Phytochemical and cytotoxicity investigations on organic solvent extracts of the aerial parts of Tinospora crispa have led to the isolation of 15 cis-clerodane-type furanoditerpenoids. Of these, nine compounds (1-9) were found to be new. Spectroscopic assignments of a previously reported compound, borapetoside A (13), were revised on the basis of HMQC and HMBC correlations. No discernible activity was observed when compounds 10-13 were subjected to evaluation in cytotoxicity assays against human prostate cancer (PC-3) and the normal mouse fibroblast (3T3) cell lines.
The Internet of Vehicles (IoV) transforms the automobile industry through connected vehicles with communication infrastructure that improves traffic control, safety and information, and entertainment services. However, some issues remain, like data protection, privacy, compatibility with other protocols and systems, and the availability of stable and continuous connections. Specific problems are related to energy consumption for transmitting information, distributing energy loads across the vehicle's sensors and communication units, and designing energy-efficient approaches to processing received data and making decisions in the context of the IoV environment. In the realm of IoV, we propose OptiE2ERL, an advanced Reinforcement Learning (RL) based model designed to optimize energy efficiency and routing. Our model leverages a reward matrix and the Bellman equation to determine the optimal path from source to destination, effectively managing communication overhead. The model considers critical parameters such as Remaining Energy Level (REL), Bandwidth and Interference Level (BIL), Mobility Pattern (MP), Traffic Condition (TC), and Network Topological Arrangement (NTA), ensuring a comprehensive approach to route optimization. Extensive simulations were conducted using NS2 and Python, demonstrating that OptiE2ERL significantly outperforms existing models like LEACH, PEGASIS, and EER-RL across various performance metrics. Specifically, our model extends the network lifetime, delays the occurrence of the first dead node, and maintains a higher residual energy rate. Furthermore, OptiE2ERL enhances network scalability and robustness, making it a superior choice for IoV applications. The simulation results highlight the effectiveness of our model in achieving energy-efficient routing while maintaining network performance under different scenarios. By incorporating a diverse set of parameters and utilizing RL techniques, OptiE2ERL provides a robust solution for the challenges faced in IoV networks. This research contributes to the field by presenting a model that optimizes energy consumption and ensures reliable and efficient communication in dynamic vehicular environments.
Bioassay-guided extraction of the stem bark of Knema laurina showed the acetylcholinesterase (AChE) inhibitory activity of DCM and hexane fractions. Further repeated column chromatography of hexane and DCM fractions resulted in the isolation and purification of five alkenyl phenol and salicylic acid derivatives. New compounds, (+)-2-hydroxy-6-(10'-hydroxypentadec-8'(E)-enyl)benzoic acid (1) and 3-pentadec-10'(Z)-enylphenol (2), along with known 3-heptadec-10'(Z)-enylphenol (3), 2-hydroxy-6-(pentadec-10'(Z)-enyl)benzoic acid (4), and 2-hydroxy-6-(10'(Z)-heptadecenyl)benzoic acid (5) were isolated from the stem bark of this plant. Compounds (1-5) were tested for their acetylcholinesterase inhibitory activity. The structures of these compounds were elucidated by the 1D and 2D NMR spectroscopy, mass spectrometry and chemical derivatizations. Compound 5 showed strong acetylcholinesterase inhibitory activity with IC(50) of 0.573 ± 0.0260 μM. Docking studies of compound 5 indicated that the phenolic compound with an elongated side chain could possibly penetrate deep into the active site of the enzyme and arrange itself through π-π interaction, H-bonding, and hydrophobic contacts with some critical residues along the complex geometry of the active gorge.