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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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Engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, ScienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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Bookqa: Stories of challenges and opportunities 2

Bookqa: Stories of challenges and opportunities

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2nd Workshop on Machine Reading for Question Answering, MRQA@EMNLP 2019

作者： Angelidis, Stefanos Frermann, Lea Marcheggiani, Diego Blanco, Roi Marquez, Lluis Institute for Language Cognition and Computation School of Informatics University of Edinburgh School of Computing and Information Systems University of Melbourne Amazon Research

ISBN: (纸本)9781950737819

We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer. To improve generalization, we pretrain our memory network using artificial questions generated from book sentences. We experiment with the recently published NarrativeQA corpus, on the subset of Who questions, which expect book characters as answers. We experimentally show that BERT-based retrieval and pretraining improve over baseline results significantly. At the same time, we confirm that NarrativeQA is a highly challenging data set, and that there is need for novel research in order to achieve high-precision BookQA results. We analyze some of the bottlenecks of the current approach, and we argue that more research is needed on text representation, retrieval of relevant passages, and reasoning, including commonsense knowledge. © 2019 MRQA@EMNLP 2019 - Proceedings of the 2nd Workshop on Machine Reading for Question Answering. All rights reserved.

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Symmetry-enforced ideal lanternlike phonons in the ternary nitride Li6WN4

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Physical Review B 2021年第4期104卷 L041104-L041104页

作者： Xiaotian Wang Feng Zhou Tie Yang Minquan Kuang Zhi-Ming Yu Gang Zhang School of Physical Science and Technology Southwest University Chongqing 400715 China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing 100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing 100081 China Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore 138632 Singapore

Topological physics of phonon spectra has been attracting great interest. Using the first-principle calculations and symmetry analysis, we show the realistic ternary nitride Li6WN4 features ideal (nearly flat) nodal-surface and nodal-line structures in its phonon spectra. These nodal degeneracies are shaped like lanterns, and their existence is guaranteed by nonsymmorphic symmetry. The corresponding topological phonon surface state covers exactly half the surface Brillouin zone and can thereby be distinguished from those of conventional nodal-line and nodal-surface semimetals. Our study enriches the classification of topological quantum phases, predicts ideal material candidates, and provides a good platform for investigating the interaction between nodal-line and nodal-surface phonons.

关键词： First-principles calculations Phonons Topological materials

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Guidelines for reporting cell types: The MIRACL standard

arXiv

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arXiv 2022年

作者： Lubiana, Tiago Roncaglia, Paola Mungall, Christopher J. Quardokus, Ellen M. Fortriede, Joshua D. Osumi-Sutherland, David Diehl, Alexander D. School of Pharmaceutical Sciences University of São Paulo SP São Paulo Brazil Ronin Institute United States Wellcome Genome Campus Hinxton Cambridge CB10 1SD United Kingdom Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH45224 United States Department of Biomedical Informatics University at Buffalo Jacobs School of Medicine and Biomedical Sciences BuffaloNY14203 United States

Cell types are at the root of modern biology, and describing them is a core task of the Human Cell Atlas project. Surprisingly, there are no standards for reporting new cell types, leading to a gap between classes mentioned in biomedical literature and the Cell Ontology, the primary registry of cell types. Here we introduce the Minimal Information Reporting About a CelL (MIRACL) standard, a guideline for describing cell types alongside scientific articles. In a MIRACL sheet, authors organize a label, a diagnostic description, a taxon, an anatomical structure, and a parent cell class for each cell type of interest. The MIRACL standard bridges the gap between wet-lab researchers and ontologists, facilitating the integration of biomedical knowledge into ontologies and artificial intelligence systems. © 2022, CC BY.

关键词： Cells

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Tourists Movement Analysis Based on Entropies of Markov Process 9

Tourists Movement Analysis Based on Entropies of Markov Proc...

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9th International Congress on Advanced Applied informatics, IIAI-AAI 2020

作者： Ishii, Naohiro Odagiri, Kazuya Iwamoto, Hidekazu Takahashi, Satoshi Matsuo, Tokuro Advanced Institute of Industrial Technology Information Systems Architecture Tokyo Japan Sugiyama Jyogakuen University Culture-Informaion Studiees Nagoya Japan Josai International University Graduate School of International Administration Tokyo Japan Electro-Communication University Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (纸本)9781728173979

Tourist movement studies have been carried out for understanding tourists decisions for visiting and sightseeing. Knowing tourist interests and wayfinding decision-making affect tourists routes and facilities since the planners and hotel facilities will do planning their hospitality and every service plans using their information. To make the model of the tourist movements, an effective mathematical description will be expected for their systematic analysis. In this paper, Markov process model of the tourist movements are newly developed using information theory. Then, it is shown that the order of Markov process of the movements is estimated using conditional entropies, efficiently and the shuffling operation of Markov process is newly proposed to verify conditional entropies of the Markov process. The stochastic simulation model based on the analysis is useful for the verification of the Markov process with the estimated order of the behavior of tourists movement. © 2020 IEEE.

关键词： Markov processes

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Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation

arXiv

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arXiv 2021年

作者： Liu, Yongtuo Xu, Dan Ren, Sucheng Wu, Hanjie Cai, Hongmin He, Shengfeng School of Computer Science South China University of Technology China Institute of Informatics University of Amsterdam Netherlands Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong School of Computing and Information Systems Singapore Management University Singapore

Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typically regard each crowd image as a whole and reduce domain discrepancies in a holistic manner, thus limiting further improvement of domain adaptation performance. To this end, we propose to untangle domain-invariant crowd and domain-specific background from crowd images and design a fine-grained domain adaption method for crowd counting. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations in a weakly-supervised manner. Based on the derived segmentation, we design a crowd-aware domain adaptation mechanism consisting of two crowd-aware adaptation modules, i.e., Crowd Region Transfer (CRT) and Crowd Density Alignment (CDA). The CRT module is designed to guide crowd features transfer across domains beyond background distractions. The CDA module dedicates to regularising target-domain crowd density generation by its own crowd density distribution. Our method outperforms previous approaches consistently in the widely-used adaptation scenarios. © 2021, CC BY.

关键词： Image enhancement

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An Information systems for Infrastructure Asset Management Tailored to Portuguese Water Utilities: Platform Conceptualization and a Prototype Demonstration

SSRN

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SSRN 2022年

作者： Carriço, Nelson Covas, Dídia Monteiro, Laura Santos, João Barreira, Raquel Mendes, Ana Ferreira, Bruno Antunes, André Grueau, Cédric Brito, Isabel S. INCITE Department of Civil Engineering Barreiro School of Technology Polytechnic Institute of Setúbal Setúbal Portugal Sustain.RD Department of Systems and Information Technology Setúbal School of Technology Polytechnic Institute of Setúbal Setúbal Portugal CINEA Department of Mathematics and Informatics Barreiro School of Technology Polytechnic Institute of Setúbal Setúbal Portugal CICE Department of Information Systems School of Business Administration Polytechnic Institute of Setúbal Setúbal Portugal CERIS Department of Civil Engineering Architecture and Georesources Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Engineering School of Technology and Management Polytechnic Institute of Beja Beja Portugal

This paper presents a new information technology platform specially tailored for infrastructure asset management of urban water systems operated by water utilities of lower digital maturity level. This platform aims at the integration of different data from the water utilities with several information systems and the assessment of the system performance. The platform was tested with data from five small to medium size Portuguese water utilities. Obtained results are very promising since the platform allows to assess the systems performance periodically which constitute an important part of the infrastructure asset management for small and medium-sized water utilities. © 2022, The Authors. All rights reserved.

关键词： Data integration

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Brain multigraph prediction using topology-aware adversarial graph neural network

arXiv

引用

arXiv 2021年

作者： Bessadok, Alaa Mahjoub, Mohamed Ali Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia School of Science and Engineering Computing University of Dundee United Kingdom

Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches. © 2021, CC BY-NC-SA.

关键词： Graphic methods

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The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting

arXiv

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arXiv 2024年

作者： Haag, David Kumar, Devender Gruber, Sebastian Hofer, Dominik Sareban, Mahdi Treff, Gunnar Niebauer, Josef Bull, Christopher Schmidt, Albrecht Smeddinck, Jan David Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg Austria Department of Psychology Paris-Lodron-University of Salzburg Salzburg Austria Digital Health Information Systems Center for Health & Bioresources AIT Austrian Institute of Technology GmbH Graz Austria University of Southern Denmark Odense Denmark Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Linz Austria Human Motion Analytics Salzburg Research Forschungsgesellschaft Salzburg Austria LMU Munich Munich Germany University Institute for Sports Medicine Prevention and Rehabilitation Salzburg Austria Institute of Molecular Sports and Rehabilitation Medicine Paracelsus Medical University Salzburg Austria Open Lab School of Computing Newcastle University Newcastle upon Tyne United Kingdom

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual’s current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing. © 2024, CC BY.

关键词： Electronic health record

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Blockchain based Public Auditing Outsourcing for Cloud Storage

Blockchain based Public Auditing Outsourcing for Cloud Stora...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Yangfei Lin Jie Li Shigetomo Kimura Yongbing Zhang Yusheng Ji Yang Yang Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki Japan Shanghai Jiao Tong University Shanghai China Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Ibaraki Japan Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan School of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (纸本)9781665408790

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user.

关键词： Cloud computing Privacy Data integrity Conferences Smart contracts Robustness Outsourcing

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