The International Council on Systems Engineering, the leading authority in the realm of systems engineering (SE), defines this field of study as a transdisciplinary and integrative approach to enabling the realization of the entire life cycle of any engineered system [...]
{"title":"Editorial to the Special Issue “Systems Engineering and Knowledge Management”","authors":"Vladimír Bureš","doi":"10.3390/info15070402","DOIUrl":"https://doi.org/10.3390/info15070402","url":null,"abstract":"The International Council on Systems Engineering, the leading authority in the realm of systems engineering (SE), defines this field of study as a transdisciplinary and integrative approach to enabling the realization of the entire life cycle of any engineered system [...]","PeriodicalId":510156,"journal":{"name":"Information","volume":"37 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cognitive network science has increased our understanding of how the mental lexicon is structured and how that structure at the micro-, meso-, and macro-levels influences language and cognitive processes. Most of the research using this approach has used single-layer networks of English words. We consider two fundamental concepts in network science—nodes and connections (or edges)—in the context of two lesser-studied languages (American Sign Language and Kaqchikel) to see if a single-layer network can model phonological similarities among words in each of those languages. The analyses of those single-layer networks revealed several differences in network architecture that may challenge the cognitive network approach. We discuss several directions for future research using different network architectures that could address these challenges and also increase our understanding of how language processing might vary across languages. Such work would also provide a common framework for research in the language sciences, despite the variation among human languages. The methodological and theoretical tools of network science may also make it easier to integrate research of various language processes, such as typical and delayed development, acquired disorders, and the interaction of phonological and semantic information. Finally, coupling the cognitive network science approach with investigations of languages other than English might further advance our understanding of cognitive processing in general.
{"title":"Defining Nodes and Edges in Other Languages in Cognitive Network Science—Moving beyond Single-Layer Networks","authors":"M. Vitevitch, Alysia E. Martinez, Riley England","doi":"10.3390/info15070401","DOIUrl":"https://doi.org/10.3390/info15070401","url":null,"abstract":"Cognitive network science has increased our understanding of how the mental lexicon is structured and how that structure at the micro-, meso-, and macro-levels influences language and cognitive processes. Most of the research using this approach has used single-layer networks of English words. We consider two fundamental concepts in network science—nodes and connections (or edges)—in the context of two lesser-studied languages (American Sign Language and Kaqchikel) to see if a single-layer network can model phonological similarities among words in each of those languages. The analyses of those single-layer networks revealed several differences in network architecture that may challenge the cognitive network approach. We discuss several directions for future research using different network architectures that could address these challenges and also increase our understanding of how language processing might vary across languages. Such work would also provide a common framework for research in the language sciences, despite the variation among human languages. The methodological and theoretical tools of network science may also make it easier to integrate research of various language processes, such as typical and delayed development, acquired disorders, and the interaction of phonological and semantic information. Finally, coupling the cognitive network science approach with investigations of languages other than English might further advance our understanding of cognitive processing in general.","PeriodicalId":510156,"journal":{"name":"Information","volume":"55 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Goswami, Niranajana Sampathila, G. M. Bairy, Anushree Goswami, Dhruva Darshan Brp Siddarama, S. Belurkar
A digital microscope plays a crucial role in the better and faster diagnosis of an abnormality using various techniques. There has been significant development in this domain of digital pathology. Sickle cell disease (SCD) is a genetic disorder that affects hemoglobin in red blood cells. The traditional method for diagnosing sickle cell disease involves preparing a glass slide and viewing the slide using the eyepiece of a manual microscope. The entire process thus becomes very tedious and time consuming. This paper proposes a semi-automated system that can capture images based on a predefined program. It has an XY stage for moving the slide horizontally or vertically and a Z stage for focus adjustments. The case study taken here is of SCD. The proposed hardware captures SCD slides, which are further used to classify them with respect to normal. They are processed using deep learning models such as Darknet-19, ResNet50, ResNet18, ResNet101, and GoogleNet. The tested models demonstrated strong performance, with most achieving high metrics across different configurations varying with an average of around 97%. In the future, this semi-automated system will benefit pathologists and can be used in rural areas, where pathologists are in short supply.
{"title":"Explainable Artificial Intelligence and Deep Learning Methods for the Detection of Sickle Cell by Capturing the Digital Images of Blood Smears","authors":"N. Goswami, Niranajana Sampathila, G. M. Bairy, Anushree Goswami, Dhruva Darshan Brp Siddarama, S. Belurkar","doi":"10.3390/info15070403","DOIUrl":"https://doi.org/10.3390/info15070403","url":null,"abstract":"A digital microscope plays a crucial role in the better and faster diagnosis of an abnormality using various techniques. There has been significant development in this domain of digital pathology. Sickle cell disease (SCD) is a genetic disorder that affects hemoglobin in red blood cells. The traditional method for diagnosing sickle cell disease involves preparing a glass slide and viewing the slide using the eyepiece of a manual microscope. The entire process thus becomes very tedious and time consuming. This paper proposes a semi-automated system that can capture images based on a predefined program. It has an XY stage for moving the slide horizontally or vertically and a Z stage for focus adjustments. The case study taken here is of SCD. The proposed hardware captures SCD slides, which are further used to classify them with respect to normal. They are processed using deep learning models such as Darknet-19, ResNet50, ResNet18, ResNet101, and GoogleNet. The tested models demonstrated strong performance, with most achieving high metrics across different configurations varying with an average of around 97%. In the future, this semi-automated system will benefit pathologists and can be used in rural areas, where pathologists are in short supply.","PeriodicalId":510156,"journal":{"name":"Information","volume":"1 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mateo Sokac, Leo Mršić, M. Balković, Maja Brkljačić
Recent advancements in cognitive neuroscience, particularly in electroencephalogram (EEG) signal processing, image generation, and brain–computer interfaces (BCIs), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of artificial intelligence (AI) to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and noise sensitivity. Instead, BRAINS employs Long Short-Term Memory (LSTM) networks and contrastive learning, which effectively handle time-series EEG data and recognize intrinsic connections and patterns. The study utilizes the MNIST dataset of handwritten digits as stimuli in EEG experiments, allowing for diverse yet controlled stimuli. The data collected are then processed through an LSTM-based network, employing contrastive learning and extracting complex features from EEG data. These features are fed into an image generator model, producing images as close to the original stimuli as possible. This study demonstrates the potential of integrating AI and EEG technology, offering promising implications for the future of brain–computer interfaces.
{"title":"Bridging Artificial Intelligence and Neurological Signals (BRAINS): A Novel Framework for Electroencephalogram-Based Image Generation","authors":"Mateo Sokac, Leo Mršić, M. Balković, Maja Brkljačić","doi":"10.3390/info15070405","DOIUrl":"https://doi.org/10.3390/info15070405","url":null,"abstract":"Recent advancements in cognitive neuroscience, particularly in electroencephalogram (EEG) signal processing, image generation, and brain–computer interfaces (BCIs), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of artificial intelligence (AI) to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and noise sensitivity. Instead, BRAINS employs Long Short-Term Memory (LSTM) networks and contrastive learning, which effectively handle time-series EEG data and recognize intrinsic connections and patterns. The study utilizes the MNIST dataset of handwritten digits as stimuli in EEG experiments, allowing for diverse yet controlled stimuli. The data collected are then processed through an LSTM-based network, employing contrastive learning and extracting complex features from EEG data. These features are fed into an image generator model, producing images as close to the original stimuli as possible. This study demonstrates the potential of integrating AI and EEG technology, offering promising implications for the future of brain–computer interfaces.","PeriodicalId":510156,"journal":{"name":"Information","volume":"83 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The novelty of this paper is in determining and using hyperparameters to improve the Extended Isolation Forest (EIF) algorithm, a relatively new algorithm, to detect malicious activities in network traffic. The EIF algorithm is a variation of the Isolation Forest algorithm, known for its efficacy in detecting anomalies in high-dimensional data. Our research assesses the performance of the EIF model on a newly created dataset composed of Zeek Connection Logs, UWF-ZeekDataFall22. To handle the enormous volume of data involved in this research, the Hadoop Distributed File System (HDFS) is employed for efficient and fault-tolerant storage, and the Apache Spark framework, a powerful open-source Big Data analytics platform, is utilized for machine learning (ML) tasks. The best results for the EIF algorithm came from the 0-extension level. We received an accuracy of 82.3% for the Resource Development tactic, 82.21% for the Reconnaissance tactic, and 78.3% for the Discovery tactic.
{"title":"Extended Isolation Forest for Intrusion Detection in Zeek Data","authors":"Fariha Moomtaheen, S. Bagui, S. Bagui, D. Mink","doi":"10.3390/info15070404","DOIUrl":"https://doi.org/10.3390/info15070404","url":null,"abstract":"The novelty of this paper is in determining and using hyperparameters to improve the Extended Isolation Forest (EIF) algorithm, a relatively new algorithm, to detect malicious activities in network traffic. The EIF algorithm is a variation of the Isolation Forest algorithm, known for its efficacy in detecting anomalies in high-dimensional data. Our research assesses the performance of the EIF model on a newly created dataset composed of Zeek Connection Logs, UWF-ZeekDataFall22. To handle the enormous volume of data involved in this research, the Hadoop Distributed File System (HDFS) is employed for efficient and fault-tolerant storage, and the Apache Spark framework, a powerful open-source Big Data analytics platform, is utilized for machine learning (ML) tasks. The best results for the EIF algorithm came from the 0-extension level. We received an accuracy of 82.3% for the Resource Development tactic, 82.21% for the Reconnaissance tactic, and 78.3% for the Discovery tactic.","PeriodicalId":510156,"journal":{"name":"Information","volume":"46 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Binh Kieu-Do-Nguyen, Nguyen The The Binh, C. Pham-Quoc, Huynh Phuc Nghi, Ngoc-Thinh Tran, Trong-Thuc Hoang, C. Pham
In the modern era of the Internet of Things (IoT), especially with the rapid development of quantum computers, the implementation of postquantum cryptography algorithms in numerous terminals allows them to defend against potential future quantum attack threats. Lattice-based cryptography can withstand quantum computing attacks, making it a viable substitute for the currently prevalent classical public-key cryptography technique. However, the algorithm’s significant time complexity places a substantial computational burden on the already resource-limited chip in the IoT terminal. In lattice-based cryptography algorithms, the polynomial multiplication on the finite field is well known as the most time-consuming process. Therefore, investigations into efficient methods for calculating polynomial multiplication are essential for adopting these quantum-resistant lattice-based algorithms on a low-profile IoT terminal. Number theoretic transform (NTT), a variant of fast Fourier transform (FFT), is a technique widely employed to accelerate polynomial multiplication on the finite field to achieve a subquadratic time complexity. This study presents an efficient FPGA-based implementation of number theoretic transform for the CRYSTAL Kyber, a lattice-based public-key cryptography algorithm. Our hybrid design, which supports both forward and inverse NTT, is able run at high frequencies up to 417 MHz on a low-profile Artix7-XC7A100T and achieve a low latency of 1.10μs while achieving state-of-the-art hardware efficiency, consuming only 541-LUTs, 680 FFs, and four 18 Kb BRAMs. This is made possible thanks to the newly proposed multilevel pipeline butterfly unit architecture in combination with employing an effective coefficient accessing pattern.
{"title":"Compact and Low-Latency FPGA-Based Number Theoretic Transform Architecture for CRYSTALS Kyber Postquantum Cryptography Scheme","authors":"Binh Kieu-Do-Nguyen, Nguyen The The Binh, C. Pham-Quoc, Huynh Phuc Nghi, Ngoc-Thinh Tran, Trong-Thuc Hoang, C. Pham","doi":"10.3390/info15070400","DOIUrl":"https://doi.org/10.3390/info15070400","url":null,"abstract":"In the modern era of the Internet of Things (IoT), especially with the rapid development of quantum computers, the implementation of postquantum cryptography algorithms in numerous terminals allows them to defend against potential future quantum attack threats. Lattice-based cryptography can withstand quantum computing attacks, making it a viable substitute for the currently prevalent classical public-key cryptography technique. However, the algorithm’s significant time complexity places a substantial computational burden on the already resource-limited chip in the IoT terminal. In lattice-based cryptography algorithms, the polynomial multiplication on the finite field is well known as the most time-consuming process. Therefore, investigations into efficient methods for calculating polynomial multiplication are essential for adopting these quantum-resistant lattice-based algorithms on a low-profile IoT terminal. Number theoretic transform (NTT), a variant of fast Fourier transform (FFT), is a technique widely employed to accelerate polynomial multiplication on the finite field to achieve a subquadratic time complexity. This study presents an efficient FPGA-based implementation of number theoretic transform for the CRYSTAL Kyber, a lattice-based public-key cryptography algorithm. Our hybrid design, which supports both forward and inverse NTT, is able run at high frequencies up to 417 MHz on a low-profile Artix7-XC7A100T and achieve a low latency of 1.10μs while achieving state-of-the-art hardware efficiency, consuming only 541-LUTs, 680 FFs, and four 18 Kb BRAMs. This is made possible thanks to the newly proposed multilevel pipeline butterfly unit architecture in combination with employing an effective coefficient accessing pattern.","PeriodicalId":510156,"journal":{"name":"Information","volume":"8 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the industrial sector, accurate fault identification is paramount for ensuring both safety and economic efficiency throughout the production process. However, due to constraints imposed by actual working conditions, the motor state features collected are often limited in number and singular in nature. Consequently, extending and extracting these features pose significant challenges in fault diagnosis. To address this issue and strike a balance between model complexity and diagnostic accuracy, this paper introduces a novel motor fault diagnostic model termed FSCL (Fourier Singular Value Decomposition combined with Long and Short-Term Memory networks). The FSCL model integrates traditional signal analysis algorithms with deep learning techniques to automate feature extraction. This hybrid approach innovatively enhances fault detection by describing, extracting, encoding, and mapping features during offline training. Empirical evaluations against various state-of-the-art techniques such as Bayesian Optimization and Extreme Gradient Boosting Tree (BOA-XGBoost), Whale Optimization Algorithm and Support Vector Machine (WOA-SVM), Short-Time Fourier Transform and Convolutional Neural Networks (STFT-CNNs), and Variational Modal Decomposition-Multi Scale Fuzzy Entropy-Probabilistic Neural Network (VMD-MFE-PNN) demonstrate the superior performance of the FSCL model. Validation using the Case Western Reserve University dataset (CWRU) confirms the efficacy of the proposed technique, achieving an impressive accuracy of 99.32%. Moreover, the model exhibits robustness against noise, maintaining an average precision of 98.88% and demonstrating recall and F1 scores ranging from 99.00% to 99.89%. Even under conditions of severe noise interference, the FSCL model consistently achieves high accuracy in recognizing the motor’s operational state. This study underscores the FSCL model as a promising approach for enhancing motor fault diagnosis in industrial settings, leveraging the synergistic benefits of traditional signal analysis and deep learning methodologies.
{"title":"Rolling Bearing Fault Diagnosis Based on CNN-LSTM with FFT and SVD","authors":"Muzi Xu, Qianqian Yu, Shichao Chen, Jianhui Lin","doi":"10.3390/info15070399","DOIUrl":"https://doi.org/10.3390/info15070399","url":null,"abstract":"In the industrial sector, accurate fault identification is paramount for ensuring both safety and economic efficiency throughout the production process. However, due to constraints imposed by actual working conditions, the motor state features collected are often limited in number and singular in nature. Consequently, extending and extracting these features pose significant challenges in fault diagnosis. To address this issue and strike a balance between model complexity and diagnostic accuracy, this paper introduces a novel motor fault diagnostic model termed FSCL (Fourier Singular Value Decomposition combined with Long and Short-Term Memory networks). The FSCL model integrates traditional signal analysis algorithms with deep learning techniques to automate feature extraction. This hybrid approach innovatively enhances fault detection by describing, extracting, encoding, and mapping features during offline training. Empirical evaluations against various state-of-the-art techniques such as Bayesian Optimization and Extreme Gradient Boosting Tree (BOA-XGBoost), Whale Optimization Algorithm and Support Vector Machine (WOA-SVM), Short-Time Fourier Transform and Convolutional Neural Networks (STFT-CNNs), and Variational Modal Decomposition-Multi Scale Fuzzy Entropy-Probabilistic Neural Network (VMD-MFE-PNN) demonstrate the superior performance of the FSCL model. Validation using the Case Western Reserve University dataset (CWRU) confirms the efficacy of the proposed technique, achieving an impressive accuracy of 99.32%. Moreover, the model exhibits robustness against noise, maintaining an average precision of 98.88% and demonstrating recall and F1 scores ranging from 99.00% to 99.89%. Even under conditions of severe noise interference, the FSCL model consistently achieves high accuracy in recognizing the motor’s operational state. This study underscores the FSCL model as a promising approach for enhancing motor fault diagnosis in industrial settings, leveraging the synergistic benefits of traditional signal analysis and deep learning methodologies.","PeriodicalId":510156,"journal":{"name":"Information","volume":"126 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrea Cadeddu, Alessandro Chessa, Vincenzo De Leo, Gianni Fenu, Enrico Motta, Francesco Osborne, Diego Reforgiato Recupero, Angelo Salatino, Luca Secchi
Online platforms have become the primary means for travellers to search, compare, and book accommodations for their trips. Consequently, online platforms and revenue managers must acquire a comprehensive comprehension of these dynamics to formulate a competitive and appealing offerings. Recent advancements in natural language processing, specifically through the development of large language models, have demonstrated significant progress in capturing the intricate nuances of human language. On the other hand, knowledge graphs have emerged as potent instruments for representing and organizing structured information. Nevertheless, effectively integrating these two powerful technologies remains an ongoing challenge. This paper presents an innovative deep learning methodology that combines large language models with domain-specific knowledge graphs for classification of tourism offers. The main objective of our system is to assist revenue managers in the following two fundamental dimensions: (i) comprehending the market positioning of their accommodation offerings, taking into consideration factors such as accommodation price and availability, together with user reviews and demand, and (ii) optimizing presentations and characteristics of the offerings themselves, with the intention of improving their overall appeal. For this purpose, we developed a domain knowledge graph covering a variety of information about accommodations and implemented targeted feature engineering techniques to enhance the information representation within a large language model. To evaluate the effectiveness of our approach, we conducted a comparative analysis against alternative methods on four datasets about accommodation offers in London. The proposed solution obtained excellent results, significantly outperforming alternative methods.
{"title":"Optimizing Tourism Accommodation Offers by Integrating Language Models and Knowledge Graph Technologies","authors":"Andrea Cadeddu, Alessandro Chessa, Vincenzo De Leo, Gianni Fenu, Enrico Motta, Francesco Osborne, Diego Reforgiato Recupero, Angelo Salatino, Luca Secchi","doi":"10.3390/info15070398","DOIUrl":"https://doi.org/10.3390/info15070398","url":null,"abstract":"Online platforms have become the primary means for travellers to search, compare, and book accommodations for their trips. Consequently, online platforms and revenue managers must acquire a comprehensive comprehension of these dynamics to formulate a competitive and appealing offerings. Recent advancements in natural language processing, specifically through the development of large language models, have demonstrated significant progress in capturing the intricate nuances of human language. On the other hand, knowledge graphs have emerged as potent instruments for representing and organizing structured information. Nevertheless, effectively integrating these two powerful technologies remains an ongoing challenge. This paper presents an innovative deep learning methodology that combines large language models with domain-specific knowledge graphs for classification of tourism offers. The main objective of our system is to assist revenue managers in the following two fundamental dimensions: (i) comprehending the market positioning of their accommodation offerings, taking into consideration factors such as accommodation price and availability, together with user reviews and demand, and (ii) optimizing presentations and characteristics of the offerings themselves, with the intention of improving their overall appeal. For this purpose, we developed a domain knowledge graph covering a variety of information about accommodations and implemented targeted feature engineering techniques to enhance the information representation within a large language model. To evaluate the effectiveness of our approach, we conducted a comparative analysis against alternative methods on four datasets about accommodation offers in London. The proposed solution obtained excellent results, significantly outperforming alternative methods.","PeriodicalId":510156,"journal":{"name":"Information","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141661092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The transportation of patients in emergency medical situations, particularly in rural areas, often faces significant challenges due to long travel distances and limited access to healthcare facilities. These challenges can result in critical delays in medical care, adversely affecting patient outcomes. Addressing this issue is essential for improving survival rates and health outcomes in underserved regions. This study explored the potential of advanced air mobility to enhance emergency medical services by reducing patient transport times through the strategic placement of vertiports. Using North Dakota as a case study, the research developed a GIS-based optimization workflow to identify optimal vertiport locations that maximize time savings. The study highlighted the benefits of strategic vertiport placement at existing airports and hospital heliports to minimize community disruption and leverage underutilized infrastructure. A key finding was that the optimized mixed-mode routes could reduce patient transport times by up to 21.8 min compared with drive-only routes, significantly impacting emergency response efficiency. Additionally, the study revealed that more than 45% of the populated areas experienced reduced ground travel times due to the integration of vertiports, highlighting the strategic importance of vertiport placement in optimizing emergency medical services. The research also demonstrated the replicability of the GIS-based optimization model for other regions, offering valuable insights for policymakers and stakeholders in enhancing EMS through advanced air mobility solutions.
{"title":"Spatial Analysis of Advanced Air Mobility in Rural Healthcare Logistics","authors":"R. Bridgelall","doi":"10.3390/info15070397","DOIUrl":"https://doi.org/10.3390/info15070397","url":null,"abstract":"The transportation of patients in emergency medical situations, particularly in rural areas, often faces significant challenges due to long travel distances and limited access to healthcare facilities. These challenges can result in critical delays in medical care, adversely affecting patient outcomes. Addressing this issue is essential for improving survival rates and health outcomes in underserved regions. This study explored the potential of advanced air mobility to enhance emergency medical services by reducing patient transport times through the strategic placement of vertiports. Using North Dakota as a case study, the research developed a GIS-based optimization workflow to identify optimal vertiport locations that maximize time savings. The study highlighted the benefits of strategic vertiport placement at existing airports and hospital heliports to minimize community disruption and leverage underutilized infrastructure. A key finding was that the optimized mixed-mode routes could reduce patient transport times by up to 21.8 min compared with drive-only routes, significantly impacting emergency response efficiency. Additionally, the study revealed that more than 45% of the populated areas experienced reduced ground travel times due to the integration of vertiports, highlighting the strategic importance of vertiport placement in optimizing emergency medical services. The research also demonstrated the replicability of the GIS-based optimization model for other regions, offering valuable insights for policymakers and stakeholders in enhancing EMS through advanced air mobility solutions.","PeriodicalId":510156,"journal":{"name":"Information","volume":"23 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A sustainable smart tourism ecosystem relies on building digital networks that link tourists to destinations. This study explores the potential of web and immersive technologies, specifically the Virtual Romania (VRRO) platform, in enhancing sustainable tourism by redirecting tourist traffic to lesser-known destinations and boosting user engagement through interactive experiences. Our research examines how virtual tourism platforms (VTPs), which include web-based and immersive technologies, support sustainable tourism, complement physical visits, influence user engagement, and foster community building through social features and user-generated content (UGC). An empirical analysis of the VRRO platform reveals high user engagement levels, attributed to its intuitive design and interactive features, regardless of the users’ technological familiarity. Our findings also highlight the necessity for ongoing enhancements to maintain user satisfaction. In conclusion, VRRO demonstrates how accessible and innovative technologies in tourism can modernize travel experiences and contribute to the evolution of the broader tourism ecosystem by supporting sustainable practices and fostering community engagement.
{"title":"Virtual Journeys, Real Engagement: Analyzing User Experience on a Virtual Travel Social Platform","authors":"Ana-Karina Nazare, A. Moldoveanu, F. Moldoveanu","doi":"10.3390/info15070396","DOIUrl":"https://doi.org/10.3390/info15070396","url":null,"abstract":"A sustainable smart tourism ecosystem relies on building digital networks that link tourists to destinations. This study explores the potential of web and immersive technologies, specifically the Virtual Romania (VRRO) platform, in enhancing sustainable tourism by redirecting tourist traffic to lesser-known destinations and boosting user engagement through interactive experiences. Our research examines how virtual tourism platforms (VTPs), which include web-based and immersive technologies, support sustainable tourism, complement physical visits, influence user engagement, and foster community building through social features and user-generated content (UGC). An empirical analysis of the VRRO platform reveals high user engagement levels, attributed to its intuitive design and interactive features, regardless of the users’ technological familiarity. Our findings also highlight the necessity for ongoing enhancements to maintain user satisfaction. In conclusion, VRRO demonstrates how accessible and innovative technologies in tourism can modernize travel experiences and contribute to the evolution of the broader tourism ecosystem by supporting sustainable practices and fostering community engagement.","PeriodicalId":510156,"journal":{"name":"Information","volume":"113 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141668044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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