Excessive melanin accumulation causes hyperpigmentation, a skin pathophysiology that highlights the need for safe and multifunctional skin-whitening agents. Building on the reported tyrosinase inhibitory potential of phenylthiazole derivatives, a series of compounds was designed and synthesized to investigate the structural determinants of their multi-functional activities. The biological evaluation included mushroom tyrosinase (mTYR) inhibition, as well as anti-melanogenic, anti-inflammatory, and antioxidant assays. Among the series, compound 3c exhibited the most potent multi-functional profile, showing mTYR inhibition (IC50 = 30.8 ± 2.7 µM) with a mixed-type inhibitory mechanism (Ki = 20 µM), a 34
Sufficient efforts have been carried out to fabricate highly efficient graphene oxide (GO) lamellar membranes for heavy metal ion separation and desalination of water. However, selectivity for small ions remains a major problem. Herein, GO was modified by using onion extractive (OE) and a bioactive phenolic compound, i.e., quercetin. The as-prepared modified materials were fabricated into membranes and used for separation of heavy metal ions and water desalination. The GO/onion extract (GO/OE) composite membrane with a thickness of 350 nm shows an excellent rejection efficiency for several heavy metal ions such as Cr6+ (∼87.5%), As3+ (∼89.5%), Cd2+ (∼93.0%), and Pb2+ (∼99.5%) and a good water permeance of ∼460 ± 20 L m-2 h-1 bar-1. In addition, a GO/quercetin (GO/Q) composite membrane is also fabricated from quercetin for comparative studies. Quercetin is an active ingredient of onion extractives (2.1% w/w). The GO/Q composite membranes show good rejection up to ∼78.0, ∼80.5, ∼88.0, and 95.2% for Cr6+, As3+, Cd2+, and Pb2+, respectively, with a DI water permeance of ∼150 ± 10 L m-2 h-1 bar-1. Further, both membranes are used for water desalination by measuring rejection of small ions such as NaCl, Na2SO4, MgCl2, and MgSO4. The resulting membranes show >70% rejection for small ions. In addition, both membranes are used for filtration of Indus River water and the GO/Q membrane shows remarkably high separation efficiency and makes river water suitable for drinking purpose. Furthermore, the GO/QE composite membrane is highly stable up to ∼25 days under acidic, basic, and neutral environments as compared to GO/Q composite and pristine GO-based membranes.
Biotin serves as a coenzyme in carboxylation reactions and plays a fundamental role in supporting growth, nutrient metabolism and overall physiological function in aquatic animals. This study investigated the biochemical and metabolic responses of graded dietary biotin in Penaeus vannamei. Five experimental diets were prepared with graded biotin supplementation levels of 0, 2, 4, 6 and 8 mg/kg (designated as Con, B02, B04, B06 and B08, respectively). The corresponding analyzed biotin concentrations were 1.65, 3.76, 5.84, 8.14 and 10.41 mg/kg, respectively. Twenty shrimp (0.55 ± 0.01 g) were randomly stocked into each tank, with four replicate tanks assigned to each experimental diet. The shrimp were fed the diets six times daily for 6 weeks. Dietary biotin supplementation significantly increased growth and improved feed utilization and protein efficiency ratio. Survival was not affected by the dietary treatment during the feeding trial. Whole-body lipid decreased linearly with increasing dietary biotin concentration. Hepatopancreatic biotin concentration exhibited a significant linear increase with graded dietary biotin concentrations. Chymotrypsin, lipase and amylase activities and intestinal villi height showed significant quadratic responses to dietary biotin concentration. Lysozyme activity increased linearly with dietary biotin concentrations, while antiprotease activity was significantly elevated in B02, B04 and B06 groups compared to Con group. Superoxide dismutase activity was significantly elevated in all biotin-supplemented groups. Based on piece-wise regression analysis, the optimal dietary biotin concentration for growth and feed conversion ratio was estimated at 3.09 to 3.66 mg/kg, respectively.
The effects of the different diffusivities of the displacing and displaced components on the growth of miscible viscous fingering in a radial porous medium are analyzed theoretically and numerically. By considering viscosity profiles determined by the diffusivity ratio and the log-viscosity parameters, six stability regimes are identified. For each regime, the influences of physical parameters on the onset and the growth of the radial viscous fingering are examined using linear stability analysis (LSA) and numerical simulations. In the present regime IVU, where viscosity decreases monotonically with an inflection point, a new dynamic stability criterion is proposed to explain the instabilities without viscosity mismatch, and its validity is demonstrated through linear stability analysis (LSA) and numerical simulations. Although the system is initially stable, double-diffusive effects render it unstable in the present regime V. Unlike a system with identical diffusivities, the Péclet number delays the onset and suppresses the growth of fingering motion in regimes IVU and V. Because of the differences in the spatio-temporal domains between the linear stability analysis (LSA) and the numerical simulations, visible motion is not observed in the present simulations, particularly for instabilities that grow slowly.
Electric vehicles (EVs) play a pivotal role in sustainable mobility and the decarbonization of transport sector. Accurate forecasting of energy demand at EV charging station clusters (EVCSCs) is essential for optimizing smart grid operations, reducing peak load stress, and guiding cost-effective infrastructure deployment. This study presents a unified and scalable hybrid deep learning framework that integrates convolutional neural networks (CNNs), bidirectional LSTM and GRU units (BiLSTM/BiGRU), and attention mechanisms to model complex spatiotemporal dynamics in EV charging data. Domain-specific feature engineering, incorporating lag and rolling window statistics, is combined with Keras Tuner-based hyperparameter tuning and Genetic Algorithm-based time-step optimization to enhance the models’ adaptability and performance across diverse urban environments. Unlike conventional univariate methods, our framework supports multivariate forecasting by jointly predicting daily energy consumption, greenhouse gas (GHG) savings, and gasoline displacement. It also includes weekly peak day forecasting, enabling utility operators to anticipate high-load periods and manage demand-side resources more effectively. The models are trained and evaluated on four real-world public datasets (Palo Alto, Boulder, Dundee, Perth) representing varying consumption patterns and feature granularities. The results demonstrate superior accuracy in terms of RMSE, MAE, and R2 (up to 0.99), consistently outperforming baseline and state-of-the-art approaches. By integrating operational forecasting with environmental impact metrics, this framework provides a scalable and data-driven tool for intelligent EV energy management system (EMS). It contributes to sustainable energy planning and supports decarbonization strategies aligned with global Net Zero and sustainable development goal (SDG) targets.
