In this study, we synthesized a novel compound, 3-(5-amino-1,3,4-thiadiazol-2-yl)-6-ethyl-4-hydroxy-2H-pyrano[3,2-c]quinoline-2,5(6H)-dione (ATEHPQ), through a condensation reaction between 6-ethyl-4-hydroxy-2,5-dioxo-5,6-dihydro-2H-pyrano [3,2-c]quinoline-3-carboxaldehyde and thiosemicarbazide, followed by oxidative cyclization. We characterized ATEHPQ using elemental analysis, IR, ¹H and ¹³C NMR spectroscopy, and mass spectrometry. Density Functional Theory (DFT) calculations with the B3LYP/6–311++G(d,p) basis set were employed to optimize the molecular geometry and analyze global reactivity descriptors, including HOMO-LUMO energies. The Molecular Electrostatic Potential (MEP) map was used to identify reactive sites, and drug-likeness studies indicated potential pharmaceutical applications. Notably, ATEHPQ showed a higher first hyperpolarizability (β_tot) compared to urea, suggesting its suitability for nonlinear optical applications. We also determined the Miller indices for ATEHPQ's preferred orientations using a specialized program. Williamson–Hall analysis revealed an average crystal size of 26.08 nm and a lattice strain of 6.3 × 10⁻³. The thin films exhibited three distinct absorption peaks at 2.8, 3.41, and 4.21 eV, with a direct energy gap of 2.43 eV. Dispersion parameters from the single oscillator model provided oscillator and dispersion energies of 3.12 eV and 14.21 eV, respectively, with a high-frequency dielectric constant of 4.71. The ATEHPQ thin films, when combined with n-Si, demonstrated significant improvements in photovoltaic performance: the open-circuit voltage (V_oc) rose from 0.13 V to 0.521 V, the short-circuit current (Isc) increased from 0.253 mA to 2.94 mA, the fill factor (FF) improved from 0.238 to 0.33, and the efficiency (η) grew from 0.71 % to 4.64 % with increased illumination intensity. These results highlight the excellent photovoltaic and photodetection capabilities of ATEHPQ thin films, underscoring their potential for advanced optoelectronic and solar cell applications.

Accurate forecasting of waste is essential for effective management and allocation of resources. As urban populations grow, the demand for municipal waste systems increases, creating the need for reliable forecasting methods to support planning and decision making. This study compares statistical models Error Trend Seasonality (ETS) and Auto Regressive Integrated Moving Average (ARIMA) with advanced machine learning approaches, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks. Five waste categories were analyzed: dead animal, building, commercial, domestic, and liquid waste. Historical datasets were used for model training and validation, with accuracy assessed through mean absolute error and root mean squared error. Results indicate that ARIMA generally outperforms ETS in forecasting building, commercial, and domestic waste streams, especially in capturing long-term domestic waste patterns. Both statistical models, however, show limitations in predicting liquid waste due to its irregular and highly variable nature, where even baseline models sometimes perform competitively. In contrast, machine learning methods consistently achieve the lowest forecasting errors across all categories. Their capacity to capture nonlinear relationships and adapt to complex datasets highlights their reliability for real-world waste management. The findings underline the importance of selecting forecasting techniques tailored to the characteristics of each waste type rather than applying a uniform method. By improving forecasting accuracy, municipalities and policymakers can design more effective waste management strategies that align with Sustainable Development Goal 11 on sustainable cities and communities, Sustainable Development Goal 12 on responsible consumption and production, and Sustainable Development Goal 13 on climate action.

Doxorubicin (DOX), an anticancer drug, is used to treat several types of tumors, but it has detrimental side effects that restrict its therapeutic efficacy. One is the iron-dependent form of ferroptosis, which is characterized by elevated ROS production and iron overload. Syzygium aromaticum has a diverse range of biological and pharmaceutical actions due to their antioxidant properties. This study investigated the effect of S. aromaticum extract (SAE) on hepatotoxicity caused by DOX in rats. Phytochemical analysis was performed to assess compounds in SAE. The ADMETlab 2.0 web server was used to predict the pharmacokinetic properties of the most active components of SAE when DOX was injected into rats. Molecular docking studies were performed using AutoDock Vina. Forty male Sprague Dawley rats were divided into four groups of ten rats each (G1 was a negative control group, G2 was given 1/10 of SAE LD₅₀ by oral gavage (340 mg/kg), G3 was given 4 mg/kg of DOX intraperitoneally (i.p.) once a week for a month, and G4 was administered DOX as in G3 and SAE as in G2). After a month, biochemical and histopathological investigations were performed. Rats given SAE had promising levels of phytochemicals, which could significantly ameliorate DOX-induced hepatotoxicity by restoring biochemical alterations, mitigating ferroptosis, and upregulating the NRF-2–SLC7A-11–GPX-4 signaling pathway. These findings suggest that SAE could potentially alleviate DOX-induced hepatotoxicity in rats.