AI-Based Positioning with Input Parameter Optimization in Indoor VLC Environments

Indoorlocation-based service (LBS) technology has been emerged as a major research topic in recent years. Positioning technology is essential for providing LBSs. The existing indoor positioning solutions generally use radio-frequency (RF)-based communication technologies such as Wi-Fi. However, RF-based communication technologies do not provide precise positioning owing to rapid changes in the received signal strength due to walls, obstacles, and people movement in indoor environments. Hence, this study adopts visible-light communication (VLC) for user positioning in an indoor environment. VLC is based on light-emitting diodes (LEDs) and its advantage includes high efficiency and long lifespan. In addition, this study uses a deep neural network (DNN) to improve the positioning accuracy and reduce the positioning processing time. The hyperparameters of the DNN model are optimized to improve the positioning performance. The trained DNN model is designed to yield the actual three-dimensional position of a user. The simulation results show that our optimized DNN model achieves a positioning error of 0.0898 m with a processing time of 0.5 ms, which means that the proposed method yields more precise positioning than the other methods.

Revolutionising Neurological Therapeutics: Investigating Drug Repurposing Strategies

Repurposing drugs (DR) has become a viable approach to hasten the search for cures for neurodegenerative diseases (NDs). This review examines different off-target and on-target drug discovery techniques and how they might be used to find possible treatments for non-diagnostic depressions. Off-target strategies look at the known or unknown side effects of currently approved drugs for repositioning, whereas on-target strategies connect disease pathways to targets that can be treated with drugs. The review highlights the potential of experimental and computational methodologies, such as machine learning, proteomic techniques, network and genomics-based approaches, and in silico screening, in uncovering new drug-disease correlations. It also looks at difficulties and failed attempts at drug repurposing for NDs, highlighting the necessity of exact and standardised procedures to increase success rates. This review's objectives are to address the purpose of drug repurposing in human disorders, particularly neurological diseases, and to provide an overview of repurposing candidates that are presently undergoing clinical trials for neurological conditions, along with any possible causes and early findings. We then include a list of drug repurposing strategies, restrictions, and difficulties for upcoming research.

Strategic partnerships for AI-driven drug discovery: The role of relational dynamics

Artificial intelligence-driven drug discovery (AIDD) companies hold significant promise for transforming pharmaceutical development, yet little is known about how they manage partnerships with established pharmaceutical firms. To address this research gap, our study explores how AIDD companies develop and leverage relational capabilities to enhance collaboration effectiveness. Through a case study approach, we focus on four key relational aspects: identifying complementary capabilities, establishing effective governance mechanisms, creating relationship-specific assets, and developing interfirm knowledge-sharing routines. Our findings demonstrate that particularly effective governance of intellectual property is essential for partnership success. We offer actionable recommendations for AIDD companies to strengthen collaborations, thereby contributing to the realization of AI's potential in drug discovery.