Publications by authors named "S H Moosavi"
Introduction: Standing balance is essential for physical functioning. Therefore, improving balance control is a key priority in the management of knee osteoarthritis (OA), underscoring the importance of accurately assessing standing balance.
Purpose: To assess reliability, construct validity and responsiveness of common clinical balance tests, including Step Test, Single-Leg Stance Test, and Functional Reach Test, in patients with knee OA.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is the causative agent of the emerging zoonotic respiratory disease. One of the most important prerequisites for combating emerging diseases is the development of vaccines within a short period of time. In this study, antigen-irradiated, inactivated SARS-CoV-2 viruses and the disaccharide trehalose were used to enhance immune responses in the Syrian hamster.
View Article and Find Full Text PDFSparse coding enables cortical populations to represent sensory inputs efficiently, yet its temporal dynamics remain poorly understood. Consistent with theoretical predictions, we show that stimulus onset triggers broad cortical activation, initially reducing sparseness and increasing mutual information. Subsequently, competitive interactions sustain mutual information as activity declines and sparseness increases.
View Article and Find Full Text PDFWith the development of smart buildings, the risks of cyber-attacks against them have also increased. One of the popular and evolving protocols used for communication between devices in smart buildings, especially HVAC systems, is the BACnet protocol. Machine learning algorithms and neural networks require datasets of normal traffic and real attacks to develop intrusion detection (IDS) and prevention (IPS) systems that can detect anomalies and prevent attacks.
View Article and Find Full Text PDFArticle Synopsis
- Large language models (LLMs) like GPT-J-6B, Llama-3.1-8B, and Mistral-7B can learn chemical properties effectively through fine-tuning without specialized features.
- Fine-tuning these models often outperforms traditional machine learning methods in simple classification tasks, with potential success in more complex problems depending on dataset size and question type.
- The ease of converting datasets for LLM training and the effectiveness of small datasets in generating predictive models suggest that LLMs could significantly streamline experimental processes in chemical research.