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Two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, possess unique physical and chemical properties, enabling diverse applications in fields ranging from energy storage to communication, catalysis, sensing, healthcare, and beyond. The transition metal and nonmetallic atoms in MXenes can exhibit distinct coordination environments, potentially leading to a wide variety of 2D phases. Despite extensive research and significant advancements, a fundamental understanding of MXenes' phase diversity and its relationship with their hierarchical precursors, including intermediate MAX phases and parent bulk phases, remains limited. Using high-throughput modeling based on first-principles density functional theory, we unveil a wide range of MXenes and comprehensively evaluate their relative stabilities across a large chemical space. The key lies in considering both octahedral and trigonal prismatic coordination environments characteristic of various bulk phases. Through this comprehensive structural library of MXenes, we uncover a close alignment between the phase stability of MXenes and that of their hierarchical 3D counterparts. Building on this, we demonstrate a new design strategy where the atomic coordination environments in parent bulk phases can serve as reliable predictors for the design of MXenes, reducing reliance on intermediate MAX phases. Our study significantly expands the landscape of MXenes, at least doubling the number of possible structures., Comment: 17 pages, 5 illustrations (3 figures and 2 tables)

This paper proposes a framework that has the ability to animate and generate different scenarios for the mobility of a movable anchor which can follow various paths in wireless sensor networks (WSNs). When the researchers use NS-2 to simulate a single anchor-assisted localization model, they face the problem of creating the movement file of the movable anchor. The proposed framework solved this problem by allowing them to create the movement scenario regarding different trajectories. The proposed framework lets the researcher set the needed parameters for simulating various static path models, which can be displayed through the graphical user interface. The researcher can also view the mobility of the movable anchor with control of its speed and communication range. The proposed framework has been validated by comparing its results to NS-2 outputs plus comparing it against existing tools. Finally, this framework has been published on the Code Project website and downloaded by many users.

Two-dimensional transition metal carbides (MXenes), especially their few-layered nanosheets, have triggered burgeoning research attentions owing to their superiorities including extraordinary conductivity, accessible active surface, and adjustable processability. Molten salts etching route further achieves their controllable surface chemistry. However, the method encounters challenges in achieving few-layer structures due to more complex delamination behaviors. Herein, we present an efficient strategy to fabricate Cl- or Br-terminated MXene nanoflakes with few-layers, achieved by electrochemical intercalation of Li ions and concomitant solvent molecules in the electrolyte solution, with gaseous scissors (propylene molecules) to break up interlayer forces. By controlling cut-off voltages, the optimal protocol results in nanosheets with an ultrahigh yield (~93%) and preserved surface chemistry. The resultant MXenes dispersions were employed as lubricants to enhance tribovoltaic nanogenerators, where Ti3C2Br2 displayed superior electrical output. These findings facilitate the understanding of MXenes' intrinsic physical properties and enable the nanoengineering of advanced electronic devices.

Prompt engineering is crucial for harnessing the potential of large language models (LLMs), especially in the medical domain where specialized terminology and phrasing is used. However, the efficacy of prompt engineering in the medical domain remains to be explored. In this work, 114 recent studies (2022-2024) applying prompt engineering in medicine, covering prompt learning (PL), prompt tuning (PT), and prompt design (PD) are reviewed. PD is the most prevalent (78 articles). In 12 papers, PD, PL, and PT terms were used interchangeably. ChatGPT is the most commonly used LLM, with seven papers using it for processing sensitive clinical data. Chain-of-Thought emerges as the most common prompt engineering technique. While PL and PT articles typically provide a baseline for evaluating prompt-based approaches, 64% of PD studies lack non-prompt-related baselines. We provide tables and figures summarizing existing work, and reporting recommendations to guide future research contributions.

On March 11, 2020, the World Health Organization declared COVID-19 a global pandemic, resulting in disruptions to education at an unprecedented scale. In response to the urgent need to recover learning losses, countries worldwide have taken RAPID actions to: Reach every child and keep them in school; Assess learning levels regularly; Prioritize teaching the fundamentals; Increase the efficiency of instruction; and Develop psychosocial health and wellbeing. Marking three years since the onset of the pandemic, this report looks back at policy measures taken during school closures and reopening based on country survey data, initiatives implemented by countries and regions to recover and accelerate learning, and their emerging lessons within each RAPID action. With schools now reopened worldwide, this report also looks ahead to longer-term education transformation, offering policy recommendations to build more resilient, effective and equitable education systems.

Large language models (LLMs) have become the preferred solution for many natural language processing tasks. In low-resource environments such as specialized domains, their few-shot capabilities are expected to deliver high performance. Named Entity Recognition (NER) is a critical task in information extraction that is not covered in recent LLM benchmarks. There is a need for better understanding the performance of LLMs for NER in a variety of settings including languages other than English. This study aims to evaluate generative LLMs, employed through prompt engineering, for few-shot clinical NER. %from the perspective of F1 performance and environmental impact. We compare 13 auto-regressive models using prompting and 16 masked models using fine-tuning on 14 NER datasets covering English, French and Spanish. While prompt-based auto-regressive models achieve competitive F1 for general NER, they are outperformed within the clinical domain by lighter biLSTM-CRF taggers based on masked models. Additionally, masked models exhibit lower environmental impact compared to auto-regressive models. Findings are consistent across the three languages studied, which suggests that LLM prompting is not yet suited for NER production in the clinical domain., Comment: Accepted to EMNLP Findings, 2024