Socioeconomic and sociodemographic correlations to COVID-19 variability in the United States in 2020.

The COVID-19 pandemic provided an additional spotlight on the longstanding socioeconomic/health impacts of redlining and has added to the myriad of environmental justice issues, which has caused significant loss of life, health, and productive work. The Centers for Disease Control and Prevention (CDC) reports that a person with any selected underlying health conditions is more likely to experience severe COVID-19 symptoms, with more than 81% of COVID-19-related deaths among people aged 65 years and older. The effects of COVID-19 are not homogeneous across populations, varying by socioeconomic status, PM2.5 exposure, and geographic location. This variability is supported by analysis of existing data as a function of the number of cases and deaths per capita/1,00,000 persons. We investigate the degree of correlation between these parameters, excluding health conditions and age. We found that socioeconomic variables alone contribute to ~40% of COVID-19 variability, while socioeconomic parameters, combined with political affiliation, geographic location, and PM2.5 exposure levels, can explain ~60% of COVID-19 variability per capita when using an OLS regression model; socioeconomic factors contribute ~28% to COVID-19-related deaths. Using spatial coordinates in a Random Forest (RF) regressor model significantly improves prediction accuracy by ~120%. Data visualization products reinforce the fact that the number of COVID-19 deaths represents 1% of COVID-19 cases in the US and globally. A larger number of democratic voters, larger per-capita income, and age >65 years is negatively correlated (associated with a decrease) with the number of COVID cases per capita. Several distinct regions of negative and positive correlations are apparent, which are dominated by two major regions of anticorrelation: (1) the West Coast, which exhibits high PM2.5 concentrations and fewer COVID-19 cases; and (2) the middle portion of the US, showing mostly high number of COVID-19 cases and low PM2.5 concentrations. This paper underscores the importance of exercising caution and prudence when making definitive causal statements about the contribution of air quality constituents (such as PM2.5) and socioeconomic factors to COVID-19 mortality rates. It also highlights the importance of implementing better health/lifestyle practices and examines the impact of COVID-19 on vulnerable populations, particularly regarding preexisting health conditions and age. Although PM2.5 contributes comparable deaths (~7M) per year, globally as smoking cigarettes (~8.5M), quantifying any causal contribution toward COVID-19 is non-trivial, given the primary causes of COVID-19 death and confounding factors. This becomes more complicated as air pollution was reduced significantly during the lockdowns, especially during 2020. This statistical analysis provides a modular framework, that can be further expanded with the context of multilevel analysis (MLA). This study highlights the need to address socioeconomic and environmental disparities to better prepare for future pandemics. By understanding how factors such as socioeconomic status, political affiliation, geographic location, and PM2.5 exposure contribute to the variability in COVID-19 outcomes, policymakers and public health officials can develop targeted strategies to protect vulnerable populations. Implementing improved health and lifestyle practices and mitigating environmental hazards will be essential in reducing the impact of future public health crises on marginalized communities. These insights can guide the development of more resilient and equitable health systems capable of responding effectively to similar future scenarios.

Drug safety assessment by machine learning models.

The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial in drug safety assessment. In this study, we discuss machine learning approaches in the prediction of drug-induced TdP risks using preclinical data. Specifically, a random forest model was trained on the dataset generated by the rabbit ventricular wedge assay. The model prediction performance was measured on 28 drugs from the Comprehensive In Vitro Proarrhythmia Assay initiative. Leave-one-drug-out cross-validation provided an unbiased estimation of model performance. Stratified bootstrap revealed the uncertainty in the asymptotic model prediction. Our study validated the utility of machine learning approaches in predicting drug-induced TdP risks from preclinical data. Our methods can be extended to other preclinical protocols and serve as a supplementary evaluation in drug safety assessment.

Bioplastic derived from corn stover: Life cycle assessment and artificial intelligence-based analysis of uncertainty and variability.

Exploring feasible and renewable alternatives to reduce dependency on traditional fossil-based plastics is critical for sustainable development. These alternatives can be produced from biomass, which may have large uncertainties and variabilities in the feedstock composition and system parameters. This study develops a modeling framework that integrates cradle-to-grave life cycle assessment (LCA) with a rigorous process model and artificial intelligence (AI) models to conduct uncertainty and variability analyses, which are highly time-consuming to conduct using only the process model. This modeling framework examines polylactic acid (PLA) produced from corn stover in the U.S. An analysis of uncertainty and variability was conducted by performing a Monte Carlo simulation to show the detailed result distributions. Our Monte Carlo simulation results show that the mean life-cycle Global Warming Potential (GWP) of 1 kg PLA is 4.3 kgCO2eq (P5-P95 4.1-4.4) for composting PLA with natural gas combusted for the biorefinery, 3.7 kgCO2eq (P5-P95 3.4-3.9) for incinerating PLA for electricity with natural gas combusted for the biorefinery, and 1.9 kgCO2eq (P5-P95 1.6-2.1) for incinerating PLA for electricity with wood pellets combusted for the biorefinery. Tradeoffs for different environmental impact categories were identified. Based on feedstock composition variations, two AI models were trained: random forest and artificial neural networks. Both AI models demonstrated high prediction accuracy; however, the random forest performed slightly better.

A methodological showcase: utilizing minimal clinical parameters for early-stage mortality risk assessment in COVID-19-positive patients.

The scarcity of data is likely to have a negative effect on machine learning (ML). Yet, in the health sciences, data is diverse and can be costly to acquire. Therefore, it is critical to develop methods that can reach similar accuracy with minimal clinical features. This study explores a methodology that aims to build a model using minimal clinical parameters to reach comparable performance to a model trained with a more extensive list of parameters. To develop this methodology, a dataset of over 1,000 COVID-19-positive patients was used. A machine learning model was built with over 90% accuracy when combining 24 clinical parameters using Random Forest (RF) and logistic regression. Furthermore, to obtain minimal clinical parameters to predict the mortality of COVID-19 patients, the features were weighted using both Shapley values and RF feature importance to get the most important factors. The six most highly weighted features that could produce the highest performance metrics were combined for the final model. The accuracy of the final model, which used a combination of six features, is 90% with the random forest classifier and 91% with the logistic regression model. This performance is close to that of a model using 24 combined features (92%), suggesting that highly weighted minimal clinical parameters can be used to reach similar performance. The six clinical parameters identified here are acute kidney injury, glucose level, age, troponin, oxygen level, and acute hepatic injury. Among those parameters, acute kidney injury was the highest-weighted feature. Together, a methodology was developed using significantly minimal clinical parameters to reach performance metrics similar to a model trained with a large dataset, highlighting a novel approach to address the problems of clinical data collection for machine learning.

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran.

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

Exploring the environmental risks and seasonal variations of potentially toxic elements (PTEs) in fine road dust in resource-based cities based on Monte Carlo simulation, geo-detector and random forest model.

The environmental pollution caused by mineral exploitation and energy consumption poses a serious threat to ecological security and human health, particularly in resource-based cities. To address this issue, a comprehensive investigation was conducted on potentially toxic elements (PTEs) in road dust from different seasons to assess the environmental risks and influencing factors faced by Datong City. Multivariate statistical analysis and absolute principal component score were employed for source identification and quantitative allocation. The geo-accumulation index and improved Nemerow index were utilized to evaluate the pollution levels of PTEs. Monte Carlo simulation was employed to assess the ecological-health risks associated with PTEs content and source orientation. Furthermore, geo-detector and random forest analysis were conducted to examine the key environmental variables and driving factors contributing to the spatiotemporal variation in PTEs content. In all PTEs, Cd, Hg, and Zn exhibited higher levels of content, with an average content/background value of 3.65 to 4.91, 2.53 to 3.34, and 2.15 to 2.89 times, respectively. Seasonal disparities were evident in PTEs contents, with average levels generally showing a pattern of spring (winter) > summer (autumn). PTEs in fine road dust (FRD) were primarily influenced by traffic, natural factors, coal-related industrial activities, and metallurgical activities, contributing 14.9-33.9 %, 41.4-47.5 %, 4.4-8.3 %, and 14.2-29.4 % to the total contents, respectively. The overall pollution and ecological risk of PTEs were categorized as moderate and high, respectively, with the winter season exhibiting the most severe conditions, primarily driven by Hg emissions from coal-related industries. Non-carcinogenic risk of PTEs for adults was within the safe limit, yet children still faced a probability of 4.1 %-16.4 % of unacceptable risks, particularly in summer. Carcinogenic risks were evident across all demographics, with children at the highest risk, mainly due to Cr and smelting industrial sources. Geo-detector and random forest model indicated that spatial disparities in prioritized control elements (Cr and Hg) were primarily influenced by particulate matter (PM10) and anthropogenic activities (industrial and socio-economic factors); variations in particulate matter (PM10 and PM2.5) and meteorological factors (wind speed and precipitation) were the primary controllers of seasonal disparities of Cr and Hg.

Exploring the evolution of coupled natural-cultural ecosystem services and their geographically scaled driven modeling in a coastal city of Southeast China.

Rapid urban expansion and economic development challenges to the sustainability of ecosystem services (ESs), a solid understanding of the mechanisms that drive ESs helps policymakers to respond. However, few existing studies on ES-driven mechanisms emphasize the integration of natural and cultural services, with most neglecting spatial non-stationarity at the geographic scale. Here, we improved the ROS model to quantify cultural ecosystem services (CES) and developed a comprehensive ecosystem services index (CESI) by coupling CES with 6 typical natural ESs (carbon storage (CS), water yield (WY), nitrogen export (NE), soil conservation (SC), habitat quality (HQ), food supply (FS)), subsequently, Spearman's correlation and MGWR were employed to reveal the CESI-driven mechanism considering geographic scales. The results showed that: (1) From 2000 to 2020, CS, WY, SC, and HQ exhibited decline, which contrasts with the significant increase in CES. (2) The CESI showed a decreasing trend (3.28-3.70) while the coefficient of variation was increasing over time (0.11-0.15). The overall spatial distribution of CESI shows higher northwest than southeast, with strong spatial autocorrelation. (3) The CESI exhibits synergistic associations with CS, SC, HQ, and CES (0.54-0.83), and forms trade-offs with WY, NE, and FS. (4) Climate, vegetation, landscape, human, and topography have significant effects on CES and CESI with a significantly geographic scale differences, especially areas closer to the sea exhibit heightened sensitivity. Besides, the combined effects of multiple factors are stronger than any individual driver. The results emphasize the necessity of introducing ecological land in coastal cities and establishing natural reserves in high CESI areas to maintain diversity. The study improves the CES assessment methodology and proposes an integrated analytical framework that combines natural and cultural ESs with geographic-scale drivers, providing a new perspective on the analysis of ESs mechanisms.

Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer.

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine.

Do green finance and green innovation affect corporate credit rating performance? Evidence from machine learning approach.

This study investigates the impact of green finance (GF) and green innovation (GI) on corporate credit rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. The least absolute shrinkage and selection operators (LASSOs) machine learning algorithms are first used to select the critical drivers of corporate credit performance. Then, we applied partialing-out LASSO linear regression (POLR) and double selection LASSO linear regression (DSLR) machine learning techniques to check the impact of GF and GI on CR. The main results reveal that a 1% increase in GF diminishes CR by 0.26%, whereas GI promotes CR performance by 0.15%. Moreover, the heterogeneity analysis reveals a more significant negative effect of GF on the CR performance of heavily polluting firms, non-state-owned enterprises, and firms in the Western region. The findings raise policies for managing green finance and encouraging green innovation formation, as well as addressing company heterogeneity to support sustainability.

A comprehensive assessment of machine learning algorithms for enhanced characterization and prediction in orodispersible film development.

Orodispersible films (ODFs) have emerged as innovative pharmaceutical dosage forms, offering patient-specific treatment through adjustable dosing and the combination of diverse active ingredients. This expanding field generates vast datasets, requiring advanced analytical techniques for deeper understanding of data itself. Machine learning is becoming an important tool in the rapidly changing field of pharmaceutical research, particularly in drug preformulation studies. This work aims to explore into the application of machine learning methods for the analysis of experimental data obtained by ODF characterization in order to obtain an insight into the factors governing ODF performance and use it as guidance in pharmaceutical development. Using a dataset derived from extensive experimental studies, various machine learning algorithms were employed to cluster and predict critical properties of ODFs. Our results demonstrate that machine learning models, including Support vector machine, Random forest and Deep learning, exhibit high accuracy in predicting the mechanical properties of ODFs, such as flexibility and rigidity. The predictive models offered insights into the complex interaction of formulation variables. This research is a pilot study that highlights the potential of machine learning as a transformative approach in the pharmaceutical field, paving the way for more efficient and informed drug development processes.

Assessment of estrogenic potential from exudates of microcystin-producing and non-microcystin-producing Microcystis by metabolomics, machine learning and E-screen assay.

Cyanobacterial blooms, often dominated by Microcystis aeruginosa, are capable of producing estrogenic effects. It is important to identify specific estrogenic compounds produced by cyanobacteria, though this can prove challenging owing to the complexity of exudate mixtures. In this study, we used untargeted metabolomics to compare components of exudates from microcystin-producing and non-microcystin-producing M. aeruginosa strains that differed with respect to their ability to produce microcystins, and across two growth phases. We identified 416 chemicals and found that the two strains produced similar components, mainly organoheterocyclic compounds (20.2%), organic acids and derivatives (17.3%), phenylpropanoids and polyketides (12.7%), benzenoids (12.0%), lipids and lipid-like molecules (11.5%), and organic oxygen compounds (10.1%). We then predicted estrogenic compounds from this group using random forest machine learning. Six compounds (daidzin, biochanin A, phenylethylamine, rhein, o-Cresol, and arbutin) belonging to phenylpropanoids and polyketides (3), benzenoids (2), and organic oxygen compound (1) were tested and exhibited estrogenic potency based upon the E-screen assay. This study confirmed that both Microcystis strains produce exudates that contain compounds with estrogenic properties, a growing concern in cyanobacteria management.

Assessment of Risk Factors for Acute Kidney Injury with Machine Learning Tools in Children Undergoing Hematopoietic Stem Cell Transplantation.

Background: Although acute kidney injury (AKI) is a common complication in patients undergoing hematopoietic stem cell transplantation (HSCT), its prophylaxis remains a clinical challenge. Attempts at prevention or early diagnosis focus on various methods for the identification of factors influencing the incidence of AKI. Our aim was to test the artificial intelligence (AI) potential in the construction of a model defining parameters predicting AKI development. Methods: The analysis covered the clinical data of children followed up for 6 months after HSCT. Kidney function was assessed before conditioning therapy, 24 h after HSCT, 1, 2, 3, 4, and 8 weeks after transplantation, and, finally, 3 and 6 months post-transplant. The type of donor, conditioning protocol, and complications were incorporated into the model. Results: A random forest classifier (RFC) labeled the 93 patients according to presence or absence of AKI. The RFC model revealed that the values of the estimated glomerular filtration rate (eGFR) before and just after HSCT, as well as methotrexate use, acute graft versus host disease (GvHD), and viral infection occurrence, were the major determinants of AKI incidence within the 6-month post-transplant observation period. Conclusions: Artificial intelligence seems a promising tool in predicting the potential risk of developing AKI, even before HSCT or just after the procedure.

The impact of comorbidities and economic inequality on COVID-19 mortality in Mexico: a machine learning approach.

Introduction: Studies from different parts of the world have shown that some comorbidities are associated with fatal cases of COVID-19. However, the prevalence rates of comorbidities are different around the world, therefore, their contribution to COVID-19 mortality is different. Socioeconomic factors may influence the prevalence of comorbidities; therefore, they may also influence COVID-19 mortality. Methods: This study conducted feature analysis using two supervised machine learning classification algorithms, Random Forest and XGBoost, to examine the comorbidities and level of economic inequalities associated with fatal cases of COVID-19 in Mexico. The dataset used was collected by the National Epidemiology Center from February 2020 to November 2022, and includes more than 20 million observations and 40 variables describing the characteristics of the individuals who underwent COVID-19 testing or treatment. In addition, socioeconomic inequalities were measured using the normalized marginalization index calculated by the National Population Council and the deprivation index calculated by NASA. Results: The analysis shows that diabetes and hypertension were the main comorbidities defining the mortality of COVID-19, furthermore, socioeconomic inequalities were also important characteristics defining the mortality. Similar features were found with Random Forest and XGBoost. Discussion: It is imperative to implement programs aimed at reducing inequalities as well as preventable comorbidities to make the population more resilient to future pandemics. The results apply to regions or countries with similar levels of inequality or comorbidity prevalence.

Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing data.

China has become the world's largest emitter of carbon dioxide, putting significant pressure on the government to reduce emissions. This study analyzes the driving factors of carbon emissions in 281 prefecture-level cities in China from 2003 to 2019, based on carbon emission data matched with the locations of thermal power stations and nighttime light data. Firstly, we compare the accuracy of multivariate linear regression and random forest models, finding that the random forest regression yields superior results. Then, we rank the impact of various factors using the random forest method, revealing that population, economic development, and industrialization are the top three influencing factors. The interaction between population and economic development explains 68.5% of carbon emissions, with regional variations in the ranking of influencing factors. The main policy implications of this study are as follows: firstly, there is no need to overly concern about the impact of population growth on carbon emissions, and policies regarding fertility can be adjusted flexibly; secondly, controlling urbanization to a certain extent is conducive to achieving efficient low-carbon cities; thirdly, during the process of industrialization, carbon emissions inevitably increase, and it is advisable to accelerate industrialization to reach a turning point as soon as possible.

A Cost-Effective Model for Predicting Recurrent Gastric Cancer Using Clinical Features.

This study used artificial intelligence techniques to identify clinical cancer biomarkers for recurrent gastric cancer survivors. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random Forest using MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged the synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive exPlanations (SHAPs) for feature importance analysis in this study. Our proposed Random Forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86%, and an F1 of 88.2% on the recurrent category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI (body mass index), and gender; these features significantly affect the prediction model's output and are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches.

Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events.

[A study of cognitive impairment quantitative assessment method based on gait characteristics].

Alzheimer's disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject's MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.

Machine learning-based bioimpedance assessment of knee osteoarthritis severity.

This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA. All participants performed 20 repetitions of each of the following five tasks: (i) sitting with the knee bent, (ii) sitting with the knee extended, (iii) sitting and performing successive extensions and flexions of the knee, (iv) standing, and (v) walking. Data from the 15 experimental setups (3 types of measurements×5 exercises) were used to train a multiclass random forest. The training and validation cycle was repeated 100 times using random undersampling. At each of the 100 cycles, 80% of the data were used for training and the rest for testing. The results showed that the proposed approach achieved average sensitivities and specificities of 100% for the four KOA severity grades in the extension, cyclic, and gait tasks. This suggests that the proposed method can serve as a screening tool to determine which individuals should undergo x-rays or magnetic resonance imaging for further evaluation of KOA.

Occurrence, fate, and risk assessment of antibiotics in conventional and advanced drinking water treatment systems: From source to tap.

The occurrence and removal of 38 antibiotics from nine classes in two drinking water treatment plants (WTPs) were monitored monthly over one year to evaluate the efficiency of typical treatment processes, track the source of antibiotics in tap water and assess their potential risks to ecosystem and human health. In both source waters, 18 antibiotics were detected at least once, with average total antibiotic concentrations of 538.5 ng/L in WTP1 and 569.3 ng/L in WTP2. The coagulation/flocculation and sedimentation, sand filtration and granular activated carbon processes demonstrated limited removal efficiencies. Chlorination, on the other hand, effectively eliminated antibiotics by 48.7 ± 11.9%. Interestingly, negative removal was observed along the distribution system, resulting in a significant antibiotic presence in tap water, with average concentrations of 131.5 ng/L in WTP1 and 362.8 ng/L in WTP2. Source tracking analysis indicates that most antibiotics in tap water may originate from distribution system. The presence of antibiotics in raw water and tap water posed risks to the aquatic ecosystem. Untreated or partially treated raw water could pose a medium risk to infants under six months. Water parameters, for example, temperature, total nitrogen and total organic carbon, can serve as indicators to estimate antibiotic occurrence and associated risks. Furthermore, machine learning models were developed that successfully predicted risk levels using water quality parameters. Our study provides valuable insights into the occurrence, removal and risk of antibiotics in urban WTPs, contributing to the broader understanding of antibiotic pollution in water treatment systems.

Assessment of machine learning algorithms and new hybrid multi-criteria analysis for flood hazard and mapping.

Floods in Iran cause a lot of damage in different places every year. The 2019 floods of the Gorgan and Atrak rivers basins in the north of Iran were one of the most destructive events in this country. Therefore, investigating the flood hazard of these areas is very necessary to manage probable future floods. For this purpose, in the present study, the capability of Random Forest (RF) and Support Vector Machine (SVM) algorithms was investigated in combination with Sentinel series and Landsat-8 images to prepare the 2019 flood map. Then, the flood hazard map of these areas was prepared using the new hybrid Fuzzy Best Worse Model-Weighted Multi-Criteria Analysis (FBWM-WMCA) model. According to the results of the FBWM-WMCA model, 38.58%, 50.18%, 11.10%, and 0.14% of the Gorgan river basin and 45.11%, 49.96%, 4.17%, and 0.076% of the Atrak river basin are in high, medium, low, and no hazards, respectively. The highest flood hazard areas in Gorgan and Atrak rivers basins in the north, northwest, west, and east, and south and southwest are mostly at medium flood hazard. Also, the results of RF and SVM algorithms with an overall accuracy of more than 85% for Sentinel-1, Sentinel-2, and Landsat-8 images and 80% for Sentinel-3 images indicate that the flooding is related to the western, southwestern, and northern regions including agricultural, bare lands and built up. According to the obtained results and the efficiency of the FBWM-WMCA model, the Gorgan and Atrak rivers basins need proper planning for flood hazard management.