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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Konstantinos Alexakis Jabier Martinez Panagiotis Kokkinakos Antonio Filograna Yury Glikman Xabier Uriarte Dimitris Askounis Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Software Technologies Tecnalia Basque Research and Technology Alliance (BRTA) Derio Spain Open Public Service Innovation Engineering Ingegneria Informatica S.p.A. Rome Italy Open Service Engineering Group Fraunhofer FOKUS Berlin Germany

public policy-making has a strong impact on our daily lives and on most of the aspects of our society including its relation with the environment. Sustainability as a global goal cannot be achieved without effective policies supporting it. The current trend in policy-making has two main pillars, policies are a) co-created with citizens and other relevant stakeholders and b) data-based to take informed decisions. Information technologies are needed to support these pillars and facilitate evidence-based policy-making. We present the DECIDO Portal that acts as a catalyst towards putting these general principles into practice gathering the technical components to be used within the whole policy life cycle. Notably, the portal lays its foundations on a well-defined co-creation methodology that can be seamlessly followed from a unique portal and provides best-practices for participatory processes and data-driven analyses. A set of real-world experimental policies in different countries serves as case studies to showcase and discuss the usage of the portal.

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Algorithmic Trading Model for Stock Price Forecasting Integrating Forester with Golden Ratio Strategy

Algorithmic Trading Model for Stock Price Forecasting Integr...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Niranjana Govindan Bharathi Baskaran Azizul Azizan Fitri Yakub Inge Dhamanti C Dhaya Department of Computing Technologies Faculty of Engineering & Technology SRMIST Kattankulathur Chennai India Cyberphysical Research Group (Embedded AI & ML Science Lab) Faculty of Intelligence UTM Kuala Lumpur Malaysia Electronic System Engineering Department Malaysia-Japan International Institute of Technology UTM Kuala Lumpur Malaysia Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Department of Computer Science and Technology Adhiparasakthi Engineering College Melmaruvathur India

ISBN: (数字)9798350340204

ISBN: (纸本)9798350340211

Algorithmic trading has emerged as a powerful tool for making data-driven trading decisions in financial markets. In this paper, we present an innovative algorithmic trading model for stock price forecasting that integrates forest-based machine learning algorithms with the Golden Ratio strategy. Our proposed approach aims to leverage the predictive power of machine learning while incorporating insights from technical analysis to enhance trading performance and profitability. The methodology involves data collection and preprocessing, feature engineering, model development, and performance evaluation. Historical stock price data, financial indicators, and sentiment analysis data are gathered and pre-processed to ensure data quality. Feature engineering techniques are applied to extract relevant features, and forest-based machine learning algorithms are trained on the data to forecast stock prices. The Golden Ratio strategy is integrated into the trading model to identify the entry point and exit point. Empirical validation and performance evaluation demonstrate the effectiveness and robustness of the proposed approach. Through back testing and simulation-based testing, we evaluate the model's accuracy, profitability, and risk-adjusted returns. Our findings contribute to the advancement of algorithmic trading research and practice, offering insights into the potential benefits of combining machine learning algorithms with established trading strategies. The proposed hybrid model incorporates Golden Ratio Strategy, integrating Random Forest (RF). The results of the proposed random forest with Golden Ratio Strategy are compared with the results of individual model. This comparison helps in proving the efficiency of the proposed model.

关键词： Adaptation models Machine learning algorithms Soft sensors Forestry Reinforcement learning Predictive models Feature extraction Data models Forecasting Testing

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Unlocking Real-Time Data Access in Diabetes Management: Toward an Interoperability Model

SSRN

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SSRN 2024年

作者： Randine, Pietro Wolff, Miriam Kopperstad Pocs, Matthias Connell, Ian R.O. Cafazzo, Joseph A. Årsand, Eirik Faculty of Science and Technology Department of Computer Science UiT The Arctic University of Norway Tromsø Norway Faculty of Information Technology and Electrical Engineering Department of ICT and Natural Sciences Norwegian University of Science and Technology Trondheim Norway STELAR Security Technology Law Research Hamburg Germany Centre for Digital Therapeutics Toronto General Hospital University Health Network Toronto Canada Institute of Health Policy Management and Evaluation Institute of Biomedical Engineering Department of Computer Science the Rotman School of Management University of Toronto Toronto Canada Institute of Health Policy Management and Evaluation Dalla Lana School of Public Health University of Toronto Toronto Canada UiT The Arctic University of Norway Tromsø Norway

Background: In today's data-driven era, 'Openness' promotes transparency and accessibility. This trend is particularly relevant in new initiatives like the European Health Data Space or when processing health data. This article focuses on Diabetes, which relies on real-time medical device data, such as continuous glucose monitors (CGMs), insulin pumps or hybrid closed-loop systems. These devices provide vital information for monitoring and adjusting treatment plans, making real-time access to their data critical for effective diabetes ***: We explore real-time data access for third-party applications, emphasising primary (treatment) and secondary (research) use. Our exploration includes analysing how Application Programming Interfaces (APIs) contribute to secure data retrieval. Additionally, we investigate the impact of terms of service and copyright law on patient-driven innovation within open-source ***: We evaluated current diabetes medical devices and software solutions in Norway, emphasising their real-time data provision capabilities and API functionality. The study also addresses the legal frameworks relevant to these devices and software, particularly the needs of open-source DIY communities, and proposes an interoperability ***: Our research identified seven diabetes medical devices and nine regulated software solutions, with only one offering a publicly accessible API. This finding underlines a significant lack of real-time data access options. The analysis compares vendor-specific and open-source software, highlighting substantial limitations in interoperability and data accessibility. The DIY solutions, while innovative, presented technical and legal challenges, limiting their practical ***: Diabetes management needing real-time data involves navigating challenges related to security, transparency, and accessibility. We propose a model for interoperability that necessitates regulatory decisio

关键词： Interoperability

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Preliminary Technoeconomical Evaluation of Cold Ironing

Preliminary Technoeconomical Evaluation of Cold Ironing

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International Conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Eleni P. Lamprinidi Stamatios Orfanos-Pepainas George J. Tsekouras Fotios D. Kanellos John M. Prousalidis Andreas Vlachos School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Department of Electrical & Electronics Engineering University of West Attica Aigaleo-Athens Greece School of Electrical & Computer Engineering Technical University of Crete Akrotiri Chania Greece School of Naval Architecture & Marine Engineering National Technical University of Athens Athens Greece Department of Long Term Energy Planning Renewable Energy Sources & Environmental Policy Regulatory Authority for Energy Athens Greece

In the context of reducing air pollution in ports, which are often located in the urban environment of cities, such as the port of Piraeus or Thessaloniki in Greece, the European Directive 2014/94/EU requires the application of Cold Ironing in the shipping sector until the end of 2025. That is, the berthed ships need to be supplied by the electricity system of the port, turning off the operation of their thermal electrical power units. A key question that arises is whether the application of cold ironing is economically viable for various types of ships and under what conditions, without subsidy. In this article, a method for estimating the economic viability of connecting ships to the port network by simply ignoring the network constraints (assuming the availability of an infinite bus) is presented. The proposed method is applied to cruise ships, containerships, and bulk carriers, indicative for the port of Thessaloniki, incorporating standard Medium Voltage (MV) tariffs of the Greek electricity system, determining the allowable corrected annual construction & maintenance cost of a Cold Ironing (CI) investment.

关键词： Renewable energy sources Costs Oils Urban areas Tariffs Europe Seaports

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Irregular warfare in strategic competition

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Defence Studies 2024年第1期24卷 148-158页

作者： Chinchilla, Alexandra Atwell, Kyle Bradstreet, Alexis Crombe, Catherine Leblanc, Luther Department of International Affairs Texas A&M University Bush School of Government and Public Service College Station TX United States Irregular Warfare Initiative and a Nonresident Atlantic Council’s Forward Defense Practice Scowcroft Center for Strategy and Security United States Department of Electrical Engineering and Computer Science United States Military at West Point Cyber Science West Point United States United States Military Academy Washington DC United States Department of Social Sciences United States Military Academy at West Point West Point United States

After two decades of counterinsurgency and counterterrorism in Afghanistan and Iraq, the US military has shifted to preparing for large-scale combat operations. However, it would be a mistake to discard hard-earned lessons from these conflicts. Despite contemporary advances in technology and important differences between current US competitors and the Soviet Union, irregular warfare will play a prominent role in the new era of strategic competition. It was a prominent form of US-Soviet competition during the Cold War, is already used extensively by the United States and its competitors and remains attractive given concerns about escalation between nuclear-armed powers. Given the continued relevance of irregular warfare, we focus on two main lessons from the US experience. First, since irregular warfare is about influencing populations and achieving political goals without large-scale combat operations, influencing and working alongside the partner is the primary mission. The second lesson follows directly from the first;if irregular warfare is ultimately about achieving policy goals with an economy of military force, IW is a team sport requiring joint and interagency collaboration to be effectively implemented. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： counterinsurgency great powers Irregular warfare security strategic competition

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The Effect of Epsilon-Near-Zero (ENZ) Modes on the Casimir Interaction between Ultrathin Films

arXiv

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arXiv 2022年

作者： Gong, Tao Liberal, Iñigo Spreng, Benjamin Camacho, Miguel Engheta, Nader Munday, Jeremy N. Department of Electrical and Computer Engineering University of California Davis United States Department of Materials Science and Engineering University of California Davis United States Institute of Smart Cities Public University of Navarre Spain Department of Electronics and Electromagnetism University of Seville Spain Department of Electrical and Systems Engineering University of Pennsylvania United States

Vacuum fluctuation-induced interactions between macroscopic metallic objects result in an attractive force between them, a phenomenon known as the Casimir effect. This force is the result of both plasmonic and photonic modes. For very thin films, field penetration through the films will modify the allowed modes. Here, we investigate the Casimir interaction between two ultrathin films from the perspective of the force distribution over real frequencies for the first time and find pronounced repulsive contributions to the force due to the highly confined and nearly dispersion-free epsilon-near-zero (ENZ) modes that only exist in ultrathin films. These contributions are found to persistently occur around the ENZ frequency of the film and are irrespective of the inter-film separation. We further associate the ENZ modes with a striking thickness dependence in the averaged force density for conductive thin films, a metric signifying a thin-film's acceleration due to Casimir effect. Our results shed light on the role of the unique vacuum fluctuation modes existing in ultrathin ENZ materials, which may offer significant potential for engineering the motion of objects in nanomechanical systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Ultrathin films

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Label-free impedimetric immunosensor for point-of-care detection of COVID-19 antibodies

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Microsystems & Nanoengineering 2023年第1期9卷 161-176页

作者： Lian C.T.Shoute Gaser N.Abdelrasoul Yuhao Ma Pedro A.Duarte Cole Edwards Ran Zhuo Jie Zeng Yiwei Feng Carmen L.Charlton Jamil N.Kanji Shawn Babiuk Jie Chen Department of Electrical and Computer Engineering University of AlbertaEdmontonABT6G 2V4Canada Public Health Laboratory Alberta Precision LaboratoriesEdmontonABCanada Department of Laboratory Medicine and Pathology University of AlbertaEdmontonABT6G 2B7Canada Li Ka Shing Institute for Virology University of AlbertaEdmontonABCanada Division of Infectious Diseases Department of MedicineCumming School of MedicineUniversity of CalgaryCalgaryABCanada Department of Pathology&Laboratory Medicine Cumming School of MedicineUniversity of CalgaryCalgaryABCanada National Centre for Foreign Animal Disease Canadian Food Inspection AgencyWinnipegMBCanada Department of Immunology University of ManitobaWinnipegMBCanada Department of Biomedical Engineering University of AlbertaEdmontonABT6G 2R3Canada

The COVID-19 pandemic has posed enormous challenges for existing diagnostic tools to detect and monitor ***,there is a need to develop point-of-care(POC)devices to perform fast,accurate,and accessible diagnostic methods to detect infections and monitor immune *** most amenable to miniaturization and suitable for POC applications are biosensors based on electrochemical *** have developed an impedimetric immunosensor based on an interdigitated microelectrode array(IMA)to detect and monitor SARS-CoV-2 antibodies in human *** chemistry was applied to functionalize and covalently immobilize the spike protein(S-protein)of SARS-CoV-2 on the surface of the IMA to serve as the recognition layer and specifically bind anti-spike *** bound to the S-proteins in the recognition layer result in an increase in capacitance and a consequent change in the impedance of the *** impedimetric immunosensor is label-free and uses non-Faradaic impedance with low nonperturbing AC voltage for *** sensitivity of a capacitive immunosensor can be enhanced by simply tuning the ionic strength of the sample *** device exhibits an LOD of 0.4 BAU/ml,as determined from the standard curve using WHO IS for anti-SARS-CoV-2 immunoglobulins;this LOD is similar to the corresponding LODs reported for all validated and established commercial assays,which range from 0.41 to 4.81 BAU/*** proof-of-concept biosensor has been demonstrated to detect anti-spike antibodies in sera from patients infected with COVID-19 within 1 h.

关键词： antibodies impedance free

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electrically switchable Casimir forces using transparent conductive oxides

arXiv

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arXiv 2022年

作者： Gong, Tao Spreng, Benjamin Camacho, Miguel Liberal, Inigo Engheta, Nader Munday, Jeremy N. Department of Electrical and Computer Engineering University of California Davis United States Department of Materials Science and Engineering University of California Davis United States Department of Electronics and Electromagnetism University of Seville Spain Institute of Smart Cities Public University of Navarre Spain Department of Electrical and Systems Engineering University of Pennsylvania United States

Casimir forces between charge-neutral bodies originate from quantum vacuum fluctuations of electromagnetic fields, which exhibit a critical dependence on material’s electromagnetic properties. Over the years, in-situ modulation of material’s optical properties has been enabled through various means and has been widely exploited in a plethora of applications such as electro-optical modulation, transient color generation, bio- or chemical sensing, etc. Yet Casimir force modulation has been hindered by difficulty in achieving high modulation signals due to the broadband nature of the Casimir interaction. Here we propose and investigate two configurations that allow for in-situ modulation of Casimir forces through electrical gating of a metal-insulator-semiconductor (MIS) junction comprised of transparent conductive oxide (TCO) materials. By switching the gate voltage on and off, a force modulation of > 400 pN is predicted due to substantive charge carrier accumulation in the TCO layer, which can be easily measured using state-of-the-art force measurement techniques in an atomic force microscope (AFM). We further examine the influence of the oxide layer thickness on the force modulation, suggesting the importance of the fine control of the oxide layer deposition. Our work provides a promising pathway for modulating the Casimir effect in-situ with experimentally measurable force contrast. Copyright © 2022, The Authors. All rights reserved.

关键词： Modulation

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The Impact of ChatGPT Exposure on User Interactions With a Motivational Interviewing Chatbot: Quasi-Experimental Study

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JMIR Formative Research 2025年 9卷 e56973页

作者： Zhu, Jiading Dong, Alec Wang, Cindy Veldhuizen, Scott Abdelwahab, Mohamed Brown, Andrew Selby, Peter Rose, Jonathan The Edward S. Rogers Sr. Department of Electrical & Computer Engineering University of Toronto Toronto ON Canada Department of Mechanical & Industrial Engineering University of Toronto Toronto ON Canada INTREPID Lab Centre for Addiction and Mental Health Toronto ON Canada Institute for Mental Health Policy Research Centre for Addiction and Mental Health Toronto ON Canada Department of Family and Community Medicine University of Toronto Toronto ON Canada Dalla Lana School of Public Health University of Toronto Toronto ON Canada

Background: The worldwide introduction of ChatGPT in November 2022 may have changed how its users perceive and interact with other chatbots. This possibility may confound the comparison of responses to pre-ChatGPT and post-ChatGPT iterations of pre-existing chatbots, in turn affecting the direction of their evolution. Before the release of ChatGPT, we created a therapeutic chatbot, MIBot, whose goal is to use motivational interviewing to guide smokers toward making the decision to quit smoking. We were concerned that measurements going forward would not be comparable to those in the past, impacting the evaluation of future changes to the chatbot. Objective: The aim of the study is to explore changes in how users interact with MIBot after the release of ChatGPT and examine the relationship between these changes and users’ familiarity with ChatGPT. Methods: We compared user interactions with MIBot prior to ChatGPT’s release and 6 months after the release. Participants (N=143) were recruited through a web-based platform in November of 2022, prior to the release of ChatGPT, to converse with MIBot, in an experiment we refer to as MIBot (version 5.2). In May 2023, a set of (n=129) different participants were recruited to interact with the same version of MIBot and asked additional questions about their familiarity with ChatGPT, in the experiment called MIBot (version 5.2A). We used the Mann-Whitney U test to compare metrics between cohorts and Spearman rank correlation to assess relationships between familiarity with ChatGPT and other metrics within the MIBot (version 5.2A) cohort. Results: In total, 83(64.3%) participants in the MIBot (version 5.2A) cohort had used ChatGPT, with 66 (51.2%) using it on a regular basis. Satisfaction with MIBot was significantly lower in the post-ChatGPT cohort (U=11,331.0;P=.001), driven by a decrease in perceived empathy as measured by the Average Consultation and Relational Empathy Measure (U=10,838.0;P=.01). Familiarity with ChatGPT was

关键词： artificial intelligence chatbot ChatGPT conversational agent digital health experimental large language models motivational interviewing natural language processing smoking cessation

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A Deep Learning Framework for the Classification of ECG Signals

A Deep Learning Framework for the Classification of ECG Sign...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Sadaf Khan Wahab Haseeb Bhatti Fawad Chaudhary Arsalan Shafique Muhammad Irfan Mansoor Ali Teevno Rizwan Qureshi Army Public College of Management and Sciences Rawalpindi Pakistan Faculty of Electrical Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Pakistan Mehran University of Engineering and Technology Jamshoro Pakistan School of Computing National University of Computer and Emerging Sciences Karachi Pakistan

ISBN: (纸本)9781665491075

The electrical activity of human heart is captured by an electrocardiogram (ECG) signal. The careful analysis of ECG signal can provide valuable insights into the cardiac activity, which can lead to timely therapeutic interventions. The rise of deep learning and the large amount of complex healthcare datasets provides an opportunity to develop intelligent data analysis systems, that can help identify disease patterns and clinical decision support. In this work, we propose a deep neural network model to classify real time ECG signals into normal, atrial fibrillation, and ventricular fibrillation. In the preprocessing stage, the signal is passed through various filters to remove different types of artifacts. The proposed model is trained on a public dataset, containing 30,450 training examples and validated across 6,525 examples and then tested with 6,525 examples. The model achieves testing accuracy of 0.9969%.

关键词： Deep learning Training Heart Neural networks Pipelines Medical services Electrocardiography

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