In this work, we investigate the use of reconfigurable intelligent surface (RIS) partitioning and transmit power allocation for terahertz (THz) enabled spectrum-sharing underlay device-to-device (D2D) communication, assuming that instantaneous channel state information (CSI) for co-channel interference (CCI) links is unavailable. We first derive closed-form expressions for the ergodic rate (ER) based on statistical CSI using the moment matching technique and Gaussian–Chebyshev quadrature approximation, while accounting for generalized fading and pointing errors. We then develop a low-complexity particle swarm optimization (PSO) algorithm based solution to jointly partition the RIS and allocate transmit powers, aiming to maximize the ER of the D2D user. Analytical findings are validated via extensive Monte Carlo simulations, which provide key system design insights and performance comparisons with both non-RIS systems and benchmark optimization schemes.

In this paper, we investigate a full-duplex underlay device-to-device (D2D) communication system, where multiple D2D pairs exchange information with the assistance of a single reconfigurable intelligent surface (RIS). We begin by deriving analytical expressions for the outage probability of the users, and subsequently for ergodic rate. The analysis is carried out over a generalized Nakagami-m fading channel and incorporates several practical impairments, including residual self-interference (RSI), hardware imperfections (HI), and co-channel interference (CCI) from both cellular and D2D users. Assuming only statistical channel state information (CSI) is available for the CCI links, we formulate a joint optimization problem for D2D transmit power allocation and RIS partitioning, with the objective of maximizing the ergodic sum rate of the network. This optimization problem is efficiently solved using the particle swarm optimization (PSO) algorithm. Simulation results validate the effectiveness of the proposed system, demonstrating superior performance over conventional half-duplex systems and highlighting the advantages of the proposed optimization framework compared to benchmark schemes.

This paper explores a full-duplex underlay D2D communication system, where multiple D2D pairs exchange data with the help of a single RIS through RIS partitioning. Closed-form expressions for outage probability and ergodic rate are derived over generalized Nakagami-m fading, considering practical impairments such as residual self-interference, hardware imperfections, and co-channel interference (CCI). Assuming only statistical channel state information (CSI) availability for the CCI links, we formulate and solve a joint optimization problem for transmit power allocation and RIS partitioning using particle swarm optimization. Simulation results show significant performance gains over half-duplex systems and comparative benchmarks schemes.

Recent research has begun to examine the potential of automatically finding and fixing accessibility issues that manifest in software. However, while recent work makes important progress, it has generally been skewed toward identifying issues that affect users with certain disabilities, such as those with visual or hearing impairments. However there are other groups of users with different types of disabilities that also need software tooling support to improve their experience. As such, this paper aims to automatically identify accessibility issues that affect users with motor-impairments.

Backscatter communication and non-orthogonal multiple access (NOMA) represent emerging paradigms in wireless communication, offering promising solutions to boost spectral and energy efficiency for energy-constrained devices in 6G networks. This work introduces a tailored symbiotic radio system designed for Internet of Things (IoT) devices, proposing an innovative framework that integrates backscatter-based IoT devices into power-domain NOMA networks. In this system, the primary base station employs NOMA principles to serve both far and near users simultaneously, while IoT devices equipped with backscattering technology transmit their data over the primary signal. The IoT transmitter not only serves the IoT receiver but also enhances the performance of the far user. The paper thoroughly examines outage probabilities for both NOMA and IoT networks. It delves explicitly into the outage probabilities of IoT receivers, considering both perfect and imperfect direct link interference cancellation. The results demonstrate significant performance improvements over traditional orthogonal multiple-access methods. Furthermore, Monte Carlo simulations are employed to validate the accuracy of the theoretical analysis.

The interaction between proteins and small molecules in the context of drug discovery presents a complex and challenging task. Key factors involved in this process include shape complementarity and intermolecular interactions, which are heavily influenced by the binding site and the ligand’s orientation as it engages with the protein. State of the art methods in the field offer a range of ligand poses that may fit a specific receptor, but these approaches are often computationally intensive and costly. In this study, we have designed a method that provides a single optimized ligand pose for a specific receptor. As a pioneering study, PandoraRLO uses reinforcement learning (RL) to train on a large dataset, through exposure to a wide range of protein-ligand pairs, the agent becomes proficient in comprehending the underlying biochemistry and generating an optimized pose for specific interactions. The novelty of PandoraRLO lies in its strategic use of reinforcement learning (RL) to effectively model chemical diversity, skillfully navigating the "explore versus exploit" dilemma inherent to RL. This pioneering technique provides an enhanced understanding of the underlying biochemistry, facilitating precise prediction of optimal ligand poses. Consequently, it paves the way for new opportunities in drug discovery by enabling more accurate and efficient design and optimization of small molecules to specific target proteins. These findings underscore the potential of reinforcement learning in advancing understanding of protein-ligand interactions and its application in developing more accurate computational tools for drug discovery. The ability to predict optimized ligand poses with greater precision opens new avenues for designing and optimizing small molecules to target specific proteins, ultimately expediting the discovery and development of novel therapeutic interventions.

The ability to predict the correct ligand binding pose for protein-ligand complex is vital for drug design. Recently several machine learning methods have suggested knowledge based scoring functions for binding energy prediction. In this study, we propose a reinforcement learning (RL) based model, PandoraRL, where the RL agent helps the ligand traverse to the optimal binding pose. The underlying representation of molecules utilizes generalized graph convolution to represent the protein ligand complex with various atomic and spatial features. The representation consists of edges formed on the basis of inter molecular interactions such as hydrogen bonds, hydrophobic interactions, etc, and nodes representing atomic features. This study presents our initial model which can train on a protein-ligand pair and predict optimal binding pose for a different ligand with the same protein. To the best of our knowledge, this is the first time an RL based approach has been put forward for predicting optimized ligand pose.

Spectrally-efficient full-duplex (FD) non-orthogonal multiple access (NOMA) communications is a promising technology for future wireless communications that superimposes users in the power domain and allows them to simultaneously transmit and receive information from the base station (BS). However, self-interference cancellation in FD is still a major challenge, and thus the concept of virtual full duplex relaying (VFD) has evolved. In this work, without loss of generality, we consider a decode-and-forward (DF) relaying based NOMA transmission with a BS, two near users, and a far user constituting VFD over Nakagami-m fading channels. Framework for the exact and asymptotic outage probability (OP) and ergodic rate (ER) expressions is developed for the considered system model. Further, the impact of fading parameters, along with pathloss, is analyzed for the first time. Accuracy of the derived expressions is verified through rigorous simulations.