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Tuning Electrocatalytic Properties of Bismuthene by Heteroatom Decoration Toward Overall Water Splitting

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ChemistrySelect 2025年第21期10卷

作者： Yue-Han Ji Jun-Miao Zhang Xiang-Yu Leng Liang-Ze Zhu Bei-Bei Xiao School of Energy and Power Engineering Jiangsu University of Science and Technology Zhenjiang Jiangsu 212003 China Jangho Architecture College Northeastern University Shenyang Liaoning Province 110167 China Department of Computer and Science Technology LyuLiang University LyuLiang Shanxi 033000 China

Developing of effective electrocatalysts for overall water splitting is of importance for the achievement of hydrogen energy cycling. In this work, hydrogen evolution and oxygen evolution of the defective bismuthene with decoration of a transition metal atom have been fully investigated via density functional theory calculations. The results indicate that the fixation of transition metal dopant into defective bismuthene is achieved via the interaction between the Bi sp band and TM sd band. Wherein, Sc@Bi, Ti@Bi, Zn@Bi, Y@Bi, Rh@Bi, Pd@Bi, Ag@Bi, Pt@Bi, and Au@Bi are thermodynamically stable according to the data of the formation energy. Furthermore, Sc@Bi, Ti@Bi, Y@Bi, Rh@Bi, Pd@Bi, and Pt@Bi have good performances toward hydrogen evolution, and the overpotentials η HER are 0.11, 0.22, 0.30, 0.13, 0.27, and 0.01 V, respectively. Furthermore, the optimal candidates toward oxygen evolution are Rh@Bi and Pt@Bi, endowed with the overpotentials η OER of 0.66 and 0.36 V, respectively. Therefore, Pt@Bi and Rh@Bi are bifunctional-active toward overall water splitting. These findings indicate that bismuthene could provide a new platform to explore high-efficiency electrocatalysts in overall water splitting.

关键词： Bismuthene Density functional theory Electrocatalysis Water splitting

Evaluating Large Language Model–Supported Instructions for Medication Use: First Steps Toward a Comprehensive Model

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Mayo Clinic Proceedings: Digital Health

Mayo Clinic Proceedings: Digital Health 2024年第4期2卷 632-644页

作者： Reis, Zilma Silveira Nogueira Pagano, Adriana Silvina Ramos de Oliveira, Isaias Jose Dias, Cristiane dos Santos Lage, Eura Martins Mineiro, Erico Franco Varella Pereira, Glaucia Miranda de Carvalho Gomes, Igor Basilio, Vinicius Araujo Cruz-Correia, Ricardo João de Jesus, Davi dos Reis de Souza Júnior, Antônio Pereira da Rocha, Leonardo Chaves Dutra Health Informatics Center Faculty of Medicine Universidade Federal de Minas Gerais Minas Gerais Belo Horizonte Brazil Arts Faculty Universidade Federal de Minas Gerais Minas Gerais Belo Horizonte Brazil Department of Pediatrics Faculty of Medicine Universidade Federal de Minas Gerais Minas Gerais Belo Horizonte Brazil Department of Design Technology School of Architecture Universidade Federal de Minas Gerais Minas Gerais Belo Horizonte Brazil Department of Obstetrics and Gynecology Faculty of Medical Sciences Universidade Estadual de Campinas São Paulo Campinas Brazil Departamento de Medicina da Comunidade Informação e Decisão em Saúde National Secretary of Primary Care of the Brazilian Ministry of Health Brasília Brazil MEDCIDS Porto University Porto Portugal Department of Computer Science Universidade Federal de São João Del Rei São João del Rei Minas Gerais Brazil

Objective: To assess the support of large language models (LLMs) in generating clearer and more personalized medication instructions to enhance e-prescription. Patients and Methods: We established patient-centered guidelines for adequate, acceptable, and personalized directions to enhance e-prescription. A dataset comprising 104 outpatient scenarios, with an array of medications, administration routes, and patient conditions, was developed following the Brazilian national e-prescribing standard. Three prompts were submitted to a closed-source LLM. The first prompt involved a generic command, the second one was calibrated for content enhancement and personalization, and the third one requested bias mitigation. The third prompt was submitted to an open-source LLM. Outputs were assessed using automated metrics and human evaluation. We conducted the study between March 1, 2024 and September 10, 2024. Results: Adequacy scores of our closed-source LLM's output showed the third prompt outperforming the first and second one. Full and partial acceptability was achieved in 94.3% of texts with the third prompt. Personalization was rated highly, especially with the second and third prompts. The 2 LLMs showed similar adequacy results. Lack of scientific evidence and factual errors were infrequent and unrelated to a particular prompt or LLM. The frequency of hallucinations was different for each LLM and concerned prescriptions issued upon symptom manifestation and medications requiring dosage adjustment or involving intermittent use. Gender bias was found in our closed-source LLM's output for the first and second prompts, with the third one being bias-free. The second LLM's output was bias-free. Conclusion: This study demonstrates the potential of LLM-supported generation to produce prescription directions and improve communication between health professionals and patients within the e-prescribing system. © 2024 The Authors

关键词： AI artificial intelligence EHR electronic health record ICPC-2 International Classification of Primary Care, 2nd edition LLM large language model e-SUS electronic Sistema Único de Saúde

CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

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CertainOdom: Uncertainty Weighted Multi-task Learning Model ...

IEEE International Conference on Robotics and Biomimetics

作者： Leyuan Sun Guanqun Ding Yusuke Yoshiyasu Fumio Kanehiro Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST). Digital Architecture Research Center (DARC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Computer Vision Research Team Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (纸本)9781665481106

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in robotics. In recent years, learning-based modeling approaches for odometry estimation have been validated to be feasible. However, it is necessary to consider security factors as the highest priorities when we apply the learning-based model to certain high-risk real-world scenarios, such as autonomous driving. The odometry uncertainty estimation provides more valuable information for downstream tasks, such as route planning and navigation. In this paper, we propose an end-to-end neural network (namely CertainOdom) to solve odometry and uncertainty estimation tasks by applying multi-task learning. Instead of using the manually-tuned hyper-parameters, we employ the learnable uncertainties to weigh the balance between the error of translation and orientation in the loss function. We evaluate the estimated trajectory and uncertainty on KITTI dataset. We also compare the robustness against the traditional geometry-based methods on our artificially degraded KITTI LiDAR dataset. Extensive experimental results show that our model with uncertainty weighted loss achieves competitive performance in LiDAR odometry estimation. We also explain our uncertainties qualitatively and quantitatively.

关键词： Uncertainty Laser radar Biological system modeling Estimation Multitasking Robustness Trajectory

Inverse learning of black-box aggregator for robust Nash equilibrium

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

Influence of surface albedo and vegetation fractional coverage on thermal environment of mining area

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Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of technology 2022年第12期54卷 117-126页

作者： Hou, Chunhua Li, Fuping He, Baojie Ma, Pengkun Song, Wen Zhao, Jingjing College of Mining Engineering North China University of Science and Technology Hebei Tangshan063210 China Department of Computer Science Tangshan Normal University Hebei Tangshan063000 China Faculty of Architecture and Urban Planning Chongqing University Chongqing400405 China Faculty of Built Environment University of New South Wales Sydney2052 Australia Institute of Urban Boundary Layer and Atmospheric Environment Institute of Urban Meteorology CMA Beijing100089 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China

The influence of the changes in surface albedo on the thermal environment of the mining area due to the destruction of the original vegetation caused by the high-intensity mining of mineral resources was analyzed. Taking Malanzhuang Iron Mine in Qian'an city of Hebei province as an example, the statistical model method was adopted to retrieve the surface albedo based on the visible light band of Landsat remote sensing image, and the pixel trisection model was used to retrieve the vegetation fractional coverage based on NDVI-DFI. By means of radiative transfer equation method, the land surface temperature (LST) was retrieved based on the thermal infrared band of Landsat remote sensing image. With the aid of superposition analysis method, correlation analysis method, and regression analysis method, the response of spatiotemporal heterogeneity of surface thermal environment to the changes in photosynthetic vegetation coverage (fPV) and surface albedo was quantitatively and visually explored. Results showed that the spatial distribution of surface thermal environment in the study area showed obvious heterogeneity and regularity. The high temperature zone was mainly distributed in the northern Shaheshan stope, central Liuheyu dump, and the unreclaimed Baimashan dump in the south. The moderate temperature zone was mainly located at the edge of the high temperature zone and other bare surfaces. The low temperature zone was mainly located in the northern reclaimed tailings pond and reclaimed waste dump, as well as the green land and water bodies in the area. The spatiotemporal variation of photosynthetic vegetation coverage and surface albedo resulted in a large heterogeneity of the surface thermal environment during the summer midday in the study area. The mean surface temperature of underlying surface in the third phase of the image was ranked as follows: mining area bare rock > dump slag > reclamation vegetation > water body. The regression analysis results showed that f

关键词： Vegetation

Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection

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arXiv 2024年

作者： Cichocki, Andrzej Cruces, Sergio Sarmiento, Auxiliadora Tanaka, Toshihisa Systems Research Institute of Polish Academy of Science Newelska 6 Warszawa01-447 Poland Global Innovation Research Institute of Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo Japan Riken AIP 103-0027 Nihonbashi Tokyo1 chome-4-1 Japan Departamento de Teoría de la Señal y Comunicaciones Universidad de Sevilla Camino de los Descubrimientos s/n Seville41092 Spain Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo Japan

This paper introduces a novel family of generalized exponentiated gradient (EG) updates derived from an Alpha-Beta divergence regularization function. Collectively referred to as EGAB, the proposed updates belong to the category of multiplicative gradient algorithms for positive data and demonstrate considerable flexibility by controlling iteration behavior and performance through three hyperparameters: α, β, and the learning rate η. To enforce a unit l1 norm constraint for nonnegative weight vectors within generalized EGAB algorithms, we develop two slightly distinct approaches. One method exploits scale-invariant loss functions, while the other relies on gradient projections onto the feasible domain. As an illustration of their applicability, we evaluate the proposed updates in addressing the online portfolio selection problem (OLPS) using gradient-based methods. Here, they not only offer a unified perspective on the search directions of various OLPS algorithms (including the standard exponentiated gradient and diverse mean-reversion strategies), but also facilitate smooth interpolation and extension of these updates due to the flexibility in hyperparameter selection. Simulation results confirm that the adaptability of these generalized gradient updates can effectively enhance the performance for some portfolios, particularly in scenarios involving transaction costs. © 2024, CC BY-NC-SA.

关键词： Iterative methods

Boolean reasoning-based biclustering for shifting pattern extraction

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arXiv 2021年

作者： Michalak, Marcin Aguilar-Ruiz, Jesús S. Department of Computer Networks and Systems Silesian University of Technology Ul. Akademicka 16 Gliwice44-100 Poland School of Engineering Pablo de Olavide University SevilleES-41013 Spain Computer Science Universidad Nacional de San Agustín Arequipa Peru

Biclustering is a powerful approach to search for patterns in data, as it can be driven by a function that measures the quality of diverse types of patterns of interest. However, due to its computational complexity, the exploration of the search space is usually guided by an algorithmic strategy, sometimes introducing random factors that simplify the computational cost (e.g. greedy search or evolutionary computation). Shifting patterns are specially interesting as they account constant fluctuations in data, i.e. they capture situations in which all the values in the pattern move up or down for one dimension maintaining the range amplitude for all the dimensions. This behaviour is very common in nature, e.g. in the analysis of gene expression data, where a subset of genes might go up or down for a subset of patients or experimental conditions, identifying functionally coherent categories. Boolean reasoning was recently revealed as an appropriate methodology to address the search for constant biclusters. In this work, this direction is extended to search for more general biclusters that include shifting patterns. The mathematical foundations are described in order to associate Boolean concepts with shifting patterns, and the methodology is presented to show that the induction of shifting patterns by means of Boolean reasoning is due to the ability of finding all inclusion-maximal shifting patterns. Experiments with a real dataset show the potential of our approach at finding biclusters with shifting patterns, which have been evaluated with the mean squared residue (MSR), providing an excellent performance at finding results very close to *** Codes 94D10 Copyright © 2021, The Authors. All rights reserved.

关键词： Gene expression

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

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arXiv 2023年

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Transportation Systems Engineering Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023, CC BY-NC-SA.

关键词： Travel time

Global solution to sensor network localization: A non-convex potential game approach and its distributed implementation

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor nodes and relative measurements among all nodes, determine the locations of all non-anchor nodes. The solution to the SNL problem is challenging due to its inherent non-convexity. In this paper, the problem takes on the form of a multi-player nonconvex potential game in which canonical duality theory is used to define a complementary dual potential function. After showing the Nash equilibrium (NE) correspondent to the SNL solution, we provide a necessary and sufficient condition for a stationary point to coincide with the NE. An algorithm is proposed_ to reach the NE and shown to have convergence rate O(1/√k). With the aim of reducing the information exchange within a network, a distributed algorithm for NE seeking is implemented and its global convergence analysis is provided. Extensive simulations show the validity and effectiveness of the proposed approach to solve the SNL problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory