, transportation etc. Therefore, it is important to understand and optimize the flow pattern of these fluids. This research emphasizes the predictions of viscosity of water/ethylene-glycol (EG) based non-Newtonian nanofluids. Four experiment-based data sets are used to predict and validate the effective viscosity, i.e., Fe3O4-Ag/EG, MWCNT-alumina/water-EG, Fe3O4-Ag/water-EG, and MWCNT-SiO2/EG-water, via existing correlations and artificial neural network (ANN). The modeling is based on three input parameters, i.e., particle concentrations, temperatures, and shear rates, and one output parameter, i.e., viscosity. The predicted outcomes are compared to the three existing correlation structures. The error matrix consists of the coefficient of determination (R2), average absolute deviation (AAD %), the sum of squared error (SSE), that are employed to evaluate the performance of the model. Results from ANN are found to be more precise, with R2 values greater than 0.99 for all datasets, compared to data fitting into existing correlations, in which Fe3O4-Ag/water-EG resulted in an R2 value as low as 0.72, to determine the nanofluids’ effective viscosity.

Identification of Novel Thiazolidinedione Based Pancreatic Lipase Inhibitors for the Management of Obesity Using In Silico Approach

AbstractAccumulation of excessive fat in the body is an indication of obesity, which is becoming an alarming health concern around the world. Pancreatic lipase has been found to have a promising role in the digestion of dietary fats thereby inhibition of pancreatic lipase can be a therapeutic strategy for the management of obesity. Literature showed the anti‐pancreatic lipase potential of thiazolidinediones thereby the current study is designed to identify substituted thiazolidinedione‐based molecules as pancreatic lipase inhibitors. A 3D QSAR model with R2 value of 0.7283 and Stability of 0.843 was generated to determine the nature of substitution necessary for the inhibitory activity. Based on the generated 3D QSAR, the activity of 352 novel‐designed molecules was predicted and further screened through pharmacokinetic parameters. These compounds were docked into the binding site of the pancreatic lipase which led to the identification of 14 hit molecules with dock score between −9.523 to −7.604 kcal/mol. The molecular simulation studies and free energy calculation of the hit compounds i. e. 87 and 80 revealed good stability and binding in the active site of pancreatic lipase. The current study provides 14 hit molecules, which can be synthesized and further evaluated to explore their anti‐pancreatic lipase inhibitory activity.

Downscaling the MODIS land surface temperature using a trapezial concept applied to the MODIS and sentinel 2 images.

Downscaling methods are crucial for accessing high-resolution thermal data simultaneously. The DisTRAD model is commonly used for downscaling thermal images, but changes in soil moisture, such as those caused by irrigation operations, can lead to errors in the process. This study investigated the potential use of TOTRAM and OPTRAM models to reduce errors in LST downscaling in irrigated fields. Sentinel satellite imagery was utilised to enhance the resolution of MODIS Land Surface Temperature (LST) from 1000 to 20 m in the fields of Megsal and Hezarjolfa agro-industrial company in Qazvin province. Soil moisture was estimated using the OPTRAM model, and the results were compared with observational data. The findings indicated that on days with NDVI greater than 0.6, the R2 value exceeded 0.88 and the RMSE value was less than 0.06 cm3/cm3. Then, MODIS LST images were downscaled to 20 m using codes in Google Earth Engine (GEE). Evaluation was conducted using observational data from collected land surface temperature data for 36 points. Comparison of the downscaled LST data with observational data on days with irrigation revealed a decrease in MAE and RMSE error indices by approximately 0.4 and 1.2 degrees Celsius, respectively, in the OPTRAM-TPTRAM model compared to the DisTRAD model. Consequently, the OPTRAM-TOTRAM model generally outperforms the DisTRAD model in LST downscaling. Lastly, it is recommended to assess the TOTARM and OPTRAM models for downscaling MODIS sensor LST in other irrigated fields.

Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

IntroductionRescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models.MethodsIn this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2) as performance metrics.ResultsIn this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA’s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions.DiscussionWhile the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Water quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality is crucial for ensuring sustainable water resource management, which supports agricultural, industrial, and domestic needs while safeguarding ecosystems. The models were assessed using important statistical variables, a dataset comprising six relevant parameters, and water use records. The database contained electrical conductivity, pH, dissolved oxygen, nitrate, phosphates, suspended solids, water temperature, water consumption records, and an appropriate date. Furthermore, Random Forest, K-nearest Neighbor, and Support Vector Machine are the three machine learning methodologies employed for water quality categorization forecasting. Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index. The water quality classification produced by the Random Forest forecast had the highest accuracy of 99.89%. The GRU model fared better than the LSTM and BiLSTM models with values of R2 and NSE, which are 0.81 and 0.720 for water consumption and 0.78 and 0.759 for water quality index, in the prediction results. The outcomes showed how reliable Random Forest was in classifying water quality forecasts and how reliable gated recurrent units were in predicting water quality indices and water demand. It is worth noting that accurate predictions of water quantity and quality are essential for sustainable resource management, public health protection, and ecological preservation. Such promising research could significantly enhance urban water demand planning and water resource management.

Simultaneous Quantification of Dutasteride and Silodosin Using a Stability-Indicating RP-HPLC Approach

An alternative stability-indicating reverse-phase high-performance liquid chromatography (RP-HPLC) approach remained established to quantify silodosin (SLD) and dutasteride (DTS) in the mixture, validated per International Council of Harmonization (ICH) recommendations. A Shimadzu Model LC-2030 PLUS (IND) HPLC system, 231 nm detection wavelength, and 10 minutes run duration were used to create the method. A 75:25 mobile phase of methanol and water with 0.05% OPA was used at 1-mL/min. The concentrations of SLD (8–40 μg/mL) and DTS (0.5–2.5 μg/mL) were shown to be linearly related, with R2 values of 0.9998 for SLD and 0.9993 for DTS. Both of the drugs had accuracy and precision within 2% RSD. SLD and DTS mean recoveries were 97.5 to 102%. According to ICH criteria, degradation trials with acid, alkali, oxidizing agent, and heat assessed the approach’s stability. The established approach is fast, accurate, sensitive, and precise for routine SLD and DTS analysis from bulk as well as pharmaceutical dosage forms.

Development and Validation of New LC-MS/MS Bioanalytical Method of Tremelimumab in Rat Plasma by Using Nivolumab as Internal Standard and Its Application with Pharmacokinetic Studies

Nivolumab was decided to be used as the IS. To separate the drugs, an isocratic mobile phase of acetonitrile (ACN): Ammonium formate containing formic acid (70:30) was adopted and delivered at 1-mL/min, along with a 150 x 4.6 mm x 3.5 μm Alliance, e2695 Luna Phenyl Hexyl column. Drug and IS, both of which display proton adducts approximately m/z 146.3685 to 120.0638 and m/z 143.7695 to 76.7964, might be detected simultaneously using MRM-positive modalities. Over a linearity level of 2.00 to 40.00 ng/mL, the method had a correlation value (r2) of 0.99977. The accuracy and precision of this method during the day ranged from 89.85 to 102.89% and 0.19 to 2.81%, respectively. Tremelimumab was reported to remain stable during three freeze-thaw cycles, benchtop tests, and postoperative stability studies. Cmax and Tmax values were acquired directly from experimental data. Cmax and Tmax averaged 18.024 ng/mL and 6 hours, respectively. At t1/2 of 18 hours, plasma levels began to fall. The obtained AUC024 and AUC0 values were 257 and 257 ng h/mL, respectively.

Analysis of physical activity in psoriatic arthritis: relationship with clinical and analytical parameters and comorbidity-description of the sedentary patient.

This study aimed to relate physical activity and a sedentary lifestyle to clinical, biological, functional, and comorbid parameters in a cohort of patients with psoriatic arthritis (PsA). A cross-sectional study was conducted with 232 PsA patients. Physical activity and sedentary lifestyle were obtained using the International Physical Activity Questionnaire (IPAQ) questionnaire. The demographic, clinical, and biological variables measured were age, time since PsA diagnosis, smoking, type of treatment used, clinical form, presence of enthesitis, dactylitis (present or past), fatigue, tumor necrosis factor (TNF)-alpha, and interleukin 6 (IL-6). Activity and functionality were measured using the Disease Activity Index for Psoriatic Arthritis (DAPSA) and Health Assessment Questionnaire (HAQ) in peripheral forms, while the Ankylosing Spondylitis Disease Activity Score (ASDAS-PCR) and Bath Ankylosing Spondylitis Functional Index (BASFI) were measured in axial forms. Disease impact was assessed using the Psoriatic Arthritis Impact of Disease (PsAID) questionnaire. Alongside comorbidities, obesity, anxiety, depression [Hospital Anxiety and Depression Scale (HADS)], and sleep quality [Insomnia Severity Index (ISI)] were assessed. The mean age was 54.6 (SD: 11.4) years, with 54.3% being male. A total of 25.6% of patients were sedentary. Physical activity and sedentary lifestyle were inversely correlated with fatigue, activity, functionality, and disease impact. Within comorbidities, they correlated with anxiety, depression, and insomnia. In addition, physical activity was inversely correlated with obesity. In linear regression analysis, physical activity was found to be related to body mass index (BMI) with a ß coefficient of -0.1 (p < 0.04; 95%CI: -194.1--4.5), and an R2 value of 0.11. In logistic regression analysis, a sedentary lifestyle was found to be related to pain, with an odds ratio (OR) of 1.5 (p < 0.001; 95%CI:1.1-1.8) and an R2 Nagelkerke value of 0.36. A quarter of the patients were sedentary. Lack of physical activity correlated with worse parameters of clinical activity, functionality, disease impact, and the presence of comorbidities.

Futures markets and the baltic dry index: A prediction study based on deep learning

The Baltic Dry Index (BDI), representing the shipping sector, displays a notable sensitivity to Chinese commodity futures markets. Stakeholders must grasp the relationship between BDI and China’s commodity futures markets. However, there is currently a lack of comprehensive evaluation of Chinese futures’ forecasting performance. Therefore, we collected data on 17 major Chinese commodity futures from April 16, 2015, to December 27, 2022, and used CNN, BiLSTM, and AM to assess China’s futures market’s BDI prediction power. The CNN-BiLSTM-AM ensemble model emerged as the most accurate, R² value of 95.3 %. This study highlights the Chinese futures market’s ability to predict the global shipping index BDI and broadens our understanding of the financial market-maritime industry interplay. By monitoring fluctuations in China’s futures market, shipping companies and regulators can make precise BDI predictions, offering a scientific foundation for policy adjustments and decision-making amidst future BDI shifts.

Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer

In this study, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, and mass of product) and dependent variables (energy consumption and size reduction) were established. For energy consumption, XGBoost demonstrates superior performance with an R2 of 0.9957 during training and 0.9971 during testing, alongside minimal MSE of 0.0034 during training and 0.0008 during the testing phase indicating high predictive accuracy and low error rates. Conversely, LGBM shows lower R2 values (0.9061 training, 0.8809 testing) and higher MSE of 0.0747 during training and 0.0337 during testing, reflecting poorer performance. Similarly, for shrinkage prediction, XGBoost outperforms LGBM, evidenced by higher R2 (0.9887 training, 0.9975 testing) and lower MSE (0.2527 training, 0.4878 testing). The comparative statistics showed that XGBoost regularly outperformed LightGBM. The game theory-based Shapley functions revealed that temperature and product types were the most influential features of the energy consumption model. These findings illustrate the practical applicability of the XGBoost and LightGBM models in food drying operations towards optimizing drying conditions, improving product quality, and reducing energy consumption.

Real-time pavement temperature prediction through ensemble machine learning

Pavement temperature is crucial for assessing asphalt pavement's performance and structural integrity due to its viscoelastic behavior. Currently, asphalt temperature prediction includes analytical, numerical, and statistical methods and relies on field investigation with intensive labor and time to hinder routine practice. Machine learning (ML) offers a promising solution for reliable and accurate real-time predictions by considering various dynamic environmental factors. This research employs four ensemble ML algorithms: eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Adaptive Boosting (AdaBoost) to predict pavement temperature at different depths. XGB consistently outperformed other ensemble models, boasting the highest average R2 value (97.92%) and lower MAE and RMSE than LGBM, RF, and AdaBoost. XGB also significantly outperformed other commonly used models (linear regression, SVR, ANN, LSTM, and GRU) with an average reduction in errors of 35.29%, 63.75%, 42.75%, and an increase in R2 by 4.39% for MAE, MSE, RMSE, and R2, respectively. The Shapley additive explanation (SHAP) was applied to interpret the underlying influencing factors and their interactions, and revealed atmospheric temperature as the most impactful feature, contributing over 40.58% among the significant features.Furthermore, the predictive performance of XGB was enhanced by incorporating Principal Component Analysis to reduce computational consumption with average reductions of 8.98%, 16.59%, 9.01%, and 7.89% in MAE, MSE, MAPE, and RMSE. XGB with PCA emerges as the most effective and efficient model for predicting pavement temperatures at various depths to facilitate timely decision-making for preventive maintenance and rehabilitation and prevent future catastrophic failures.

Assessment of spatial air quality on the East Coast of Peninsular Malaysia utilizing environmetric techniques.

The presence of harmful substances in the atmosphere poses significant risks to the environment and public health. These pollutants can come from natural sources like dust and wildfires, or from human activities such as industrial, transportation, and agricultural practices. The objective of this study was to assess air quality on the East Coast of Peninsular Malaysia by analyzing historical data from the Department of Environment, Malaysia. Daily measurements of PM10, O3, SO2, NO2, and CO were collected from eight monitoring stations over 11years (2011-2021) and analyzed using environmetric techniques. Hierarchical agglomerative cluster analysis (HACA) classified two stations as belonging to the high pollution cluster (HPC), three stations as part of the moderate pollution cluster (MPC), and three stations as the low pollution cluster (LPC). Discriminant analysis revealed a correct assignment rate of 90.50%, indicating that all five parameters were able to differentiate pollution levels with high significance (p < 0.0001). Principal component analysis (PCA) was conducted to validate the pattern of air quality variables in relation to the identified clusters (HPC, MPC, and LPC). The results showed that two verifactors (VFs) were extracted in HPC and LPC, while three VFs were identified in MPC. The cumulative variance explained by the PCA for HPC, MPC, and LPC was 69.43%, 82.32%, and 62.16%, respectively. Finally, an artificial neural network (ANN) was used to forecast the air pollutant index (API) levels, using the R2 and RMSE performance metrics. The PCA-MLP Model A yielded an R2 value of 0.8470 and an RMSE of 6.6470, while PCA-MLP Model B achieved an R2 value of 0.8591 and an RMSE of 6.3000, both indicating a significant and strong correlation.

Functionalized graphitic carbon nitride as adsorbent for the removal of arsenic and lead from groundwater

Water pollution caused by highly toxic arsenic (As) and lead (Pb) poses a serious threat to water quality. Hence, the development of materials for their effective removal from water continues to attract research attention. The present study reports functionalized graphitic carbon nitride nanosheets (GCN) as a green and low-cost adsorbent for the removal of As and Pb from polluted water. The adsorbent was prepared through the protonation and hydroxyl and cyano functionalized graphitic carbon nitride to form H/GCN and OH/CN-GCN respectively. Characterization techniques including Fourier transform infrared (FTIR), X-ray diffraction, and scanning electron microscopy were respectively used to study functional groups, structure, and morphology of the adsorbents. The adsorption study showed that modification of GCN with −OH and CN− ions in OH/CN-GCN, increased the density of negative charges on the functionalized surface, which also enhances the attraction of the positively charged ions. This may be responsible for the improved removal of As and Pb from wastewater compared to H/GCN. Isotherm studies on the adsorption behavior of OH/CN-GCN suggest that Langmuir isotherm model corroborates with the As adsorption. Therefore, indicating that the removal of As via its adsorption onto OH/CN-GCN is a surface phenomenon. However, the adsorption of Pb could be described as mainly a multilayer adsorption process, based on its R2 value. It is proposed that the −OH and CN− groups on the tri-s-triazine units of GCN nanosheets may be responsible for the adsorption process. The prepared materials are promising adsorbents that nay find useful applications in wastewater treatment plants involving advanced oxidation processes.

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian cancer. Materials and Methods This retrospective study included contrast-enhanced and noncontrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age, 60 years ± 11 [SD]; 143 female), was tested on two internal (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the Dice coefficient, standard deviations, and 95% confidence intervals, focusing on ascites volume in the peritoneal cavity. Results On NIH-LC (25 patients; mean age, 59 years ± 14; 14 male) and NIH-OV (166 patients; mean age, 65 years ± 9; all female), the model achieved Dice scores of 85.5% ± 6.1% (CI: 83.1%-87.8%) and 82.6% ± 15.3% (CI: 76.4%-88.7%), with median volume estimation errors of 19.6% (IQR: 13.2%-29.0%) and 5.3% (IQR: 2.4%- 9.7%), respectively. On UofW-LC (124 patients; mean age, 46 years ± 12; 73 female), the model had a Dice score of 83.0% ± 10.7% (CI: 79.8%-86.3%) and median volume estimation error of 9.7% (IQR: 4.5%-15.1%). The model showed strong agreement with expert assessments, with r2 values of 0.79, 0.98, and 0.97 across the test sets. Conclusion The proposed deep learning method performed well in segmenting and quantifying the volume of ascites in concordance with expert radiologist assessments. ©RSNA, 2024.

Spatiotemporal Epidemiological Trends of Mpox in Mainland China: Spatiotemporal Ecological Comparison Study.

The World Health Organization declared mpox an international public health emergency. Since January 1, 2022, China has been ranked among the top 10 countries most affected by the mpox outbreak globally. However, there is a lack of spatial epidemiological studies on mpox, which are crucial for accurately mapping the spatial distribution and clustering of the disease. This study aims to provide geographically accurate visual evidence to determine priority areas for mpox prevention and control. Locally confirmed mpox cases were collected between June and November 2023 from 31 provinces of mainland China excluding Taiwan, Macao, and Hong Kong. Spatiotemporal epidemiological analyses, including spatial autocorrelation and regression analyses, were conducted to identify the spatiotemporal characteristics and clustering patterns of mpox attack rate and its spatial relationship with sociodemographic and socioeconomic factors. From June to November 2023, a total of 1610 locally confirmed mpox cases were reported in 30 provinces in mainland China, resulting in an attack rate of 11.40 per 10 million people. Global spatial autocorrelation analysis showed that in July (Moran I=0.0938; P=.08), August (Moran I=0.1276; P=.08), and September (Moran I=0.0934; P=.07), the attack rates of mpox exhibited a clustered pattern and positive spatial autocorrelation. The Getis-Ord Gi* statistics identified hot spots of mpox attack rates in Beijing, Tianjin, Shanghai, Jiangsu, and Hainan. Beijing and Tianjin were consistent hot spots from June to October. No cold spots with low mpox attack rates were detected by the Getis-Ord Gi* statistics. Local Moran I statistics identified a high-high (HH) clustering of mpox attack rates in Guangdong, Beijing, and Tianjin. Guangdong province consistently exhibited HH clustering from June to November, while Beijing and Tianjin were identified as HH clusters from July to September. Low-low clusters were mainly located in Inner Mongolia, Xinjiang, Xizang, Qinghai, and Gansu. Ordinary least squares regression models showed that the cumulative mpox attack rates were significantly and positively associated with the proportion of the urban population (t0.05/2,1=2.4041 P=.02), per capita gross domestic product (t0.05/2,1=2.6955; P=.01), per capita disposable income (t0.05/2,1=2.8303; P=.008), per capita consumption expenditure (PCCE; t0.05/2,1=2.7452; P=.01), and PCCE for health care (t0.05/2,1=2.5924; P=.01). The geographically weighted regression models indicated a positive association and spatial heterogeneity between cumulative mpox attack rates and the proportion of the urban population, per capita gross domestic product, per capita disposable income, and PCCE, with high R2 values in north and northeast China. Hot spots and HH clustering of mpox attack rates identified by local spatial autocorrelation analysis should be considered key areas for precision prevention and control of mpox. Specifically, Guangdong, Beijing, and Tianjin provinces should be prioritized for mpox prevention and control. These findings provide geographically precise and visualized evidence to assist in identifying key areas for targeted prevention and control.

Pengaruh Electronic Word Of Mouth Dan Citra Merek Terhadap Keputusan Pembelian Aplikasi Tokopedia

This study evaluates the influence of Electronic Word of Mouth (eWOM) and brand image on the purchasing decisions of students of the Faculty of Economics, Pasim Bandung National University on the Tokopedia application. eWOM includes reviews, testimonials, and recommendations through digital platforms, while brand image refers to students' perceptions of Tokopedia as a brand. This study uses a survey method with a questionnaire to collect data from students who have used the Tokopedia application. The analysis was conducted with multiple linear regression to determine the relationship between eWOM, brand image, and purchasing decisions. The results showed that eWOM had an influence of 4.7% on purchasing decisions, while brand image had an influence of 59.8%. Hypothesis testing shows that eWOM and brand image significantly influence purchasing decisions, with an R2 value of 0.646. Thus, about 64.6% of the variation in purchasing decisions on the Tokopedia application can be explained by the combined influence of eWOM and brand image. The remaining 35.4% is caused by other factors not examined in this study.