Recently, the fifth generation of cellular mobile communications (5G) network has been deployed and become pervasive. 5G offers a significant increase in terms of bandwidth and data rate compared to the previous generations. In addition, new technologies such as millimeter-wave (mmWave) technology and massive MIMO (mMIMO), have been proposed to meet the demand. However, some inevitable challenges still exist. In mmWave frequency, path loss and shadowing become more severe due to the radio electromagnetic (EM) wave characteristics. In this paper, we propose the utilization of reconfigurable intelligent surface (RIS) to aid wireless communications to overcome path loss and shadowing issues, by using a compressive sensing-based adaptive beamforming algorithm. To validate the theory, hypothesis, and simulation results, we have designed, fabricated, and conducted experiments with a 1-bit RIS testbed. The results show that the bit error rate (BER) and signal-to-noise ratio (SNR) of the received signal are significantly improved when the proposed RIS is employed. Further, we have also demonstrated a video streaming application aided by the proposed RIS as one of the potential RIS deployment scenarios.

ChatGPT has gained attention worldwide. In the medical education field, ChatGPT, or any similar large language model, provides a convenient way for students to access information and practice their skills. ChatGPT can simulate patient interactions, allowing students to practice their diagnostic and communication skills in a safe and controlled environment. ChatGPT can also answer questions and provide explanations for complex medical concepts. There have been attempts involving ChatGPT to assist researchers in writing journal articles. Due to its capabilities, ChatGPT has the potential to be abused and sparking ethical concerns. Unwise researchers can now employ ChatGPT to write academic articles. Similarly, irresponsible students, might utilize ChatGPT to cheat during exams. We investigated whether ChatGPT, in its current state, can answer Indonesian medical doctor examination problems. Among the 3 problems that we have picked, ChatGPT can only correctly answer 1 question. We also examine whether ChatGPT-generated abstracts can fool professionals and educators. We have brought 6 abstracts, 3 of which are taken from actual published papers, while the other 3 were generated by ChatGPT. We recruited 12 participants with either a medical doctor (M.D.), a doctorate (Ph.D.), or an M.D., Ph.D. degree from various institutions in Indonesia. Surprisingly, 4 of the participants couldn't guess a single abstract correctly, 6 could only identify 1 abstract accurately, one correctly guessed 2 abstracts, and one correctly identified 3 abstracts. Therefore, it is safe to say that ChatGPT, in its current state, has been able to fool professionals and educators.

We propose and implement a novel 1-bit coding metasurface that is capable of focusing and steering beam for enhancing power transfer efficiency of the electromagnetic (EM) wave-based wireless power transfer systems. The proposed metasurface comprises 16 × 16 unit cells which are designed with a fractal structure and the operating frequency of 5.8 GHz. One PIN diode is incorporated within each unit cell and enables two states with ${180}^{°}$ phase change of the reflected signal at the unit cell. The two states of the unit cell correspond to the ON and OFF states of the PIN diode or “0” and “1” coding in the metasurface. By appropriately handling the ON/OFF states of the coding metasurface, we can control the reflected EM wave impinged on the metasurface. To verify the working ability of the coding metasurface, a prototype metasurface with a control board has been fabricated and measured. The results showed that the coding metasurface is capable of focusing beam to desired direction. For practical scenarios, we propose an adaptive optimal phase control scheme for focusing the beam to a mobile target. Furthermore, we prove that the proposed adaptive optimal phase control scheme outperforms the random phase control and beam synthesis schemes.

The future of disease detection, treatment, and prevention may very well lie in data-driven healthcare. Here, we take stock of where things stand and highlight certain emerging issues and long-standing difficulties. We looked at all the research that has been published on the topic of data-driven healthcare decision-making. Our research shows that the use of data in healthcare has already improved patient care and results. However, there are substantial obstacles that must be overcome, such as the need to more effectively integrate data from multiple sources, as well as issues with data quality, privacy, and security. Furthermore, healthcare data use raises significant ethical concerns. We examine what these results mean for the future of data-driven healthcare and offer suggestions on where future research should focus. We conclude that data-driven healthcare has the potential to transform healthcare delivery and enhance patient outcomes, but that the inherent difficulties and dangers of this approach must be carefully considered.

Artificial intelligence (AI) has shown promising potential to transform various fields, including medical education. Recently, the rapid advancement of AI has led to many ``new'' discoveries that caught everyone's attention. Among those discoveries are introduced by OpenAI (e.g., ChatGPT, Dall-E, and the most recent, GPT-4). The integration of AI tools, such as ChatGPT and Dall-E, can offer a new dimension to medical education by creating an interactive and engaging learning experience. In this article, we explore the potential benefits of ChatGPT and Dall-E in medical education and provide practical utilization examples of those tools. For starters, ChatGPT, or in this sense, any other similar large language models, can simulate patient interactions in a safe environment, allowing medical learners to practice their communication skills and diagnosis techniques. Furthermore, it can assist medical students and researchers in reading and writing academic articles by accurately summarizing the key points of a given topic and generating an indistinguishable In addition, ChatGPT can also create problems for medical assignments and exam practice. In this article, we also discussed ChatGPT's capability to answer standard medical assignment problems. Dall-E, on the other hand, can generate dummy copyright-free and consent-free medical images (e.g., x-ray and electrocardiogram (ECG) graphs), allowing medical learners to practice and enhance their interpretation skills. Incorporating AI-based tools into medical education can provide a new approach to teaching and learning, bridging the gap between theory and practice, and unlocking new avenues for learning and discoveries for both, the students and the instructors. It can also offer a cost-effective solution to simulate real-world scenarios that would otherwise require significant resources and time. In summary, this article concludes that AI-based tools have the potential to revolutionize medical education, empowering medical learners with the skills and knowledge necessary to excel in their field.

This article proposes a sparsity-aware channel estimation scheme for reconfigurable intelligent surface (RIS)-assisted wireless communications. We present an angular domain-channel sparsity model in a closed-form mathematical expression. A comprehensive formulation of the RIS channel estimation problem based on the sparsity analysis and RIS reflection model is also presented in this manuscript. This work aims to achieve a practical RIS beamforming algorithm without requiring excessive training overhead or any sensor deployment. We achieve the goal by proposing two sparsity-aware RIS channel estimation schemes based on the compressive sensing (CS) algorithms such as the Dantzig selector (DS) and orthogonal matching pursuit (OMP). Differently from the existing works on the CS algorithms for RIS, we consider a fully passive RIS without any active sensor. We validate the theory and algorithm through both simulations and experiments. We have experimented orthogonal frequency division multiplexing (OFDM) communications on our 5.8 GHz 1-bit RIS testbed with QPSK, 16QAM, 64QAM, and 256QAM modulation schemes. By experiments, it is shown that the proposed scheme is able to adaptively form a beam toward the receiver and improves the quality of the wireless communication to a notable level. Thanks to the properties of the proposed scheme, the wireless channel can be estimated without excessive training time and complexity.