ChatGPT in transforming communication in seismic engineering: Case studies, implications, key challenges and future directions

Seismic engineering,a critical field with significant societal implications,often presents communication challenges due to the complexity of its concepts.This paper explores the role of Artificial Intelligence(AI),specifically OpenAI’s ChatGPT,in bridging these communication gaps.The study delves into how AI can simplify intricate seismic engineering terminologies and concepts,fostering enhanced understanding among students,professionals,and policymakers.It also presents several intuitive case studies to demonstrate the practical application of ChatGPT in seismic engineering.Further,the study contemplates the potential implications of AI,highlighting its potential to transform decision-making processes,augment education,and increase public engagement.While acknowledging the promising future of AI in seismic engineering,the study also considers the inherent challenges and limitations,including data privacy and potential oversimplification of content.It advocates for the collaborative efforts of AI researchers and seismic experts in overcoming these obstacles and enhancing the utility of AI in the field.This exploration provides an insightful perspective on the future of seismic engineering,which could be closely intertwined with the evolution of AI.

Generative AI:A transformative force in advancing research and care in metabolic dysfunction-associated fatty liver disease

Dear Editor,The advent of generative artificial intelligence(Al)has ushered in a new era of innovation across various domains,with healthcare being a prominent beneficiary.Specifically,the field of metabolic dysfunction-associated fatty liver disease(MAFLD)stands to benefit greatly from the applications of generative AI.1 This corre-spondence aims to explore the potential impact of generative Al on research,diagnosis,and patient care in MAFLD,highlighting its promise in revolutionizing our understanding and management of this complex disease.

Large language models in laparoscopic surgery: A transformative opportunity

This opinion paper explores the transformative potential of large language models(LLMs)in laparoscopic surgery and argues for their integration to enhance surgical education,decision support,reporting,and patient care.LLMs can revolutionize surgical education by providing personalized learning experiences and accelerating skill acquisition.Intelligent decision support systems powered by LLMs can assist surgeons in making complex decisions,optimizing surgical workflows,and improving patient outcomes.Moreover,LLMs can automate surgical reporting and generate personalized patient education materials,streamlining documentation and improving patient engagement.However,challenges such as data scarcity,surgical semantic capture,real-time inference,and integration with existing systems need to be addressed for successful LLM integration.The future of laparoscopic surgery lies in the seamless integration of LLMs,enabling autonomous robotic surgery,predictive surgical planning,intraoperative decision support,virtual surgical assistants,and continuous learning.By harnessing the power of LLMs,laparoscopic surgery can be transformed,empowering surgeons and ultimately benefiting patients.

Synthesis of Nanocrystalline FeS_2 with Increased Band Gap for Solar Energy Harvesting

In this paper,we have reported the synthesis of FeS2 of higher band gap energy（2.75 eV） by using capping reagent and its successive application in organic-inorganic based hybrid solar cells.Hydrothermal route was adopted for preparing iron pyrite（FeS2） nanoparticles with capping reagent PEG-400.The quality of synthesized FeS2 material was confirmed by X-ray diffraction,field emission scanning electron microscopy,transmission electron microscopy,Fourier transform infrared,thermogravimetric analyzer,and Raman study.The optical band gap energy and electro-chemical band gap energy of the synthesized FeS2 were investigated by UV-vis spectrophotometry and cyclic voltammetry.Finally band gap engineered FeS2 has been successfully used in conjunction with conjugated polymer MEHPPV for harvesting solar energy.The energy conversion efficiency was obtained as 0.064%with a fill-factor of 0.52.