ChatGPT (Generative Pretrained Transformer) is a chatbot using artificial intelligence (AI) launched by OpenAI, which is an AI research and deployment company. The ChatGPT has taken the technology world by storm. The ChatGPT is a trained AI model that can chat almost like a human. The dialog format allows the ChatGPT to answer follow-up questions, admit mistakes, challenge incorrect premises, and reject inappropriate requests. The ChatGPT can be utilized for compiling research, drafting marketing content, brainstorming ideas, delivering aftercare services, increasing customer engagement, and many others. The ChatGPT can provide enormous opportunities for companies leveraging this breakthrough technology strategically. Thus, we evaluate ChatGPT as a tool in common business decision-making cases in the current study. For example, the ChatGPT was asked about the impacts of a hypothetical merging of two supermarket chains in Sweden. In another example, the ChatGPT was asked about recommendations for investment in a Brazilian oil company. Finally, it was asked about the factors that influence online shopping behavior. The results are significant and demonstrate the tremendous potential of the ChatGPT in revolutionizing the corporate world.

Sensors for detecting wear particles in fluid lubricants have been widely studied owing to their ability to diagnose mechanical machine conditions and to help in preventing mechanical failures caused by wear particles or contamination particles. The proposed novelty sensor was fabricated using photolithography to make the complementary split ring resonator (CSSR) on PCB and it utilized neodymium magnets at the center of the CSRR structure to attract ferrous wear particles. The proposed sensor was tested, and we found that it is highly sensitive and could detect 50 ppm of ferrous wear particles larger than 10 μm dissolved in mineral white oil ISO VG 15. The sensor of this article has a low-cost and can be used in different situations to measure the ferrous wear particles while monitoring the real-time mechanical equipment wear, as it can be easily integrated into them. The sensor offers good accuracy, and is insensitive to fluid transparency and bubble formation, unlike other sensors for detection of wear particles. Therefore, the proposed novelty sensor of this work is in line with Industry 4.0, which requires many low-cost sensors for continuous monitoring to apply predictive maintenance techniques for extending the lifespan of infrastructures.

The printed circuit board (PCB) plays a critical role in any electronic product, and its manufacturing quality assurance is responsible for substantially impacting the final product’s price. Hence, research and development (R&D) for better inspection methods have been an internationally growing subject. Regarding the multiple PCB inspection paradigms, the visual investigation of these boards represents the majority of modern techniques. Thus, the present survey collects the most impacting studies over the last 25 years, highlighting their operation strategies and how they evolved until recently, including introducing artificial intelligence (AI) approaches to increase their overall performance and lower operating costs. Furthermore, this work calls attention to the importance of public PCB datasets to contribute to developing such methods and provide benchmarking references. Finally, the most recent challenges in the field are described and discussed.

This study introduces a sophisticated neural network structure for segmenting breast tumors. It achieves this by combining a pretrained Vision Transformer (ViT) model with a UNet framework. The UNet architecture, commonly employed for biomedical image segmentation, is further enhanced with depthwise separable convolutional blocks to decrease computational complexity and parameter count, resulting in better efficiency and less overfitting. The ViT, renowned for its robust feature extraction capabilities utilizing self-attention processes, efficiently captures the overall context within images, surpassing the performance of conventional convolutional networks. By using a pretrained ViT as the encoder in our UNet model, we take advantage of its extensive feature representations acquired from extensive datasets, resulting in a major enhancement in the model’s ability to generalize and train efficiently. The suggested model has exceptional performance in segmenting breast cancers from medical images, highlighting the advantages of integrating transformer-based encoders with efficient UNet topologies. This hybrid methodology emphasizes the capabilities of transformers in the field of medical image processing and establishes a new standard for accuracy and efficiency in activities related to tumor segmentation.