4,042 research outputs found

    Design and implementation of an automatic nursing assessment system based on CDSS technology

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    BACKGROUND: Various quantitative and quality assessment tools are currently used in nursing to evaluate a patient's physiological, psychological, and socioeconomic status. The results play important roles in evaluating the efficiency of healthcare, improving the treatment plans, and lowing relevant clinical risks. However, the manual process of the assessment imposes a substantial burden and can lead to errors in digitalization. To fill these gaps, we proposed an automatic nursing assessment system based on clinical decision support system (CDSS). The framework underlying the CDSS included experts, evaluation criteria, and voting roles for selecting electronic assessment sheets over paper ones.METHODS: We developed the framework based on an expert voting flow to choose electronic assessment sheets. The CDSS was constructed based on a nursing process workflow model. A multilayer architecture with independent modules was used. The performance of the proposed system was evaluated by comparing the adverse events' incidence and the average time for regular daily assessment before and after the implementation.RESULTS: After implementation of the system, the adverse nursing events' incidence decreased significantly from 0.43 % to 0.37 % in the first year and further to 0.27 % in the second year (p-value: 0.04). Meanwhile, the median time for regular daily assessments further decreased from 63 s to 51 s.CONCLUSIONS: The automatic assessment system helps to reduce nurses' workload and the incidence of adverse nursing events.</p

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    Securing NextG networks with physical-layer key generation: A survey

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    As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks

    Authentication enhancement in command and control networks: (a study in Vehicular Ad-Hoc Networks)

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    Intelligent transportation systems contribute to improved traffic safety by facilitating real time communication between vehicles. By using wireless channels for communication, vehicular networks are susceptible to a wide range of attacks, such as impersonation, modification, and replay. In this context, securing data exchange between intercommunicating terminals, e.g., vehicle-to-everything (V2X) communication, constitutes a technological challenge that needs to be addressed. Hence, message authentication is crucial to safeguard vehicular ad-hoc networks (VANETs) from malicious attacks. The current state-of-the-art for authentication in VANETs relies on conventional cryptographic primitives, introducing significant computation and communication overheads. In this challenging scenario, physical (PHY)-layer authentication has gained popularity, which involves leveraging the inherent characteristics of wireless channels and the hardware imperfections to discriminate between wireless devices. However, PHY-layerbased authentication cannot be an alternative to crypto-based methods as the initial legitimacy detection must be conducted using cryptographic methods to extract the communicating terminal secret features. Nevertheless, it can be a promising complementary solution for the reauthentication problem in VANETs, introducing what is known as “cross-layer authentication.” This thesis focuses on designing efficient cross-layer authentication schemes for VANETs, reducing the communication and computation overheads associated with transmitting and verifying a crypto-based signature for each transmission. The following provides an overview of the proposed methodologies employed in various contributions presented in this thesis. 1. The first cross-layer authentication scheme: A four-step process represents this approach: initial crypto-based authentication, shared key extraction, re-authentication via a PHY challenge-response algorithm, and adaptive adjustments based on channel conditions. Simulation results validate its efficacy, especially in low signal-to-noise ratio (SNR) scenarios while proving its resilience against active and passive attacks. 2. The second cross-layer authentication scheme: Leveraging the spatially and temporally correlated wireless channel features, this scheme extracts high entropy shared keys that can be used to create dynamic PHY-layer signatures for authentication. A 3-Dimensional (3D) scattering Doppler emulator is designed to investigate the scheme’s performance at different speeds of a moving vehicle and SNRs. Theoretical and hardware implementation analyses prove the scheme’s capability to support high detection probability for an acceptable false alarm value ≤ 0.1 at SNR ≥ 0 dB and speed ≤ 45 m/s. 3. The third proposal: Reconfigurable intelligent surfaces (RIS) integration for improved authentication: Focusing on enhancing PHY-layer re-authentication, this proposal explores integrating RIS technology to improve SNR directed at designated vehicles. Theoretical analysis and practical implementation of the proposed scheme are conducted using a 1-bit RIS, consisting of 64 × 64 reflective units. Experimental results show a significant improvement in the Pd, increasing from 0.82 to 0.96 at SNR = − 6 dB for multicarrier communications. 4. The fourth proposal: RIS-enhanced vehicular communication security: Tailored for challenging SNR in non-line-of-sight (NLoS) scenarios, this proposal optimises key extraction and defends against denial-of-service (DoS) attacks through selective signal strengthening. Hardware implementation studies prove its effectiveness, showcasing improved key extraction performance and resilience against potential threats. 5. The fifth cross-layer authentication scheme: Integrating PKI-based initial legitimacy detection and blockchain-based reconciliation techniques, this scheme ensures secure data exchange. Rigorous security analyses and performance evaluations using network simulators and computation metrics showcase its effectiveness, ensuring its resistance against common attacks and time efficiency in message verification. 6. The final proposal: Group key distribution: Employing smart contract-based blockchain technology alongside PKI-based authentication, this proposal distributes group session keys securely. Its lightweight symmetric key cryptography-based method maintains privacy in VANETs, validated via Ethereum’s main network (MainNet) and comprehensive computation and communication evaluations. The analysis shows that the proposed methods yield a noteworthy reduction, approximately ranging from 70% to 99%, in both computation and communication overheads, as compared to the conventional approaches. This reduction pertains to the verification and transmission of 1000 messages in total

    Deep Learning Techniques for Electroencephalography Analysis

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    In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes

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    Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown target function under robot embodiment constraints. RIG has many applications, including but not limited to autonomous exploration and mapping, 3D reconstruction or inspection, search and rescue, and environmental monitoring. A RIG system relies on a probabilistic model's prediction uncertainty to identify critical areas for informative data collection. Gaussian Processes (GPs) with stationary kernels have been widely adopted for spatial modeling. However, real-world spatial data is typically non-stationary -- different locations do not have the same degree of variability. As a result, the prediction uncertainty does not accurately reveal prediction error, limiting the success of RIG algorithms. We propose a family of non-stationary kernels named Attentive Kernel (AK), which is simple, robust, and can extend any existing kernel to a non-stationary one. We evaluate the new kernel in elevation mapping tasks, where AK provides better accuracy and uncertainty quantification over the commonly used stationary kernels and the leading non-stationary kernels. The improved uncertainty quantification guides the downstream informative planner to collect more valuable data around the high-error area, further increasing prediction accuracy. A field experiment demonstrates that the proposed method can guide an Autonomous Surface Vehicle (ASV) to prioritize data collection in locations with significant spatial variations, enabling the model to characterize salient environmental features.Comment: International Journal of Robotics Research (IJRR). arXiv admin note: text overlap with arXiv:2205.0642

    Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization

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    The design and optimization of wireless networks have mostly been based on strong mathematical and theoretical modeling. Nonetheless, as novel applications emerge in the era of 5G and beyond, unprecedented levels of complexity will be encountered in the design and optimization of the network. As a result, the use of Artificial Intelligence (AI) is envisioned for wireless network design and optimization due to the flexibility and adaptability it offers in solving extremely complex problems in real-time. One of the main future applications of AI is enabling user-level personalization for numerous use cases. AI will revolutionize the way we interact with computers in which computers will be able to sense commands and emotions from humans in a non-intrusive manner, making the entire process transparent to users. By leveraging this capability, and accelerated by the advances in computing technologies, wireless networks can be redesigned to enable the personalization of network services to the user level in real-time. While current wireless networks are being optimized to achieve a predefined set of quality requirements, the personalization technology advocated in this article is supported by an intelligent big data-driven layer designed to micro-manage the scarce network resources. This layer provides the intelligence required to decide the necessary service quality that achieves the target satisfaction level for each user. Due to its dynamic and flexible design, personalized networks are expected to achieve unprecedented improvements in optimizing two contradicting objectives in wireless networks: saving resources and improving user satisfaction levels

    The association between pre-operative pain experience and post-operative pain in patients undergoing elective gastrointestinal surgery: a descriptive-comparative study

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    Aims: The purpose of the rapid review was to summarize and aggregate information for researchers and clinicians about predisposing factors for post-operative pain in laparoscopic patients and the prevalent management approaches post-operatively. The purpose of the descriptive-comparative study was to explore the associations between previous pain experiences and medication on the intensity of pain post-operatively in patients undergoing elective gastrointestinal surgery, using data collected by the Smart Pain Assessment Tool Based on Internet of Things. Methods: For the rapid review, the databases of PubMed, Web of Science and Embase were searched. ROBINS-I tool was used to evaluate the quality of non-randomized studies while ROB 2 tool was used for randomized controlled trials. For the descriptive-comparative study, 50 patients after gastrointestinal operations at Turku University hospital were included. The data collection of the study was done by a researcher belonging to Turku University staff at Turku University hospital. The data analysis was done by using descriptive and comparative methods of analysis. Descriptive statistics were used for the presentation and analysis of participants outcomes, diagnoses, procedures, and groupings based on variables related to the experience of pain (e.g., graphical measurement maximal pain levels using the numeric rating scale). Comparative statistics were used for associations and correlations regarding previous pain levels, medications, fear, and expectation of pain on maximal pain levels after gastrointestinal operations at Turku University Hospital. Results: The result of the rapid review suggest many predisposing factors for post-operative pain are influenced by the psychological profile of the patient. Among these factors are anxiety, fear, depression, expectation of pain, and other factors related to gastrointestinal surgery. Nevertheless, the results of this review also describe acute pre-operative pain, surgical factors, genetics, age, gender, obesity, and previous experiences of pain as relevant predisposing factors to pain following gastrointestinal surgery. Pain care strategies following gastrointestinal surgery include the use of pharmacological and non-pharmacological interventions. The literature suggests, non-pharmacological interventions are under-utilized and should be encouraged as an adjunct to pharmacological pain control strategies following elective gastrointestinal surgery. The results of the descriptive-comparative study somewhat contradict the results of the rapid review. Previous pain experiences or the recollection of preceding painful events were not associated with the administration of supplemental pain medication post-operatively (p = 0.741). Fear related to the upcoming pain following surgery was not associated with the level of invasiveness of the surgery (p = 0.662). In addition, the relationship between expectation of pain (p = 0.698), fear of pain related to the upcoming surgical procedure (p = 0.637) and medication post-operatively (p = .481) on the intensity of maximal post-operative pain was found to be negligible. The results of this study suggest patient expectation as a possible domain of intervention for better pain outcomes post-operatively. The administration of pain medication in the recovery room and the amount of pain medication in the recovery room were significant predictors of maximal post-operative pain (p = .001). Discussion: The results of the rapid review suggest a high to critical risk of bias in the studies included. The predisposing factors for post-operative pain differed widely across studies, but mainly included psychological factors as factors for post-operative pain. Pain management strategies should include an individualized approach and be implemented before, during and after the operation. For the descriptive-comparative study, there are substantial difficulties in discerning the effect of pain history or experience on post-operative pain using physiological or subjective reporting for conscious individuals due to risk of bias and using a unidimensional approach. Conclusion: Predisposing factors for post-operative pain should be screened in the pre-operative phase if possible, focusing on addressable factors whereas management of pain care strategies should include careful screening of participants biopsychosocial profile for elective surgery. The descriptive-comparative study suggests a possible, yet minimal benefit for managing patients’ expectation of pain related to the upcoming gastrointestinal surgery. The amount of pain medication in the recovery room is a significant predictor of maximal post-operative pain. Future research should include a larger sample, more variables related to pain and continue with a follow-up. Keywords: gastrointestinal, post-operative, pain, analgesia, anesthesiaTavoitteet: Katsauksen tarkoituksena oli tiivistää ja koota yhteen tutkijoille ja kliinikoille tietoa laparoskooppisten potilaiden postoperatiiviselle kivulle altistavista tekijöistä ja vallitsevista postoperatiivisista hoitokeinoista. Kuvailevan-vertailevan tutkimuksen tarkoituksena oli tutkia aiempien kipukokemusten ja lääkityksen välisiä yhteyksiä postoperatiivisen kivun voimakkuuteen potilailla, joille tehdään elektiivinen ruoansulatuskanavan leikkaus, käyttäen tietoja, jotka on kerätty esineiden internetiin perustuvalla älykkäällä kivunarviointityökalulla. Menetelmät: Katsausta varten tehtiin hakuja PubMed-, Web of Science- ja Embase-tietokannoista. ROBINS-I-työkalua käytettiin satunnaistamattomien tutkimusten laadun arviointiin, kun taas satunnaistettujen kontrolloitujen tutkimusten osalta käytettiin ROB 2-työkalua. Kuvailevaan-vertailevaan tutkimukseen otettiin mukaan 50 potilasta Turun yliopistollisessa sairaalassa tehtyjen ruoansulatuskanavan leikkausten jälkeen. Tutkimuksen aineistonkeruun suoritti Turun yliopiston henkilökuntaan kuuluva tutkija Turun yliopistollisessa sairaalassa. Aineiston analysoinnissa käytettiin kuvailevia ja vertailevia analyysimenetelmiä. Kuvailevia tilastoja käytettiin osallistujien tulosten, diagnoosien, toimenpiteiden ja kivun kokemiseen liittyvien muuttujien perusteella tehtyjen ryhmittelyjen esittämiseen ja analysointiin (esim. maksimaalisen kiputason graafinen mittaaminen numeerisella arviointiasteikolla). Vertailevia tilastoja käytettiin yhdistelmiin ja korrelaatioihin, jotka koskivat aiempia kiputiloja, lääkkeitä, pelkoa ja kivun odotusta maksimaalisen kiputason suhteen ruoansulatuskanavan leikkausten jälkeen Turun yliopistollisessa sairaalassa. Tulokset: Katsauksen tulokset viittaavat siihen, että potilaan psykologinen profiili vaikuttaa moniin leikkauksen jälkeiselle kivulle altistaviin tekijöihin. Näihin tekijöihin kuuluvat ahdistus, pelko, masennus, kivun odotus ja muut ruoansulatuskanavan leikkaukseen liittyvät tekijät. Tämän katsauksen tuloksissa kuvataan kuitenkin myös akuutti preoperatiivinen kipu, kirurgiset tekijät, genetiikka, ikä, sukupuoli, lihavuus ja aiemmat kokemukset kivusta merkityksellisinä altistavina tekijöinä ruoansulatuskanavan leikkauksen jälkeiselle kivulle. Ruoansulatuskanavan leikkauksen jälkeisiin kivunhoitostrategioihin kuuluu farmakologisten ja ei-farmakologisten toimenpiteiden käyttö. Kirjallisuuden mukaan ei-farmakologisia toimenpiteitä käytetään liian vähän, ja niitä olisi edistettävä farmakologisten kivunhoitostrategioiden lisänä elektiivisen ruoansulatuskanavan leikkauksen jälkeen. Kuvailevan ja vertailevan tutkimuksen tulokset ovat jossain määrin ristiriidassa nopean katsauksen tulosten kanssa. Aiemmat kipukokemukset tai aiempien kivuliaiden tapahtumien muistaminen eivät olleet yhteydessä ylimääräisen kipulääkityksen antamiseen leikkauksen jälkeen (p = 0,741). Leikkauksen jälkeiseen tulevaan kipuun liittyvä pelko ei ollut yhteydessä leikkauksen invasiivisuuteen (p = 0,662). Lisäksi kivun odotuksen (p = 0,698), tulevaan kirurgiseen toimenpiteeseen liittyvän kivun pelon (p = 0,637) ja leikkauksen jälkeisen lääkityksen (p = 0,481) välinen yhteys maksimaalisen leikkauksen jälkeisen kivun voimakkuuteen todettiin merkityksettömäksi. Tämän tutkimuksen tulokset viittaavat siihen, että potilaan odotukset ovat mahdollinen interventioalue, jolla voidaan parantaa leikkauksen jälkeistä kiputilannetta. Kipulääkityksen antaminen heräämössä ja kipulääkityksen määrä heräämössä olivat merkittäviä postoperatiivisen maksimaalisen kivun ennustajia (p = .001). Pohdinta: Katsauksen tulokset viittaavat siihen, että mukana olleissa tutkimuksissa on suuri tai kriittinen harhan riski. Postoperatiiviselle kivulle altistavat tekijät vaihtelivat suuresti eri tutkimuksissa, mutta niihin sisältyi pääasiassa psykologisia tekijöitä postoperatiivisen kivun tekijöinä. Kivunhoitostrategioihin olisi sisällyttävä yksilöllinen lähestymistapa, ja niitä olisi sovellettava ennen leikkausta, sen aikana ja sen jälkeen. Kuvailevassa ja vertailevassa tutkimuksessa on huomattavia vaikeuksia havaita kipuhistorian tai -kokemuksen vaikutusta leikkauksen jälkeiseen kipuun fysiologisen tai subjektiivisen raportoinnin avulla tietoisten yksilöiden osalta, koska on olemassa harhan riski ja koska käytetään yksiulotteista lähestymistapaa. Johtopäätökset: Kivunhoitostrategioihin olisi kuuluttava osallistujien biopsykososiaalisen profiilin huolellinen seulonta valintaleikkausta varten. Kuvaileva-vertaileva tutkimus viittaa siihen, että potilaiden tulevaan ruoansulatuskanavan leikkaukseen liittyvien kipuodotusten hallinnasta on mahdollista, joskin vähäistä hyötyä. Kipulääkkeiden määrä heräämössä on merkittävä leikkauksen jälkeisen maksimaalisen kivun ennustaja. Tulevaan tutkimukseen olisi sisällytettävä suurempi otos, enemmän kipuun liittyviä muuttujia ja jatkettava seurantaa
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