Otitis media has profound health and economic impact, and its occurrence is known to be influenced by air pollution and climate. The purpose of this study was to develop prediction models using climate and air pollution indicators for the occurrence of acute otitis media (AOM). The study was conducted from January 1, 2014, to December 31, 2019, and included pediatric patients (age < 12 years) diagnosed on their emergency room visit in our tertiary medical institution. We obtained data on the weekly number of AOM patients and the weekly average values of air pollution and climate indicators. Poisson regression analysis and eXtreme Gradient Boosting (XGBoost) were used to develop prediction models for the overall pediatric patients and for the patients classified according to sex (male and female) and age (< 2 years and ≥ 2 years). For the overall population, the correlation coefficients between the original and estimated data in the testing set were 0.441 (p < 0.001) and 0.844 (p < 0.001) for the models developed using Poisson regression analysis and XGBoost, respectively. The root-mean-square errors in the testing set were 3.094 and 1.856, respectively. For patients classified according to sex and age, the prediction models developed using XGBoost showed better performance than the models developed using Poisson regression analysis. In conclusion, this study successfully developed prediction models with air pollution and climate indicators for the incidence of pediatric AOM, using XGBoost. This model can be further developed to prevent pediatric AOM.

Vermicomposting of textile mill sludge (TMS) with cow dung (CD) and tea waste (TW) as amendments was done in seven different combinations using Eisenia fetida for 90 days. Results revealed that pH decreased from 7.68–8.63 to 7.09–7.59. TOC content and C/N ratio reductions were in range of 15.71–20.08% and 39.33–50.05%, respectively (P < 0.05). The macronutrients in the form of TN, TP, and TK increased 0.38–0.64, 1.07–2.27, and 0.56–1.98 times respectively after end of bioconversion process (P < 0.05), among increases in ash content and EC. The biomass and cocoon production of E. fetida increased significantly (P < 0.05), while high mortality rate of earthworms was observed in treatments containing 50% or more TMS content. The bacterial population beneficial for degradation of organic matter increased significantly over initial substrates (0th day) (P < 0.05). Increased humification index in end-product indicated better maturity of vermicompost as observed in treatments containing higher proportions of amendments. The addition of amendments favored earthworm activity which significantly decreased the heavy metal concentration (Fe, Cu, Pb, Zn) in the end-product. The study concluded that sustainable utilization of TMS could be achieved for cleaner and enriched vermicompost production by addition of amendments CD and TW in proportions of 50% and above.

In this work, the effect of using Ag-doped TiO₂ nanopigments on optical, mechanical and antimicrobial properties of coated paper was explored. Furthermore, the long-term antimicrobial activity of the coated paper was examined for up to 25 years. Titanium dioxide (TiO₂) nanoparticles have been synthesized and doped with different percentages of Ag nanoparticles (Ag NPs) using a simple wet chemical approach. The Ag@TiO₂ modified nanopigments were in the form of nanorods with an average size of about 20 nm as observed from TEM images. Increasing Ag content from 0.01 to 1.0% showed an increase in the mechanical properties of coated paper in terms of tensile, stretching, tensile energy absorption and burst while preserving the optical properties. Moreover, the antimicrobial inhibition activity increased with increasing the Ag content. The 1% Ag@TiO₂ showed a long-lasting antimicrobial effect against Staphylococcus aureus (S. aureus) Gram-positive bacteria even after 25 years of ageing (93.4% inhibition). Investigation of reactive oxygen species (ROS) generation and reaction mechanism of antimicrobial activity over Ag/TiO₂ under visible light is proposed. These results suggest that Ag/TiO₂ NPs can be potentially used as a disinfection coating for paper and improving its mechanical properties.

Floods are among the most devastating environmental hazards that directly and indirectly affect people’s lives and activities. In many countries, sustainable environmental management requires the assessment of floods and the likely flood-prone areas to avoid potential hazards. In this study, the performance and capabilities of seven machine learning algorithms (MLAs) for flood susceptibility mapping were tested, evaluated, and compared. These MLAs, including support vector machine (SVM), random forest (RF), multivariate adaptive regression spline (MARS), boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA), were tested for the area between Safaga and Ras Gharib cities, Red Sea, Egypt. A geospatial database was developed with eleven flood-related factors, namely altitude, slope aspect, lithology, land use/land cover (LULC), slope length (LS), topographic wetness index (TWI), slope angle, profile curvature, plan curvature, stream power index (SPI), and hydrolithology units. In addition, 420 actual flooded areas were recorded from the study area to create a flood inventory map. The inventory data were randomly divided into training group with 70% and validation group with 30%. The flood-related factors were tested with a multicollinearity test, the variance inflation factor (VIF) was less than 2.135, the tolerance (TOL) was more than 0.468, and their importance was evaluated with a partial least squares (PLS) method. The results show that RF performed the best with the highest AUC (area under curve) value of 0.813, followed by GLM with 0.802, MARS with 0.801, BRT with 0.777, MDA with 0.768%, FDA with 0.763, and SVM with 0.733. The results of this study and the flood susceptibility maps could be useful for environmental mitigation, future development activities in the area, and flood control areas.

Recognizing the factors responsible for the gradual increase in greenhouse gas [e.g. carbon dioxide (CO₂)] emissions is crucial to reduce the detrimental consequences on environmental sustainability and human life. Accordingly, spotting the sectors which contribute the most to CO₂ emissions and dampen economic growth have become one of the major concerns for policymakers around the globe. Against this background, this paper examines the nexus between economic growth and CO₂ emissions in three Baltic countries namely Estonia, Latvia and Lithuania. Thus, the study basically checks the validity of the environmental Kuznets curve (EKC) hypothesis by taking into account the role of energy consumption and financial development over the period of 1990–2018. This type of study is highly important for the region in order to comply with the commitments of the Paris Agreement and Sustainable Development Goals of the United Nations. The study first employs appropriate testing procedures and second-generation panel data methods to account for cross-sectional dependency and slope heterogeneity among countries. Applying unit roots and cointegration tests, the study then employed different mean group estimation models and heterogeneous panel causality methods suitable for cross-sectionally dependent and heterogeneous panels. The results of the econometric analyses reveal that the inverted U-shaped EKC hypothesis does not hold in the Baltic countries. But the pollution haven hypothesis is evidenced to hold for these nations. By boosting the CO₂ emissions figures, again, the study also revealed that higher levels of energy consumption exhibit adverse environmental consequences. Financial development is found to be effective in explaining the variations in the CO₂ emission figures of the selected countries as well. Causality test results confirm bi-directional causality between economic growth and CO₂ emissions, energy use and CO₂ emissions, CO₂ emissions and financial development, energy use and economic growth as well as between energy use and financial development. Furthermore, country-specific impacts are found to be similar to the corresponding panel estimates. Consistent with the findings, the study finally puts forward some policy-level suggestions. Accordingly, it is recommended that the Baltic countries need to move away from fossil-fuel dependent energy consumption growth policies to mitigate environmental degradation.

Historically, the Santos estuary illustrated a scenario of pollution and environmental catastrophes. In 1980, it was called the Vale da Morte (Death Valley), after the public resonance and mitigation measures became a model of environmental recovery. This study aims at assessing the ecosystem health of Santos estuary using Mytella falcata bivalve as bioindicator, and histopathology as biomarker. Bivalves were collected at three sample sites: Site A, located in Branco River with no industrial impact; Sites B, located in Barnabé Island, at the left bank of Santos Port; Site C, located next to Companhia Siderúrgica Paulista, in direct contact with industrial effluents. The collected animals had their gills dissected, fixed, and processed according to the histological routine. The tissue sections were dyed with hematoxylin and eosin to identify morphological changes. To analyze the slides, we used a disorder classification system and frequency index assignment to quantify the disorders observed. For the statistical background, tests of normality and of significant difference among the sample sites were performed. Results showed severe disorders in organisms from Sites B and C, such as tumors; however, the Site B revealed more expressive complications, such as necrosis. In contrast, Site A presented only mild disorders, such as infiltration. By analyzing the results and comparing environmental data, it is possible to affirm that the pollution status on the Santos estuary remains similar to the last histopathology study published, approximately 10 years ago, pointing out high concentrations of contaminants that directly affect the ecosystem and the organisms’ health.

In a context of climate change and global warming, the literature paid more and more attention to the determinants of energy consumption. This article aims at examining the influences of the financial development and the institutional quality on the energy consumption in a global sample of 112 countries between 2002 and 2014. Our analysis is based on dynamic two-step system GMM estimations for three different energy consumption indicators—our findings are interesting. First, the financial development induces a higher energy consumption per capita; a higher energy consumption per output, and a lower renewable energy consumption. Second, the institutions have an insignificant positive influence on the energy use per capita and the energy use per output. Third, and this is our major contribution, the institutional quality can actually reverse the effect of the financial development. In other words, the effect of financial development on the energy use per capita is positive in weak institutional environment but it is negative when the latter is well developed. This article discusses these finding and their implications.

The critical rare earth element dysprosium (Dy) is integral for sustainable technologies. What is concerning is that Dy is in imminent short supply and no current replacements yet exist, coupled with increasing environmental Dy levels influenced by anthropogenic activities. This study applies chemometric methods such as response surface methodology and artificial neural networks to predict low Dy removal levels using the biosorbent Euglena gracilis. A three-factor Box-Behnken experimental design was conducted with initial concentration (1 to 100 µg L⁻¹), contact time (30 to 180 min), and pH (3 to 8) as the three independent variables, and percentage removal and sorption capacity (q) as dependent variables. Using Dy percentage removal as response, for the worst and best conditions ranged from 0 to 92% respectively, with an average removal of 66 ± 4%. Using sorption capacity (q) as a different response variable, q varied from 0 to 93 µg/g with 27 ± 4 µg/g capacity as average. Maximum removal was 92% (q = 93 µg/g) was at pH 3, a contact time of 105 min and at a concentration of 100 µg/L. Using sorption capacity as the response variable for ANOVA, pH and metal concentrations were statistically significant factors, with lower pH and higher metal concentration having improved Dy removal, with a desirability near 1. Statistical tests such as analysis of variance, lack-of-fit, and coefficient of determination (R²) confirmed model validity. A 3–10-1 ANN network array was used to model experimental responses (q). RSM and ANN effectively modeled Dy biosorption. E. gracilis proved to be a cheap and effective biosorbent for Dy biosorption and has the potential to remediate acid mine drainage areas exhibiting low Dy concentrations.

Deregulated lncRNA DSCAM-AS1 expression was found in several tumors. However, mechanism and functional role of DSCAM-AS1 in cervical carcinoma remain unknown. DSCAM-AS1 was detected in cervical carcinoma specimens and cells by RT-qPCR. CCK-8, Matrigel transwell, and flow cytometry were conducted to determine cell functions. In this research, we firstly we explored DSCAM-AS1 expression in cervical carcinoma cells and specimens. We revealed that DSCAM-AS1 was upregulated in cervical carcinoma lines (C4-1, Caski, Hela, and Siha) compared to GH329 cells. DSCAM-AS1 was upregulated in cervical carcinoma specimens compared to control no-tumor specimens. Overexpression of DSCAM-AS1 induced cervical carcinoma cell growth and cycle. Moreover, our data revealed that miR-338-3p expression was downregulated in cervical carcinoma cells and specimens. There was a negative correlation between miR-338-3p expression and DSCAM-AS1 expression in cervical carcinoma specimens. Elevated expression of miR-338-3p decreased cervical carcinoma cell growth and cycle and invasion. Furthermore, luciferase reporter analysis revealed that miR-338-3p overexpression suppressed luciferase activity of WT-DSCAM-AS1 vector but not the mut-DSCAM-AS1. Ectopic expression of DSCAM-AS1 decreased miR-338-3p expression in the Siha cell. Overexpression of DSCAM-AS1 promoted cervical carcinoma cell growth and cycle via regulating miR-338-3p. These results suggested that DSCAM-AS1 functions as one oncogene through sponging miR-338-3p in cervical carcinoma.