This paper aims to unravel the driving forces behind carbon dioxide emissions in low- and high-income countries by applying gradient boosting and random forest algorithms. The study reveals that gradient boosting demonstrates superior accuracy over random forests in low-income countries, whereas the opposite pattern is observed in high-income countries. Additionally, the study demonstrates that, according to the gradient boosting algorithm-based feature selection, the major influencers of carbon dioxide emissions in low-income countries are the agriculture sector (49.9%), the industry sector (17%), the services sector (10.4%), population size (9.8%), gross domestic product growth (7%), and foreign direct investment inflow (5.3%). Furthermore, random forest algorithm-based feature selection reveals that, in high-income countries, the key drivers of carbon dioxide emissions are the services sector (30.8%), the agriculture sector (27.1%), the industry sector (21.5%), population size (19%), foreign direct investment inflow (1.2% - A different working methodology than low-income countries), and gross domestic product growth (0.4%). Moreover, the study corroborates that low carbon dioxide emissions in low-income countries correlate positively with industrial sector growth, foreign direct investment inflow, gross domestic product, and population size but negatively correlate with the agricultural and service sectors. In the case of high-income countries, carbon dioxide emissions positively correlate with foreign direct investment inflow, industrial and agricultural sector growth, and gross domestic product while exhibiting a negative correlation with population size and service sector growth.

Dental composites are widely used restorative materials but secondary caries to the restored tooth, polymerization shrinkage, and mechanical failure, limiting their longevity. Incorporating nanoparticles synthesized via eco-friendly methods offers a promising strategy to enhance composite performance. This study introduces a novel, eco-friendly strategy to enhance dental composite resins by incorporating TiO₂-NPs synthesized using Vitis vinifera extract. This study uniquely employs Vitis vinifera, rich in flavonoids and phenolics, as a reducing and capping agent, and for the first time integrates molecular docking analysis to explore the mechanistic antimicrobial interaction between TiO₂-NPs and Streptococcus mutans glucosyltransferase. The research evaluates the impact of these green-synthesized nanoparticles on antimicrobial properties, mechanical performance, polymerization shrinkage, and their molecular interactions with Streptococcus mutans glucosyltransferase via molecular docking analysis. The green-synthesized TiO₂-NPs were thoroughly characterized and integrated into dental composites at 10% and 20% by weight (Groups II and III, respectively), while Group I (control) contained only fumed silica fillers. The antimicrobial activity was assessed against S. mutans, S. sanguinis, and L. acidophilus, and mechanical properties (flexural strength and microhardness) were evaluated according to ISO standards. Polymerization shrinkage was measured using a strain gauge. Additionally, molecular docking analysis was performed to investigate the interaction of TiO₂-NPs with S. mutans glucosyltransferase. The modified composites exhibited significantly improved antimicrobial activity against key oral pathogens and superior mechanical properties compared to the control. Molecular docking studies provided insights into the potential antimicrobial mechanisms of TiO₂-NPs. These findings suggest that green-synthesized TiO₂-NPs offer a promising, eco-friendly approach to improve the performance and longevity of dental restorative materials.

Given that inflation is one of the most important problems facing the Egyptian economy, determining the factors affecting it is very important. Thus, we aim to use machine learning algorithms like Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (R.F.), Neural Network (ANN), Gradient boosting (G.B.) and decision tree (D.T.) to determine the accurate algorithm and analyze the factors affecting on Egypt inflation. The study found that the G.B. algorithm is the most accurate among the used algorithms and showed that The major significant variables determining inflation in Egypt are the exchange rate (30.5%), gross fixed formation (24.5%) and government expenditure (12.3%). We also found a positive relationship between the inflation rate and government expenditure, money supply, gross domestic product (GDP) growth, gross fixed formation, foreign direct investment, GDP per capita and exchange rate. Furthermore, there is a negative relationship between the inflation rate, household expenditure and the external trade balance.

Recurrent caries continues to pose a major problem in restorative dentistry. Although numerous antimicrobial agents have been added to restorative materials, their effectiveness and their influence on the materials’ mechanical properties are still uncertain. This study investigated the physicomechanical performance of glass ionomer cement (GIC) modified with green-synthesized titanium dioxide nanoparticles prepared using Citrus aurantium seed extract (CA-TiO₂NPs). The novelty of this work lies in integrating a plant-mediated synthesis approach with systematic mechanical evaluation and preliminary in silico safety screening within a single proof-of-concept framework. CA-TiO₂NPs were synthesized via phytoreduction using C. aurantium seed extract and added to the powder phase of a conventional GIC at 5% and 10% w/w. Flexural strength was evaluated according to ISO 9917-2:2017, water sorption and solubility according to ISO 4049:2009, and Vickers microhardness according to ASTM E384. The major bioactive constituents identified by Gas Chromatography-Mass Spectrometry (GC–MS) were profiled. In addition, computational toxicological and ADME predictions were performed to provide early-stage safety indicators. While nanoparticle incorporation influenced selected mechanical parameters, flexural strength differences were not statistically significant. The 10% CA-TiO₂ NPs group exhibited the highest flexural modulus and microhardness (P < 0.0001) and lowest water sorption and solubility. Additionally. In silico pharmacokinetic and toxicological analyses suggested a favorable preliminary safety profile of the phytochemical constituents, supporting proof-of-concept evaluation rather than clinical validation. The combination of green synthesis and mechanical testing, together with screening-level toxic informatics of major extract-associated metabolites, supports a proof-of-concept for improving CA TiO₂ NPs –modified GIC properties while prioritizing safety-relevant endpoints for subsequent leachate and cytotoxicity testing. Clinical Significance: Computational integration strengthens the translational validity of green nanomaterial development by providing preliminary molecular-level safety indicators for phytochemical constituents potentially associated with the modified material.

This study aims to determine the primary determination of FDI inflow to Egypt using machine learning algorithms and the ARIMA model and get an accurate prediction of FDI inflow to Egypt during the current decade (2020–2030) and approved that the gradient boosting model is the most accurate algorithms. Also, we find stability in economic indicators in Egypt during the current decade using the ARIMA model. The last step approved that the primary determinant of FDI inflow to Egypt is the Human Development Index, followed by population size, gross domestic product per capita, lending rate, and gross domestic product value.