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DiaBD: A Diabetes Dataset for Enhanced Risk Analysis and research in Bangladesh

Data in Brief 2025年

作者： Tabia Tanzin Prama Md. Jobayer Rahman Marzia Zaman Farhana Sarker Khondaker A. Mamun Advanced Intelligent Multidisciplinary Systems (AIMS) Lab Institute of Research Innovation Incubation and Commercialization (IRIIC) United International University Dhaka 1212 Bangladesh CMED Health Ltd. Mohakhali DOHS Dhaka 1206 Bangladesh Department of Computer Science and Engineering Independent University Bangladesh (IUB) Dhaka 1229 Bangladesh Department of Computer Science and Engineering United International University (UIU) Dhaka 1212 Bangladesh

Diabetes is a chronic condition affecting millions worldwide and severely impacts health and quality of life. According to the International Diabetes Federation (IDF), over 463 million adults, which is 9.3% of the global population, live with diabetes. Diabetes ranks among the most prevalent chronic diseases and was the ninth-leading cause of mortality in 2019, with 4.2 million deaths reported. This article presents a dataset of 5,288 patient records from Bangladesh, addressing gaps in diabetes research and aiding in healthcare planning, risk analysis, and predictive modelling. The dataset comprises 14 attributes: age, gender, pulse rate, systolic and diastolic blood pressure, glucose levels, height, weight, body mass index (BMI), family history of diabetes and hypertension, cardiovascular disease (CVD), and stroke. A dependent attribute, Diabetic, indicates whether an individual has diabetes or not. The dataset ensures demographic diversity and precise measurements, supporting the study of diabetes and its related health issues. Features like CVD and stroke enable broader research on comorbidities. This dataset facilitates machine learning applications, risk assessment, and personalized healthcare strategies. researchers can explore the links between diabetes, hypertension, CVD, and stroke, while healthcare providers and policymakers can leverage this to identify trends, allocate resources efficiently, and enhance public health strategies.

关键词： Diabetes Medical care Biometric Data Standardization Risk Factor Analysis Predictive Analysis

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DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks

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Vehicular Communications 2024年 47卷

作者： Hosseinzadeh, Mehdi Ali, Saqib Ahmad, Husham Jawad Alanazi, Faisal Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Rahmani, Amir Masoud Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet deliver

关键词： Artificial intelligence (AI) Clustering Flying ad hoc networks (FANETs) Optimization algorithms Unmanned aerial vehicles (UAVs)

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Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

arXiv

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arXiv 2024年

作者： Zhou, Peng Wang, Jianmin Li, Chunyan Wang, Zixu Liu, Yiping Sun, Siqi Lin, Jianxin Wei, Leyi Cai, Xibao Lai, Houtim Liu, Wei Wang, Longyue Liu, Yuansheng Zeng, Xiangxiang College of Information Science and Engineering Hunan University Changsha China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of School of Informatics Yunnan Normal University Kunming650500 China Department of Computer Science University of Tsukuba Tsukuba3058577 Japan Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai AI Laboratory Shanghai200232 China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University China School of Informatics Xiamen University Xiamen China Tencent AI Lab China

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Magneto-oscillatory localization for small-scale robots

npj Robotics

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npj Robotics 2024年第1期2卷 1-10页

作者： F. Fischer T. Qiu C. Gletter M. Jeong Division of Smart Technologies for Tumor Therapy German Cancer Research Center (DKFZ) Site Dresden Dresden Germany Faculty of Engineering Sciences University of Heidelberg Heidelberg Germany Faculty of Medicine Carl Gustav Carus Dresden University of Technology Dresden Germany Faculty of Electrical and Computer Engineering Dresden University of Technology Dresden Germany Cyber Valley Group - Biomedical Microsystems Institute of Physical Chemistry University of Stuttgart Stuttgart Germany Micro Nano and Molecular Systems Lab Max Planck Institute for Medical Research Heidelberg Germany

Magnetism is widely used for the wireless localization and actuation of robots and devices for medical procedures. However, current static magnetic localization methods suffer from large required magnets and are limited to only five degrees of freedom due to a fundamental constraint of the rotational symmetry around the magnetic axis. We present the small-scale magneto-oscillatory localization (SMOL) method, which is capable of wirelessly localizing a millimeter-scale tracker with full six degrees of freedom in deep biological tissues. The SMOL device uses the temporal oscillation of a mechanically resonant cantilever with a magnetic dipole to break the rotational symmetry, and exploits the frequency-response to achieve a high signal-to-noise ratio with sub-millimeter accuracy over a large distance of up to 12 centimeters and quasi-continuous refresh rates up to 200 Hz. Integration into real-time closed-loop controlled robots and minimally-invasive surgical tools are demonstrated to reveal the vast potential of the SMOL method.

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Picasso: A sparse learning library for high dimensional data analysis in R and Python∗

arXiv

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arXiv 2020年

作者： Ge, Jason Li, Xingguo Jiang, Haoming Liu, Han Zhang, Tong Wang, Mengdi Zhao, Tuo Department of Operations Research and Financial Engineering Princeton University Computer Science Department Princeton University School of Industrial and Systems Engineering Georgia Tech Tencent Artificial Intelligence Lab

We describe a new library named picasso 1, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies. Besides, the library allows users to choose different sparsity-inducing regularizers, including the convex `1, nonvoncex MCP and SCAD regularizers. The library is coded in C++ and has user-friendly R and Python wrappers. Numerical experiments demonstrate that picasso can scale up to large problems efficiently. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Task Offloading and Dispatching for MEC with Selfish Mobile Devices and Access Points

Task Offloading and Dispatching for MEC with Selfish Mobile ...

引用

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Lu Fang Tong Liu Yanmin Zhu Yuanyuan Yang School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Shanghai Key Lab of Scalable Computing and Systems Stony Brook University

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

Multi-access Edge Computing (MEC) comes forth as a promising computing paradigm to meet the low-delay requirements of computation-intensive applications. In this work, we focus on the task offloading and dispatching problem in MEC, where mobile devices (MDs) can decide to execute tasks locally or offload them to access points (APs), and each AP can dispatch its received tasks to other APs. Specially, we consider both APs and MDs are selfish, which aim to minimize their respective task completion delay. The problem is particularly difficult, as there exists computing resource competition among MDs and APs, respectively. Furthermore, the offloading decisions made by MDs and the dispatching decisions made by APs are interactive. To overcome the challenges, we first formulate the problem as a multi-leader multi-follower Stackelberg game, and rigorously prove the existence of a Stackelberg equilibrium. Then, we propose an efficient approach to achieve a Stackelberg equilibrium, which includes a Q-learning based offloading strategy for MDs and a best response based dispatching strategy for APs. We also demonstrate an upper bound of the total completion delay achieved by our approach with a constant approximation ratio. Extensive simulations are also conducted to show the performance of our approach, compared with baselines.

关键词： Upper bound Simulation Games Dispatching Mobile handsets Delays Task analysis

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A Deep Learning Approach for Grading of Motor Impairment Severity in Parkinson’s Disease

A Deep Learning Approach for Grading of Motor Impairment Sev...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Prithvi Prakash Rachneet Kaur Joshua Levy Richard Sowers James Brašić Manuel E. Hernandez Mobility and Fall Prevention Research Lab University of Illinois at Urbana-Champaign Urbana IL USA Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana IL USA Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL USA Russel H. Morgan Department of Radiology and Radiological Sciences The John Hopkins University Baltimore MD USA Department of Biomedical and Translational Sciences Department of Kinesiology and Community Health and Beckman Institute University of Illinois at Urbana-Champaign Urbana IL USA

Objective and quantitative monitoring of movement impairments is crucial for detecting progression in neurological conditions such as Parkinson’s disease (PD). This study examined the ability of deep learning approaches to grade motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. A convolutional neural network architecture, XceptionTime, was used to classify lower and higher levels of motor impairment in persons with PD, across five distinct rhythmic tasks: finger tapping, hand movements, pronation-supination movements of the hands, toe tapping, and leg agility. In addition, an aggregate model was trained on data from all tasks together for evaluating bradykinesia symptom severity in PD. The model performance was highest in the hand movement tasks with an accuracy of 82.6% in the hold-out test dataset; the accuracy for the aggregate model was 79.7%, however, it demonstrated the lowest variability. Overall, these findings suggest the feasibility of integrating low-cost wearable technology and deep learning approaches to automatically and objectively quantify motor impairment in persons with PD. This approach may provide a viable solution for a widely deployable telemedicine solution.

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Design of biorefineries towards carbon neutrality: A critical review

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Bioresource Technology 2023年 369卷 128256页

作者： Culaba, Alvin B. Mayol, Andres Philip San Juan, Jayne Lois G. Ubando, Aristotle T. Bandala, Argel A. Concepcion II, Ronnie S. Alipio, Melchizedek Chen, Wei-Hsin Show, Pau Loke Chang, Jo-Shu Department of Mechanical Engineering De La Salle University 2401 Taft Avenue Manila0922 Philippines Center for Engineering Sustainable Development Research De La Salle University 2401 Taft Avenue Manila0922 Philippines Department of Manufacturing Engineering and Management De La Salle University 2401 Taft Avenue Manila0922 Philippines Department of Industrial and Systems Engineering De La Salle University 2401 Taft Avenue Manila0922 Philippines Thermomechanical Analysis Laboratory De La Salle University Laguna Campus LTI Spine Road Laguna Blvd. Laguna Binan4024 Philippines Department of Electronics and Computer Engineering De La Salle University 2401 Taft Avenue Manila0922 Philippines Department of Aeronautics and Astronautics National Cheng Kung University Tainan701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung411 Taiwan Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection Wenzhou University Wenzhou325035 China Department of Sustainable Engineering Saveetha School of Engineering SIMATS Chennai602105 India Department of Chemical and Environmental Engineering University of Nottingham Malaysia 43500 Semenyih Selangor Darul Ehsan Malaysia Department of Chemical and Materials Engineering Tunghai University Taichung407 Taiwan Department of Chemical Engineering National Cheng Kung University Tainan701 Taiwan

The increase in worldwide demand for energy is driven by the rapid increase in population and exponential economic development. This resulted in the fast depletion of fossil fuel supplies and unprecedented levels of greenhouse gas in the atmosphere. To valorize biomass into different bioproducts, one of the popular and carbon–neutral alternatives is biorefineries. This system is an appropriate technology in the circular economy model. Various research highlighted the role of biorefineries as a centerpiece in the carbon–neutral ecosystem of technologies of the circular economy model. To fully realize this, various improvements and challenges need to be addressed. This paper presents a critical and timely review of the challenges and future direction of biorefineries as an alternative carbon–neutral energy source. © 2022 Elsevier Ltd

关键词： Fossil fuels

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Vector Symbolic Architectures as a Computing Framework for Emerging Hardware

arXiv

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arXiv 2021年

作者： Kleyko, Denis Davies, Mike Frady, E. Paxon Kanerva, Pentti Kent, Spencer J. Olshausen, Bruno A. Osipov, Evgeny Rabaey, Jan M. Rachkovskij, Dmitri A. Rahimi, Abbas Sommer, Friedrich T. The Redwood Center for Theoretical Neuroscience The University of California BerkeleyCA94720 United States The Intelligent Systems Lab Research Institutes of Sweden Kista16440 Sweden The Neuromorphic Computing Laboratory Intel Labs Santa ClaraCA95054 United States The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden The Department of Electrical Engineering and Computer Sciences The University of California BerkeleyCA94720 United States International Research and Training Center for Information Technologies and Systems Kyiv03680 Ukraine IBM Research – Zurich Rüschlikon8803 Switzerland

This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional Computing). This framework is well suited for implementation in stochastic, emerging hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the field-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that Vector Symbolic Architectures are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind Vector Symbolic Architectures, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing. Copyright © 2021, The Authors. All rights reserved.

关键词： Vectors

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Observation of vortex-string chiral modes in metamaterials

arXiv

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arXiv 2023年

作者： Ma, Jingwen Jia, Ding Zhang, Li Guan, Yi-Jun Ge, Yong Sun, Hong-Xiang Yuan, Shou-Qi Chen, Hongsheng Yang, Yihao Zhang, Xiang Faculties of Science and Engineering The University of Hong Kong Hong Kong Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China Research Center of Fluid Machinery Engineering and Technology School of Physics and Electronic Engineering Jiangsu University Zhenjiang212013 China International Joint Innovation Center The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute Zhejiang University Zhejiang University Jinhua321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China State Key Laboratory of Acoustics Institute of Acoustics Chinese Academy of Sciences Beijing100190 China

As a hypothetical topological defect in the geometry of spacetime, vortex strings play a crucial role in shaping the clusters of galaxies that exist today, and their distinct features can provide observable clues about the early universe’s evolution. A key feature of vortex strings is that they can interact with Weyl fermionic modes and support topological chiral-anomaly states with massless dispersions at the core of strings. To date, despite many attempts to detect vortex strings in astrophysics or to emulate them in artificially created systems, observation of these topological vortex-string chiral modes remains experimentally elusive. Here we report the experimental observation of such vortex-string chiral modes using a metamaterial system. This is implemented by inhomogeneous perturbation of a Yang-monopole phononic metamaterial. The measured linear dispersion and modal profiles confirm the existence of topological modes bound to and propagating along the vortex string with the chiral anomaly. Our work not only provides a platform for studying diverse cosmic topological defects in astrophysics but also offers intriguing device applications as topological fibres in signal processing and communication *** Codes No © 2023, CC BY.

关键词： Vortex flow

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