Journal articles on the topic 'Multi-domain Performance Assessment'
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Kumar, Amit, and Bikash Kanti Sarkar. "Performance Assessment of Learning Algorithms on Multi-Domain Data Sets." International Journal of Knowledge Discovery in Bioinformatics 8, no. 1 (January 2018): 27–41. http://dx.doi.org/10.4018/ijkdb.2018010103.
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This article describes how for the last few decades, data mining research has had significant progress in a wide spectrum of applications. Research in prediction of multi-domain data sets is a challenging task due to the imbalanced, voluminous, conflicting, and complex nature of data sets. A learning algorithm is the most important technique for solving these problems. The learning algorithms are widely used for classification purposes. But choosing the learners that perform best for data sets of particular domains is a challenging task in data mining. This article provides a comparative performance assessment of various state-of-the-art learning algorithms over multi-domain data sets to search the effective classifier(s) for a particular domain, e.g., artificial, natural, semi-natural, etc. In the present article, a total of 14 real world data sets are selected from University of California, Irvine (UCI) machine learning repository for conducting experiments using three competent individual learners and their hybrid combinations.
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Jiao, Yu, Xinpei Wang, Changchun Liu, Han Li, Huan Zhang, Ying Hu, Runkun Liu, and Bing Ji. "Heart Sound Signal Quality Assessment Based on Multi-Domain Features." Journal of Medical Imaging and Health Informatics 10, no. 3 (March 1, 2020): 736–42. http://dx.doi.org/10.1166/jmihi.2020.2926.
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Heart sound is one of the most important physiological signals of our body, including a large number of physiological and pathological information that can reflect the cardiovascular status. This study aims to develop a heart sound signal quality assessment method. In view of the 3 common noises (deep breath, speaking and cough) in clinical data collection, a total of 72 features were extracted from 6 domains, i.e., time, frequency, entropy, energy, high-order statistics and cyclostationarity. Then information gain, which was used as feature selection method, as well as statistical analysis were employed for dimension reduction. A SVM with radial basis kernel function was trained for final signal quality classification. The best effect was obtained on distinguishing resting from cough and the result showed that the classification performance was significantly improved after feature selection. In contrast, statistical analysis had little effect on the improvement of classification results. The best accuracy in distinguishing between resting and deep breath, resting and speaking, resting and cough is 87.73%, 95.00%, 98.64%, respectively. These results indicate that the proposed method is effective for identifying different noise states, namely cough, speaking and deep breath.
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Seelye, Adriana M., Maureen Schmitter-Edgecombe, Diane J. Cook, and Aaron Crandall. "Naturalistic Assessment of Everyday Activities and Prompting Technologies in Mild Cognitive Impairment." Journal of the International Neuropsychological Society 19, no. 4 (January 25, 2013): 442–52. http://dx.doi.org/10.1017/s135561771200149x.
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AbstractOlder adults with mild cognitive impairment (MCI) often have difficulty performing complex instrumental activities of daily living (IADLs), which are critical to independent living. In this study, amnestic multi-domain MCI (N = 29), amnestic single-domain MCI (N = 18), and healthy older participants (N = 47) completed eight scripted IADLs (e.g., cook oatmeal on the stove) in a smart apartment testbed. We developed and experimented with a graded hierarchy of technology-based prompts to investigate both the amount of prompting and type of prompts required to assist individuals with MCI in completing the activities. When task errors occurred, progressive levels of assistance were provided, starting with the lowest level needed to adjust performance. Results showed that the multi-domain MCI group made more errors and required more prompts than the single-domain MCI and healthy older adult groups. Similar to the other two groups, the multi-domain MCI group responded well to the indirect prompts and did not need a higher level of prompting to get back on track successfully with the tasks. Need for prompting assistance was best predicted by verbal memory abilities in multi-domain amnestic MCI. Participants across groups indicated that they perceived the prompting technology to be very helpful. (JINS, 2013, 19, 1–11)
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Ford, Thomas, David Meyer, John Colombi, Brian Scheller, and Cody Palmer. "A method of assessing the time-variant value of multi-domain architectures." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 15, no. 4 (June 29, 2016): 471–81. http://dx.doi.org/10.1177/1548512916656291.
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Modeling and simulation continues to improve the ability to evaluate future complex concepts and conduct early systems analysis. While the systems engineering community now advocates model-based systems engineering, the actual integrated methods and tools need to be refined. This research synthesizes descriptive architectural depictions of multi-domain (air and space) concepts, simulates these concepts through a physics-based model, collects several performance metrics, and integrates them using value-focused thinking. The proposed method enables the assessment of alternatives whose value can increase or decrease over time, depending on the design properties and operational scenario. Three surveillance concepts are proposed, made up of a low-earth orbit satellite constellation, with varying payloads, and extended by an unmanned air system flying waypoints in the area of interest. Uniquely, this method shows promise of depicting time-variant value assessments of these concepts across the simulation.
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Liu, Tao, Xing Wu, Yu Guo, and Chang Liu. "BEARING PERFORMANCE DEGRADATION ASSESSMENT BY ORTHOGONAL LOCAL PRESERVING PROJECTION AND CONTINUOUS HIDDEN MARKOV MODEL." Transactions of the Canadian Society for Mechanical Engineering 40, no. 5 (December 2016): 1019–30. http://dx.doi.org/10.1139/tcsme-2016-0084.
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Bearing is the key component in rotating machine. It is important to assess the performance degradation degree of bearings for making proactive maintenance and realizing near-zero downtime. A methodology based on orthogonal local preserving projection (OLPP) and continuous hidden Markov model (CHMM) is introduced in bearing performance degradation assessment. Firstly, the time domain, frequency domain and time-frequency domain features are extracted from the vibration signals. Then, the multi-dimensional features are reduced by OLPP. And the selection of the adjacent paragraph parameters in OLPP is optimized adaptively by minimizing the ratio of between-class distance to within-class distance. A CHMM is trained by using the reduced feature in normal condition. At last, the test bearing data are input into the pre-trained CHMM to calculate the log-likelihood of the test data, which can assess the performance degradation of bearings quantitatively. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method.
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Bethge, David, Luis Falconeri Coelho, Thomas Kosch, Satiyabooshan Murugaboopathy, Ulrich von Zadow, Albrecht Schmidt, and Tobias Grosse-Puppendahl. "Technical Design Space Analysis for Unobtrusive Driver Emotion Assessment Using Multi-Domain Context." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 4 (December 21, 2022): 1–30. http://dx.doi.org/10.1145/3569466.
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Driver emotions play a vital role in driving safety and performance. Consequently, regulating driver emotions through empathic interfaces have been investigated thoroughly. However, the prerequisite - driver emotion sensing - is a challenging endeavor: Body-worn physiological sensors are intrusive, while facial and speech recognition only capture overt emotions. In a user study (N=27), we investigate how emotions can be unobtrusively predicted by analyzing a rich set of contextual features captured by a smartphone, including road and traffic conditions, visual scene analysis, audio, weather information, and car speed. We derive a technical design space to inform practitioners and researchers about the most indicative sensing modalities, the corresponding impact on users' privacy, and the computational cost associated with processing this data. Our analysis shows that contextual emotion recognition is significantly more robust than facial recognition, leading to an overall improvement of 7% using a leave-one-participant-out cross-validation.
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Yang, Chuanlei, Hechun Wang, Zhanbin Gao, and Xinjie Cui. "Improving rolling bearing online fault diagnostic performance based on multi-dimensional characteristics." Royal Society Open Science 5, no. 5 (May 2018): 180066. http://dx.doi.org/10.1098/rsos.180066.
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As the main cause of failure and damage to rotating machinery, rolling bearing failure can result in huge economic losses. As the rolling bearing vibration signal is nonlinear and has non-stationary characteristics, the health status information distributed in the rolling bearing vibration signal is complex. Using common time-domain or frequency-domain approaches cannot easily enable an accurate assessment of rolling bearing health. In this paper, a novel rolling bearing fault diagnostic method based on multi-dimensional characteristics was developed to meet the requirements for accurate diagnosis of different fault types and severities with real-time computational performance. First, a multi-dimensional feature extraction algorithm based on entropy characteristics, Holder coefficient characteristics and improved generalized fractal box-counting dimension characteristics was performed to extract the health status feature vectors from the bearing vibration signals. Second, a grey relation algorithm was employed to achieve bearing fault pattern recognition intelligently using the extracted multi-dimensional feature vector. This experimental study has illustrated that the proposed method can effectively recognize different fault types and severities after integration of the improved fractal box-counting dimension into the multi-dimensional characteristics, in comparison with existing pattern recognition methods.
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Agarwal, Mansi, Maitree Leekha, Ramit Sawhney, and Rajiv Ratn Shah. "Crisis-DIAS: Towards Multimodal Damage Analysis - Deployment, Challenges and Assessment." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 346–53. http://dx.doi.org/10.1609/aaai.v34i01.5369.
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In times of a disaster, the information available on social media can be useful for several humanitarian tasks as disseminating messages on social media is quick and easily accessible. Disaster damage assessment is inherently multi-modal, yet most existing work on damage identification has focused solely on building generic classification models that rely exclusively on text or image analysis of online social media sessions (e.g., posts). Despite their empirical success, these efforts ignore the multi-modal information manifested in social media data. Conventionally, when information from various modalities is presented together, it often exhibits complementary insights about the application domain and facilitates better learning performance. In this work, we present Crisis-DIAS, a multi-modal sequential damage identification, and severity detection system. We aim to support disaster management and aid in planning by analyzing and exploiting the impact of linguistic cues on a unimodal visual system. Through extensive qualitative, quantitative and theoretical analysis on a real-world multi-modal social media dataset, we show that the Crisis-DIAS framework is superior to the state-of-the-art damage assessment models in terms of bias, responsiveness, computational efficiency, and assessment performance.
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Muhammad, Naik, Saeed Ullah Jan Mandokhai, Zafar Baloch, Muhammad Habib, and Shamsher Sadiq. "ASSESSMENT OF SEISMIC PERFORMANCE OF SUBMERGED FLOATING TUNNEL UNDER MULTI-SUPPORT SEISMIC EXCITATIONS." NED University Journal of Research 3, Special Issue on First SACEE'19 (December 12, 2019): 217–24. http://dx.doi.org/10.35453/nedjr-stmech-2019-0025.
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Submerged floating tunnel (SFT) is an innovative structural solution for the transportation infrastructure through sea straits, fjords, and inland waters and is an economical alternative to the long-span cable-supported bridge, immersed tunnel, or underground tunnel. An SFT is a massive cylindrical structure that floats at a certain depth below the water surface and subjected to extreme environmental conditions, such as waves, tsunamis and earthquakes. The seismic assessment of SFT supported by mooring cables, under multi-support seismic excitations needs to be evaluated in more detail. The time domain dynamic problem of SFT moored by inclined mooring cables/anchors is formulated and the geometric non-linear dynamic analysis of SFT subjected to hydrodynamic and seismic excitations is performed in this paper. The prototype of SFT to be constructed in Qindao Lake of China is analysed under multi-support seismic excitations. It was found that due to the fluid environment and flexible cables the effective seismic forces are dissipated well and SFT is stable under the spectrum compatible ground motions. However, the lateral component of seismic excitations produce larger responses compared to longitudinal one and may be critical for other case studies.
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Mukherjee, S., B. Quilty, H. Burstow, and K. Hennessy. "AB0808 IMPLEMENTING IPad-BASED ASSESSMENTS TO IMPROVE PERFORMANCE IN A PSORIATIC ARTHRITIS CLINIC AT A DISTRICT GENERAL HOSPITAL." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 1705.1–1706. http://dx.doi.org/10.1136/annrheumdis-2020-eular.1111.
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Background:Psoriatic Arthritis (PsA) is a complex disease with profound physical and psychosocial effects. The core domain set for this condition was updated by the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)-Outcome Measures in Rheumatology (OMERACT) PsA working group in 20161and the TICOPA (TIght COntrol of Psoriatic Arthritis) study suggested that adopting a ‘treat-to-target’ approach aiming for Minimal Disease Activity (MDA) could result in better clinical outcomes2.Objectives:To improve assessment of all the core domains of PsA during clinic appointments and aim to treat these patients using a ‘treat-to-target’ approach to improve clinical outcomes.Methods:We were able to confirm through a retrospective baseline audit that all core domains of PsA were not being fully addressed in our general rheumatology clinics. A dedicated weekly PsA clinic was then set up at our district general hospital. Subsequently, iPads incorporated with GRAPPA App were implemented in these clinics to facilitate multi-domain assessments aiming for MDA. This was supported by a Health Education England (Wessex) Quality Improvement Fellowship that involved rheumatology and dermatology team members working in close collaboration. We then carried out a re-audit to assess our performance. Additionally we set up quarterly combined Rheumatology and Dermatology clinics for patients with severe joint and skin involvement. We also conducted a baseline survey by asking patients for their opinion about the ‘setting up of the dedicated PsA service’, the ‘quarterly combined clinics’ and the ‘use of iPad-based assessments’. We asked them to score each of these on a scale of 0 to 10, with 0 being ‘very negative’ and 10 ‘very positive’.Results:We had pragmatically set a standard of 75% for our baseline audit but we found an overall compliance of only 27.4%. There was also a wide variation between different domains with a compliance of even 0% for some. Domains that are not assessed are unlikely to be fully taken into account when deciding about treatment. The re-audit following the implementation of iPad-based assessments in dedicated PsA clinics showed a significant improvement in each of the domains and the overall compliance went up to 97.9% (Table 1). The patient survey findings were also excellent with mean scores of 9.5, 9.0 and 9.5 respectively for the three items (Figure 1).Table. 1Audit(n = 25)Re-audit(n = 29)ComplianceQ1. Documented evidence of joint count being performed?84.0%100.0%Q2. If yes, then was it a 66/68 count for swollen and tender joints?4.7%100.0%Q3. Documented evidence of dactylitis being assessed?8.0%100.0%Q4. Documented evidence of enthesitis being assessed?4.0%100%Q5. Documented evidence of assessment of spinal involvement?8.0%100.0%Q6. Documented evidence of assessment of skin involvement?36.0%100.0%Q7. Documented evidence of assessment of nail involvement?4.0%93.1%Q8. Documented evidence of assessment of fatigue?8.0%96.6%Q9. Documented evidence of assessment of degree of pain?60.0%100.0%Q10. Documented evidence of patient’s global assessment?68.0%96.6%Q11. Documented evidence of assessment of physical function?0.0%96.6%Q12. Documented evidence of assessment of health-related quality of life?0.0%93.1%Q13. Documented evidence of assessment of systemic inflammation?72.0%96.6%Overall Compliance27.4%97.9%Conclusion:Dedicated PsA clinics using the GRAPPA App on iPads could facilitate comprehensive multi-domain assessments of patients with PsA and potentially lead to better outcomes as well as greater patient satisfaction.References:[1]Orbai AMet al., (2017) Updating the psoriatic arthritis (PsA) core domain set: a report from the PsA workshop at OMERACT 2016. J Rheumatol 44(10):1522–1528[2]Coates LCet al., Effect of tight control of inflammation in early psoriatic arthritis (TICOPA): a UK multicentre, open-label, randomised controlled trial. Lancet. 2015; 386(10012):2489–2498AcknowledgmentsDisclosure of Interests:None declared
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Liu, Jun, Quansheng Zang, Fan Yang, Jing Zhang, and Gao Lin. "Performance assessment of internal porous structures on liquid sloshing in various 3D tanks by multi-domain IGABEM." Applied Mathematics and Computation 438 (February 2023): 127621. http://dx.doi.org/10.1016/j.amc.2022.127621.
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Zhang, Chen, Luis Fernando D'Haro, Thomas Friedrichs, and Haizhou Li. "MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11657–66. http://dx.doi.org/10.1609/aaai.v36i10.21420.
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Chatbots are designed to carry out human-like conversations across different domains, such as general chit-chat, knowledge exchange, and persona-grounded conversations. To measure the quality of such conversational agents, a dialogue evaluator is expected to conduct assessment across domains as well. However, most of the state-of-the-art automatic dialogue evaluation metrics (ADMs) are not designed for multi-domain evaluation. We are motivated to design a general and robust framework, MDD-Eval, to address the problem. Specifically, we first train a teacher evaluator with human-annotated data to acquire a rating skill to tell good dialogue responses from bad ones in a particular domain and then, adopt a self-training strategy to train a new evaluator with teacher-annotated multi-domain data, that helps the new evaluator to generalize across multiple domains. MDD-Eval is extensively assessed on six dialogue evaluation benchmarks. Empirical results show that the MDD-Eval framework achieves a strong performance with an absolute improvement of 7% over the state-of-the-art ADMs in terms of mean Spearman correlation scores across all the evaluation benchmarks.
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Kolk, Nanja J., Marise Ph Born, and Henk van der Flier. "Three method factors explaining the low correlations between assessment center dimension ratings and scores on personality inventories." European Journal of Personality 18, no. 2 (March 2004): 127–41. http://dx.doi.org/10.1002/per.504.
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In general, correlations between assessment centre (AC) ratings and personality inventories are low. In this paper, we examine three method factors that may be responsible for these low correlations: differences in (i) rating source (other versus self), (ii) rating domain (general versus specific), and (iii) rating format (multi‐ versus single item). This study tests whether these three factors diminish correlations between AC exercise ratings and external indicators of similar dimensions. Ratings of personality and performance were combined in an analytical framework following a 2 × 2 × 2 (source, domain, format) completely crossed, within subjects design. Results showed partial support for the influence of each of the three method factors. Implications for future research are discussed. Copyright © 2004 John Wiley & Sons, Ltd.
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Petkus, Andrew J., Diana Younan, Xinhui Wang, Daniel P. Beavers, Mark A. Espeland, Margaret Gatz, Tara Gruenewald, et al. "Associations Between Air Pollution Exposure and Empirically Derived Profiles of Cognitive Performance in Older Women." Journal of Alzheimer's Disease 84, no. 4 (December 7, 2021): 1691–707. http://dx.doi.org/10.3233/jad-210518.
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Background: Elucidating associations between exposures to ambient air pollutants and profiles of cognitive performance may provide insight into neurotoxic effects on the aging brain. Objective: We examined associations between empirically derived profiles of cognitive performance and residential concentrations of particulate matter of aerodynamic diameter < 2.5 (PM2.5) and nitrogen dioxide (NO2) in older women. Method: Women (N = 2,142) from the Women’s Health Initiative Study of Cognitive Aging completed a neuropsychological assessment measuring attention, visuospatial, language, and episodic memory abilities. Average yearly concentrations of PM2.5 and NO2 were estimated at the participant’s addresses for the 3 years prior to the assessment. Latent profile structural equation models identified subgroups of women exhibiting similar profiles across tests. Multinomial regressions examined associations between exposures and latent profile classification, controlling for covariates. Result: Five latent profiles were identified: low performance across multiple domains (poor multi-domain; n = 282;13%), relatively poor verbal episodic memory (poor memory; n = 216; 10%), average performance across all domains (average multi-domain; n = 974; 45%), superior memory (n = 381; 18%), and superior attention (n = 332; 15%). Using women with average cognitive ability as the referent, higher PM2.5 (per interquartile range [IQR] = 3.64μg/m3) was associated with greater odds of being classified in the poor memory (OR = 1.29; 95% Confidence Interval [CI] = 1.10–1.52) or superior attention (OR = 1.30; 95% CI = 1.10–1.53) profiles. NO2 (per IQR = 9.86 ppb) was associated with higher odds of being classified in the poor memory (OR = 1.38; 95% CI = 1.17–1.63) and lower odds of being classified with superior memory (OR = 0.81; 95% CI = 0.67–0.97). Conclusion: Exposure to PM2.5 and NO2 are associated with patterns of cognitive performance characterized by worse verbal episodic memory relative to performance in other domains.
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LaFlair, Geoffrey T., and Shelley Staples. "Using corpus linguistics to examine the extrapolation inference in the validity argument for a high-stakes speaking assessment." Language Testing 34, no. 4 (September 19, 2017): 451–75. http://dx.doi.org/10.1177/0265532217713951.
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Investigations of the validity of a number of high-stakes language assessments are conducted using an argument-based approach, which requires evidence for inferences that are critical to score interpretation (Chapelle, Enright, & Jamieson, 2008b; Kane, 2013). The current study investigates the extrapolation inference for a high-stakes test of spoken English, the Michigan English Language Assessment Battery (MELAB) speaking task. This inference requires evidence that supports the inferential step from observations of what test takers can do on an assessment to what they can do in the target domain (Chapelle et al., 2008b; Kane, 2013). Typically, the extrapolation inference has been supported by evidence from a criterion measure of language ability. This study proposes an additional empirical method, namely corpus-based register analysis (Biber & Conrad, 2009), which provides a quantitative framework for examining the linguistic relationship between performance assessments and the domains to which their scores are extrapolated. This approach extends Bachman and Palmer’s (2010) focus on the target language use (TLU) domain analysis in their study of assessment use arguments by providing a quantitative approach for the study of language. We first explain the connections between corpus-based register analysis and TLU analysis. Second, an investigation of the MELAB speaking task compares the language of test-taker responses to the language of academic, professional, and conversational spoken registers, or TLU domains. Additionally, the language features at different performance levels within the MELAB speaking task are investigated to determine the relationship between test takers’ scores and their language use in the task. Following previous studies using corpus-based register analysis, we conduct a multi-dimensional (MD) analysis for our investigation. The comparison of the language features from the MELAB with the language of TLU domains revealed that support for the extrapolation inference varies across dimensions of language use.
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Ferocino, Edoardo, Antonio Pifferi, Simon Arridge, Fabrizio Martelli, Paola Taroni, and Andrea Farina. "Multi Simulation Platform for Time Domain Diffuse Optical Tomography: An Application to a Compact Hand-Held Reflectance Probe." Applied Sciences 9, no. 14 (July 17, 2019): 2849. http://dx.doi.org/10.3390/app9142849.
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Time Domain Diffuse Optical Tomography (TD-DOT) enables a full 3D reconstruction of the optical properties of tissue, and could be used for non-invasive and cost-effective in-depth body exploration (e.g., thyroid and breast imaging). Performance quantification is crucial for comparing results coming from different implementations of this technique. A general-purpose simulation platform for TD-DOT clinical systems was developed with a focus on performance assessment through meaningful figures of merit. The platform was employed for assessing the feasibility and characterizing a compact hand-held probe for breast imaging and characterization in reflectance geometry. Important parameters such as hardware gating of the detector, photon count rate and inclusion position were investigated. Results indicate a reduced error (<10%) on the absorption coefficient quantification of a simulated inclusion up to 2-cm depth if a photon count rate ≥ 106 counts per second is used along with a good localization (error < 1 mm down to 25 mm-depth).
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Reina, Giulio, Annalisa Milella, and Rainer Worst. "LIDAR and stereo combination for traversability assessment of off-road robotic vehicles." Robotica 34, no. 12 (June 15, 2015): 2823–41. http://dx.doi.org/10.1017/s0263574715000442.
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SUMMARYReliable assessment of terrain traversability using multi-sensory input is a key issue for driving automation, particularly when the domain is unstructured or semi-structured, as in natural environments. In this paper, LIDAR-stereo combination is proposed to detect traversable ground in outdoor applications. The system integrates two self-learning classifiers, one based on LIDAR data and one based on stereo data, to detect the broad class of drivable ground. Each single-sensor classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the classifier automatically learns to associate geometric appearance of 3D data with class labels. Then, it makes predictions based on past observations. The output obtained from the single-sensor classifiers are statistically combined in order to exploit their individual strengths and reach an overall better performance than could be achieved by using each of them separately. Experimental results, obtained with a test bed platform operating in rural environments, are presented to validate and assess the performance of this approach, showing its effectiveness and potential applicability to autonomous navigation in outdoor contexts.
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Vouyoukas, Constantinos. "An Investigation on the Accuracy of Teachers’ Judgments on Students’ School Achievement." International Journal of Education 14, no. 3 (August 23, 2022): 116. http://dx.doi.org/10.5296/ije.v14i3.20008.
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Teacher-based assessment of students’ school achievement is a process in which collection of information on students’ performance is needed to allow teachers to understand what and how students achieve. This process involves the development of teachers’ judgments. This study investigated the predictive utility of an informal teacher-made achievement test on teachers’ judgments to students’ academic performance. The study involved 166 primary school students (mean age 11.7) and their respective 46 teachers. Main findings indicated that: 1. teachers’ general judgments did not distinguish the difference between students with low achievement and underachievement, and 2. teachers’ judgments with the use of an academic performance rating scale could differentiate low achieving from underachieving students. It is suggested that the accuracy of teachers’ judgments on students’ school achievement is increased when domain-specific and multi-point scales of students’ academic performance and behavior are used.
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Muttakin, Fitriani, Jui-Tang Wang, Mulyanto Mulyanto, and Jenq-Shiou Leu. "Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment." Electronics 11, no. 1 (December 30, 2021): 114. http://dx.doi.org/10.3390/electronics11010114.
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Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data with high-dimensional features. In contrast, not all features are needed by machine learning, and in many cases, high-dimensional features decrease the performance of machine learning. The feature selection method is one of the appropriate approaches to reducing the features to ensure machine learning works efficiently. Various selection methods have been proposed, but research to determine the essential subset feature in psychosocial education has not been established thus far. This research investigated and proposed methods to determine the best feature selection method in the domain of psychosocial education. We used a multi-criteria decision system (MCDM) approach with Additive Ratio Assessment (ARAS) to rank seven feature selection methods. The proposed model evaluated the best feature selection method using nine criteria from the performance metrics provided by machine learning. The experimental results showed that the ARAS is promising for evaluating and recommending the best feature selection method for psychosocial education data using the teacher’s psychosocial risk levels dataset.
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Bayanlou, M. R., and M. Khoshboresh-Masouleh. "MULTI-TASK LEARNING FROM FIXED-WING UAV IMAGES FOR 2D/3D CITY MODELLING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 1–5. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-1-2021.
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Abstract. Single-task learning in artificial neural networks will be able to learn the model very well, and the benefits brought by transferring knowledge thus become limited. In this regard, when the number of tasks increases (e.g., semantic segmentation, panoptic segmentation, monocular depth estimation, and 3D point cloud), duplicate information may exist across tasks, and the improvement becomes less significant. Multi-task learning has emerged as a solution to knowledge-transfer issues and is an approach to scene understanding which involves multiple related tasks each with potentially limited training data. Multi-task learning improves generalization by leveraging the domain-specific information contained in the training data of related tasks. In urban management applications such as infrastructure development, traffic monitoring, smart 3D cities, and change detection, automated multi-task data analysis for scene understanding based on the semantic, instance, and panoptic annotation, as well as monocular depth estimation, is required to generate precise urban models. In this study, a common framework for the performance assessment of multi-task learning methods from fixed-wing UAV images for 2D/3D city modelling is presented.
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Sabo, Aliyu, Noor Izzri Abdul Wahab, Mohammad Lutfi Othman, Mai Zurwatul Ahlam Mohd Jaffar, Hakan Acikgoz, and Hamzeh Beiranvand. "Application of Neuro-Fuzzy Controller to Replace SMIB and Interconnected Multi-Machine Power System Stabilizers." Sustainability 12, no. 22 (November 18, 2020): 9591. http://dx.doi.org/10.3390/su12229591.
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In this research, an effective application and performance assessment of the Neuro-Fuzzy Controller (NFC) damping controller is designed to replace a single machine infinite bus (SMIB) power system stabilizer (PSS), and coordinated multi PSSs in large interconnected power systems are presented. The limitation of the conventional PSSs on SMIB and interconnected multi-machine test power systems are exposed and disclosed by the proposed NFC stabilizer. The NFC is a nonlinear robust controller which does not require a mathematical model of the test power system to be controlled, unlike the conventional PSSs’ damping controller. The Proposed NFC is designed to improve the stability of SMIB, an interconnected IEEE 3-machine, 9-bus power system, and an interconnected two-area 10-machine system of 39-bus New England IEEE test power system under multiple operating conditions. The proposed NFC damping controller performance is compared with the conventional PSS damping controller to confirm the capability of the proposed stabilizer and realize an improved system stability enhancement. The conventional PSSs’ design problem is transformed into an optimization problem where an eigenvalue-based objective function is developed and applied to design the SMIB-PSS and the interconnected multi-machine PSSs. The time-domain phasor simulation was done in the SIMULINK domain, and the simulation results show that the transient responses of the system rise time, settling time, peak time, and peak magnitude were all impressively improved by an acceptable amount for all the test system with the proposed NFC stabilizer. Thus, the NFC was able to effectively control the LFOs and produce an enhanced performance compared to the conventional PSS damping controller. Similarly, the result validates the effectiveness of the proposed NFC damping controller for LFO control, which demonstrates more robustness and efficiency than the classical PSS damping controller. Therefore, the application and performance of the NFC has appeared as a promising method and can be considered as a remarkable method for the optimal design damping stabilizer for small and large power systems.
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Behrendt, Finn, Debayan Bhattacharya, Julia Krüger, Roland Opfer, and Alexander Schlaefer. "Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs." Current Directions in Biomedical Engineering 8, no. 1 (July 1, 2022): 34–37. http://dx.doi.org/10.1515/cdbme-2022-0009.
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Abstract Radiographs are a versatile diagnostic tool for the detection and assessment of pathologies, for treatment planning or for navigation and localization purposes in clinical interventions. However, their interpretation and assessment by radiologists can be tedious and error-prone. Thus, a wide variety of deep learning methods have been proposed to support radiologists interpreting radiographs. Mostly, these approaches rely on convolutional neural networks (CNN) to extract features from images. Especially for the multi-label classification of pathologies on chest radiographs (Chest X-Rays, CXR), CNNs have proven to be well suited. On the Contrary, Vision Transformers (ViTs) have not been applied to this task despite their high classification performance on generic images and interpretable local saliency maps which could add value to clinical interventions. ViTs do not rely on convolutions but on patch-based self-attention and in contrast to CNNs, no prior knowledge of local connectivity is present. While this leads to increased capacity, ViTs typically require an excessive amount of training data which represents a hurdle in the medical domain as high costs are associated with collecting large medical data sets. In this work, we systematically compare the classification performance of ViTs and CNNs for different data set sizes and evaluate more data-efficient ViT variants (DeiT). Our results show that while the performance between ViTs and CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a reasonably large data set is available for training.
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Zhang, Huiling, Zhendong Bei, Wenhui Xi, Min Hao, Zhen Ju, Konda Mani Saravanan, Haiping Zhang, Ning Guo, and Yanjie Wei. "Evaluation of residue-residue contact prediction methods: From retrospective to prospective." PLOS Computational Biology 17, no. 5 (May 24, 2021): e1009027. http://dx.doi.org/10.1371/journal.pcbi.1009027.
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Sequence-based residue contact prediction plays a crucial role in protein structure reconstruction. In recent years, the combination of evolutionary coupling analysis (ECA) and deep learning (DL) techniques has made tremendous progress for residue contact prediction, thus a comprehensive assessment of current methods based on a large-scale benchmark data set is very needed. In this study, we evaluate 18 contact predictors on 610 non-redundant proteins and 32 CASP13 targets according to a wide range of perspectives. The results show that different methods have different application scenarios: (1) DL methods based on multi-categories of inputs and large training sets are the best choices for low-contact-density proteins such as the intrinsically disordered ones and proteins with shallow multi-sequence alignments (MSAs). (2) With at least 5L (L is sequence length) effective sequences in the MSA, all the methods show the best performance, and methods that rely only on MSA as input can reach comparable achievements as methods that adopt multi-source inputs. (3) For top L/5 and L/2 predictions, DL methods can predict more hydrophobic interactions while ECA methods predict more salt bridges and disulfide bonds. (4) ECA methods can detect more secondary structure interactions, while DL methods can accurately excavate more contact patterns and prune isolated false positives. In general, multi-input DL methods with large training sets dominate current approaches with the best overall performance. Despite the great success of current DL methods must be stated the fact that there is still much room left for further improvement: (1) With shallow MSAs, the performance will be greatly affected. (2) Current methods show lower precisions for inter-domain compared with intra-domain contact predictions, as well as very high imbalances in precisions between intra-domains. (3) Strong prediction similarities between DL methods indicating more feature types and diversified models need to be developed. (4) The runtime of most methods can be further optimized.
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Yang, Yong, Shuying Huang, Junfeng Gao, and Zhongsheng Qian. "Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm." Measurement Science Review 14, no. 2 (April 1, 2014): 102–8. http://dx.doi.org/10.2478/msr-2014-0014.
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Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.
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Wang, Anchen, Ying Zhang, and Hongfu Zuo. "Assessing the Performance Degradation of Lithium-Ion Batteries Using an Approach Based on Fusion of Multiple Feature Parameters." Mathematical Problems in Engineering 2019 (May 19, 2019): 1–12. http://dx.doi.org/10.1155/2019/3091071.
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A method based on fusion of multiple features is proposed to assess and accurately describe the performance degradation of lithium-ion batteries in this paper. First, the discharge voltage signal of lithium-ion batteries under real-time monitoring is analyzed from the perspective of time domain and complexity to obtain the values of multiple features. Then, the multi-feature parameters undergo a spectral regression process to reduce the number of dimensions and to eliminate redundancy, and on the basis of this regression, a Gaussian mixture model is established to model the health state of batteries. Thus, the degree of lithium-ion battery performance degradation can be quantitatively assessed using the Bayesian inference-based distance metric. A case calculation experiment is carried out to verify the effectiveness of the method proposed in this paper. The experimental results demonstrate that, compared with other assessment methods, the performance degradation assessment method proposed in this paper can be used to monitor the degradation process of lithium-ion batteries more effectively and to improve the accuracy of condition monitoring of batteries, thereby providing powerful support for making maintenance decisions.
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Taylor, Morag, Annika Toots, Jacqueline C. T. Close, Kim Van Schooten, Matthew Brodie, and Kim Delbaere. "162 Walking, Thinking and Daily-Life Activity in Older People with Dementia." Age and Ageing 48, Supplement_4 (December 2019): iv34—iv39. http://dx.doi.org/10.1093/ageing/afz164.162.
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Abstract Gait has been linked with cognition in cognitively healthy and impaired older people. However, the studies in cognitively impaired people have been small, have not contrasted the strength of cognitive domain associations and have reported inconsistent findings. We examined baseline data of 309 older people with mild-moderate cognitive impairment (age 82±6 years; 47% female) who were participating in a large fall prevention randomised controlled trial. Gait speed was measured at usual pace over 2.4m and cognitive performance was assessed with the Addenbrooke’s Cognitive Examination-III (ACE-III). The ACE-III assesses cognitive domain performance (attention; memory; verbal fluency; language; visuospatial ability). Executive function (EF) was additionally examined using the Frontal Assessment Battery (FAB). Each cognitive domain was associated with gait speed in separate models adjusted for confounders. EF (verbal fluency and the FAB) demonstrated the strongest association which withstood adjustment for attention, memory, language and visuospatial ability. In contrast, visuospatial ability was the only cognitive domain to withstand adjustment for EF (verbal fluency, not the FAB). These findings support higher-order gait regulation. Characterising individuals at risk of negative health outcomes can assist in identifying effective prevention strategies. Forty-five older people with mild-moderate dementia were age-sex matched to two (n=90) healthy controls and all participants (age 81±6 years, 42% female) wore triaxial accelerometers (MoveMonitor, McRoberts) on their lower back for 7-days. Daily-life gait quantity and quality were estimated from the MoveMonitor. Steady-state clinical walking speed was assessed at usual pace over 2.4 to 10.0m. Participants with dementia had reduced gait quantity, slower clinical, habitual daily-life and maximum daily-life walking speeds and multi-domain gait impairment compared to controls. In participants with dementia, clinical walking speed more closely represented habitual daily-life walking speed, whereas in controls, it more closely represented maximum daily-life walking speed. These findings have assessment, functional (e.g. crossing roads) and training/treatment implications.
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Anjum, Ahmad, Ding, Shangguan, Zaman, Ijaz, Sarwar, Han, and Yang. "Assessment of IMERG-V06 Precipitation Product over Different Hydro-Climatic Regimes in the Tianshan Mountains, North-Western China." Remote Sensing 11, no. 19 (October 4, 2019): 2314. http://dx.doi.org/10.3390/rs11192314.
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This study presents an assessment of the version-6 (V06) of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product from June 2014 to December 2017 over different hydro-climatic regimes in the Tianshan Mountains. The performance of IMERG-V06 was compared with IMERG-V05 and the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 precipitation products. The precipitation products were assessed against gauge-based daily and monthly precipitation observations over the entire spatial domain and five hydro-climatologically distinct sub-regions. Results showed that: (1) The spatiotemporal variability of average daily precipitation over the study domain was well represented by all products. (2) All products showed better correlations with the monthly gauge-based observations than the daily data. Compared to 3B42V7, both IMERG products presented a better agreement with gauge-based observations. (3) The estimation skills of all precipitation products showed significant spatial variations. Overall performance of all precipitation products was better in the Eastern region compared to the Middle and Western regions. (4) Satellite products were able to detect tiny precipitation events, but they were uncertain in capturing light and moderate precipitation events. (5) No significant improvements in the precipitation estimation skill of IMERG-V06 were found as compared to IMERG-V05. We deduce that the IMERG-V06 precipitation detection capability could not outperform the efficiency of IMERG-V05. This comparative evaluation of the research products of Global Precipitation Measurement (GPM) and TRMM products in the Tianshan Mountains is useful for data users and algorithm developers.
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Sánchez, Luis, Jorge Lanza, Juan Santana, Rachit Agarwal, Pierre Raverdy, Tarek Elsaleh, Yasmin Fathy, et al. "Federation of Internet of Things Testbeds for the Realization of a Semantically-Enabled Multi-Domain Data Marketplace." Sensors 18, no. 10 (October 10, 2018): 3375. http://dx.doi.org/10.3390/s18103375.
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The Internet of Things (IoT) concept has attracted a lot of attention from the research and innovation community for a number of years already. One of the key drivers for this hype towards the IoT is its applicability to a plethora of different application domains. However, infrastructures enabling experimental assessment of IoT solutions are scarce. Being able to test and assess the behavior and the performance of any piece of technology (i.e., protocol, algorithm, application, service, etc.) under real-world circumstances is of utmost importance to increase the acceptance and reduce the time to market of these innovative developments. This paper describes the federation of eleven IoT deployments from heterogeneous application domains (e.g., smart cities, maritime, smart building, crowd-sensing, smart grid, etc.) with over 10,000 IoT devices overall which produce hundreds of thousands of observations per day. The paper summarizes the resources that are made available through a cloud-based platform. The main contributions from this paper are twofold. In the one hand, the insightful summary of the federated data resources are relevant to the experimenters that might be seeking for an experimental infrastructure to assess their innovations. On the other hand, the identification of the challenges met during the testbed integration process, as well as the mitigation strategies that have been implemented to face them, are of interest for testbed providers that can be considering to join the federation.
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Chakiri, Houda, Mohammed El Mohajir, and Nasser Assem. "A data warehouse hybrid design framework using domain ontologies for local good-governance assessment." Transforming Government: People, Process and Policy 14, no. 2 (April 11, 2020): 171–203. http://dx.doi.org/10.1108/tg-04-2019-0025.
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Purpose Most local governance assessment tools are entirely or partially based on stakeholders’ surveys, focus groups and benchmarks of different local governments in the world. These tools remain a subjective way of local governance evaluation. To measure the performance of local good-governance using an unbiased assessment technique, the authors have developed a framework to help automate the design process of a data warehouse (DW), which provides local and central decision-makers with factual, measurable and accurate local government data to help assess the performance of local government. The purpose of this paper is to propose the extraction of the DW schema based on a mixed approach that adopts both i* framework for requirements-based representation and domain ontologies for data source representation, to extract the multi-dimensional (MD) elements. The data was collected from various sources and information systems (ISs) deployed in different municipalities. Design/methodology/approach The authors present a framework for the design and implementation of a DW for local good-governance assessment. The extraction of facts and dimensions of the DW’s MD schema is done using a hybrid approach, where the extraction of requirement-based DW schema and source-based DW schema are done in parallel followed by the reconciliation of the obtained schemas to obtain the good-governance assessment DW final design. Findings The authors developed a novel framework to design and implement a DW for local good-governance assessment. The framework enables the extraction of the DW MD schema by using domain ontologies to help capture semantic artifacts and minimize misconceptions and misunderstandings between different stakeholders. The introduction and use of domain ontologies during the design process serves the generalization and automation purpose of the framework. Research limitations/implications The presently conducted research faced two main limitations as follows: the first is the full automation of the design process of the DW and the second, and most important, is access to local government data as it remains limited because of the lack of digitally stored data in municipalities, especially in developing countries in addition to the difficulty of accessing the data because of regulatory aspects and bureaucracy. Practical implications The local government environment is among the public administrations most subject to change-adverse cultures and where the authors can face high levels of resistance and significant difficulties during the implementation of decision support systems, despite the commitment/engagement of decision-makers. Access to data sources stored by different ISs might be challenging. While approaching the municipalities for data access, it was done in the framework of a research project within one of the most notorious universities in the country, which gave more credibility and trust to the research team. There is also a need for further testing of the framework to reveal its scalability and performance characteristics. Originality/value Compared to other local government assessment ad hoc tools that are partially or entirely based on subjectively collected data, the framework provides a basis for automated design of a comprehensive local government DW using e-government domain ontologies for data source representation coupled with the goal, rationale and business process diagrams for user requirements representations, thus enabling the extraction of the final DW MD schema.
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Migut, Malgorzata, and Marcel Worring. "Visual exploration of classification models for various data types in risk assessment." Information Visualization 11, no. 3 (February 16, 2012): 237–51. http://dx.doi.org/10.1177/1473871611433715.
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In risk assessment applications well-informed decisions need to be made based on large amounts of multi-dimensional data. In many domains, not only the risk of a wrong decision, but also of the trade-off between the costs of possible decisions are of utmost importance. In this paper we describe a framework to support the decision-making process, which tightly integrates interactive visual exploration with machine learning. The proposed approach uses a series of interactive 2D visualizations of numerical and ordinal data combined with visualization of classification models. These series of visual elements are linked to the classifier’s performance, which is visualized using an interactive performance curve. This interaction allows the decision-maker to steer the classification model and instantly identify the critical, cost-changing data elements in the various linked visualizations. The critical data elements are represented as images in order to trigger associations related to the knowledge of the expert. In this way the data visualization and classification results are not only linked together, but are also linked back to the classification model. Such a visual analytics framework allows the user to interactively explore the costs of his decisions for different settings of the model and, accordingly, use the most suitable classification model. More informed and reliable decisions result. A case study in the forensic psychiatry domain reveals the usefulness of the suggested approach.
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Chattopadhyay, Ayan, and Somarata Chakraborty. "Performance Reflections on Quality Education in Respect of SDGs: A Re-Assessment of Indian States and UTs Using TOPSIS Approach." Asian Review of Social Sciences 8, no. 1 (February 5, 2019): 102–9. http://dx.doi.org/10.51983/arss-2019.8.1.1511.
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The success of Millennium Development Goals (MDGs) has led to the initiation of Sustainable Development Goals by the United Nations, earmarking a time frame of 2016–2030 for its achievement. The Sustainable Development Goals framework consists of 17 goals and 169 targets within the three broader dimensions of economic, social and environmental development. The performance evaluation of Indian states and union territories, available in SDG India Index Baseline Report, 2018, has been recently concluded by NITI Aayog. Focusing on goal 4that puts thrust on quality education, NITI Aayog has considered 7 criteria capturing targets 4.1 besides 4.c and 36 alternatives (Indian states and union territories) within a multi criteria decision making environment where criteria weights are assumed to be equal, performance calculated on the basis of simple arithmetic average theory and missing value cases not considered in their computation. This simplistic approach partially captures the effect of complex interplay between the multiple criterions. The purpose of this paper is to re-evaluate the performance of Indian states and union territories with respect to goal 4of Sustainable Development Goals, as estimated by NITI Aayog. It also aims to provide a more holistic picture on performance ranking by incorporating varying weights of criteria, as obtained from Shannon’s entropy, and replacing arithmetic average theory with a more rigorous mathematical model within the domain of multi criteria decision making. In this study Technique for Order Preference by Similarity to Ideal Solution have been used to generate an index for ranking alternatives and all missing values have been figured from expectation maximization algorithm. As expected, rank reversal phenomenon has been observed and a very low level of convergence between ranks obtained from the proposed approach and that of NITI Aayog emerges.
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Dong, Shaojiang, Yang Li, Peng Zhu, Xuewu Pei, Xuejiao Pan, Xiangyang Xu, Lanhui Liu, Bin Xing, and Xiaolin Hu. "Rolling bearing performance degradation assessment based on singular value decomposition-sliding window linear regression and improved deep learning network in noisy environment." Measurement Science and Technology 33, no. 4 (January 28, 2022): 045015. http://dx.doi.org/10.1088/1361-6501/ac39d1.
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Abstract It is difficult to evaluate the degradation performance and the degradation state of a rolling bearing in noisy environment. A new method is proposed to solve the problem based on singular value decomposition (SVD)-sliding window linear regression and ResNeXt–multi-attention mechanism’s deep neural network (RMADNN). Firstly, the root mean square (rms) gradient value is calculated on the basis of rms based on SVD and sliding window linear regression, which is used as the rolling bearing performance degradation indicator in noisy environment. Secondly, the degradation state of the rolling bearing is divided by the rms gradient. Thirdly, for the deep learning network model, the soft attention mechanism is introduced into the bidirectional long short-term memory network to extract more important and deep fault features. At the same time, the ResNeXt layer is added into the convolutional neural network to extract more fault features and merge them through multi-scale grouped convolution. The hybrid domain attention mechanism (HDAM) is introduced after the ResNeXt layer. The HDAM can screen out more important features from the output features of the ResNeXt in the two dimensions of channel and spatial which demonstrates the improved deep learning network of the RMADNN. Finally, the labeled data set is input into the improved model for training, and the Softmax classifier is used to identify the life degradation state of the rolling bearing. Through the verification of multiple test data sets, the results show that the method proposed in this research is very effective on the rolling bearing performance degradation assessment.
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Chatterjee, Prasenjit, Supraksh Mondal, Soumava Boral, Arnab Banerjee, and Shankar Chakraborty. "A NOVEL HYBRID METHOD FOR NON-TRADITIONAL MACHINING PROCESS SELECTION USING FACTOR RELATIONSHIP AND MULTI-ATTRIBUTIVE BORDER APPROXIMATION METHOD." Facta Universitatis, Series: Mechanical Engineering 15, no. 3 (December 9, 2017): 439. http://dx.doi.org/10.22190/fume170508024c.
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Selection of the most appropriate non-traditional machining process (NTMP) for a definite machining requirement can be observed as a multi-criteria decision-making (MCDM) problem with conflicting criteria. This paper proposes a novel hybrid method encompassing factor relationship (FARE) and multi-attributive border approximation area comparison (MABAC) methods for selection and evaluation of NTMPs. The application of FARE method is pioneered in NTMP assessment domain to estimate criteria weights. It significantly condenses the problem of pairwise comparisons for estimating criteria weights in MCDM environment. In order to analyze and rank different NTMPs in accordance with their performance and technical properties, MABAC method is applied. Computational procedure of FARE-MABAC hybrid model is demonstrated while solving an NTMP selection problem for drilling cylindrical through holes on non-conductive ceramic materials. The results achieved by FARE-MABAC method exactly corroborate with those obtained by the past researchers which validate the usefulness of this method while solving complex NTMP selection problems.
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Sharma, Neha, Dennis Welker, Soheila Aghlmandi, Michaela Maintz, Hans-Florian Zeilhofer, Philipp Honigmann, Thomas Seifert, and Florian M. Thieringer. "A Multi-Criteria Assessment Strategy for 3D Printed Porous Polyetheretherketone (PEEK) Patient-Specific Implants for Orbital Wall Reconstruction." Journal of Clinical Medicine 10, no. 16 (August 13, 2021): 3563. http://dx.doi.org/10.3390/jcm10163563.
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Pure orbital blowout fractures occur within the confines of the internal orbital wall. Restoration of orbital form and volume is paramount to prevent functional and esthetic impairment. The anatomical peculiarity of the orbit has encouraged surgeons to develop implants with customized features to restore its architecture. This has resulted in worldwide clinical demand for patient-specific implants (PSIs) designed to fit precisely in the patient’s unique anatomy. Material extrusion or Fused filament fabrication (FFF) three-dimensional (3D) printing technology has enabled the fabrication of implant-grade polymers such as Polyetheretherketone (PEEK), paving the way for a more sophisticated generation of biomaterials. This study evaluates the FFF 3D printed PEEK orbital mesh customized implants with a metric considering the relevant design, biomechanical, and morphological parameters. The performance of the implants is studied as a function of varying thicknesses and porous design constructs through a finite element (FE) based computational model and a decision matrix based statistical approach. The maximum stress values achieved in our results predict the high durability of the implants, and the maximum deformation values were under one-tenth of a millimeter (mm) domain in all the implant profile configurations. The circular patterned implant (0.9 mm) had the best performance score. The study demonstrates that compounding multi-design computational analysis with 3D printing can be beneficial for the optimal restoration of the orbital floor.
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THOMAIDIS, NIKOLAOS S., NIKITAS NIKITAKOS, and GEORGIOS D. DOUNIAS. "THE EVALUATION OF INFORMATION TECHNOLOGY PROJECTS: A FUZZY MULTICRITERIA DECISION-MAKING APPROACH." International Journal of Information Technology & Decision Making 05, no. 01 (March 2006): 89–122. http://dx.doi.org/10.1142/s0219622006001897.
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This paper presents a fuzzy set-based approach to the evaluation of information technology (IT) projects. We assume a multi-criteria decision-making framework, where sets of general and domain-specific criteria are used to judge the relative performance of alternative technologies. The methodology was originally developed for DIAS.net, an EU project aiming at the development of the Information Society in insular and isolated regions of Europe. In this paper, we present many aspects of our evaluation framework, including the synthesis of evaluation teams, the assessment of the importance of criteria, the evaluation of the performance of the alternatives and the final ranking and selection of projects. The methodology presented has the innovative feature of embodying techniques of fuzzy sets theory into the classical multi-criteria decision analysis. This combination enables us to handle efficiently the subjectiveness that often characterizes expert judgements on a decision problem. Fuzzy linguistic terms, such as "poor," "fair," "very important," etc. are proposed for assessing the relative merit of alternatives and criteria. The paper concludes by exploring the potentiality of the above methodology in providing a flexible and robust IT evaluation framework.
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Yu, Mei, Hai Li Liao, Ming Shui Li, Cun Ming Ma, and Ming Liu. "Analysis of Flutter Stability of the Xihoumen Bridge in the Completed Stage." Advanced Materials Research 243-249 (May 2011): 1629–33. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.1629.
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Aerodynamic stability is an issue in the wind-resistant design of long-span bridges, flutter is an aerodynamic instability phenomenon that occurs due to interactions between wind and structural motion. The Xihoumen Bridge is the second long suspension bridge in the world, the aeroelastic performance of the Xihoumen Bridge is investigated by wind tunnel testing and an analytical approach. In the case, wind-tunnel testing was performed using an aeroelastic full model of the bridge, and two section models of the bridge. Flutter derivatives of bridge decks are routinely extracted from wind tunnel section model experiments for the assessment of performance against wind loading, the analytical method used here were a two-dimensional flutter analysis and a multi-mode analysis in the frequency domain. The analytical results were compared with the wind tunnel test data; it showed that the flutter analysis results were good agreement with the wind-tunnel test data.
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Delle Donne, Valentina, Nicoletta Ciccarelli, Valentina Massaroni, Alberto Borghetti, Alex Dusina, Damiano Farinacci, Elena Visconti, Enrica Tamburrini, Massimiliano Fabbiani, and Simona Di Giambenedetto. "The University of California San Diego performance-based skills assessment: a useful tool to detect mild everyday functioning difficulties in HIV-infected patients with very good immunological condition." Journal of NeuroVirology 26, no. 6 (August 24, 2020): 899–907. http://dx.doi.org/10.1007/s13365-020-00891-8.
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AbstractEveryday functioning (EF) impairment is frequent in people living with HIV (PLWH). Our aim was to better explore EF and its association with PLWH cognition, by administering both the IADL scale, the most common functional scale, and a new and ecologic multi-domain (communication and financial skills) tool to measure EF as the University of California San Diego (UCSD) Performance-Based Skills Assessment-Brief Version (UPSA-B). Eighty-five PLWH on cART with very good immunological condition and 23 age- and education-matched healthy controls (HC) were enrolled. PLWH underwent a standardized neuropsychological battery plus IADL, and cognitive impairment was defined according to Frascati criteria. Both groups underwent the UPSA-B. Only 6 subjects (7%) were affected by cognitive impairment (asymptomatic profile). While IADL score was at ceiling for all patients, the UPSA-B total score was significantly worse in PLWH when compared with HC [mean 82.1 (SD 9.3) vs 89.2 (SD 6.2); p < 0.001]. At communication subtest, PLWH group and HC were significantly different (p = 0.002), while no difference emerged at financial skills (p = 0.096). Higher score at UPSA-B was independently associated with better global cognitive performance (composite Z-score) (β 7.79; p < 0.001). Also considering each single cognitive domain, UPSA-B performance (both total and at subtests) confirmed the association with neurocognitive performance. In conclusion, UPSA-B seems to better discriminate EF impairment than IADL in PLWH, and it was associated with cognitive functions, also in the absence of symptomatic cognitive impairment. Thus, it appears a promising tool in the context of HIV infection to avoid misdiagnosis and to better detect also mild EF.
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Piselli, Cristina, Carla Balocco, Ilaria Pigliautile, Claudia Fabiani, Roberta J. Cureau, Fabio Sciurpi, Cristina Carletti, Anna Laura Pisello, and Franco Cotana. "Microclimate Assessment at Real Experimental Conditions for Green Energy Urban Policy." International Journal of Sustainable Development and Planning 17, no. 5 (August 31, 2022): 1381–87. http://dx.doi.org/10.18280/ijsdp.170501.
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The incoming transformation of urban built-up areas, surroundings morphology, and local climate due to global warming connected to the necessity of renewable energy use maximization is the fundamental to the main aim of the present research. Indeed, urban policies can benefit from the accurate monitoring of microclimate variables in order to ensure fine living standards in cities, while improving the energy performance of the built environment. In this view, a systemic experimental approach was implemented. A monitoring campaign using dynamic experimental measurements under real conditions was carried out at an inter-urban scale taking into account different building-plant systems forms and urban configuration. In detail, two innovative portable monitoring systems were used for monitoring key multi-domain parameters at hyperlocal, urban, and intra-urban scale. The monitoring campaign was carried out in summer and winter in the city center of Florence, Italy. Research findings highlighted that urban microclimate control can be a potential factor for urban heat island (UHI) mitigation and sustainable green energy solutions, which involve social, economic, and energy policy beyond environment. The analysis of real microclimate conditions may support the green energy urban policy development in terms of renewable energy integration and urban areas design and management.
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Xiang, Min, Feng Xiong, Yuanfeng Shi, Kaoshan Dai, and Zhibin Ding. "Wavelet multi-resolution approximation of time-varying frame structure." Advances in Mechanical Engineering 10, no. 8 (August 2018): 168781401879559. http://dx.doi.org/10.1177/1687814018795596.
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Engineering structures usually exhibit time-varying behavior when subjected to strong excitation or due to material deterioration. This behavior is one of the key properties affecting the structural performance. Hence, reasonable description and timely tracking of time-varying characteristics of engineering structures are necessary for their safety assessment and life-cycle management. Due to its powerful ability of approximating functions in the time–frequency domain, wavelet multi-resolution approximation has been widely applied in the field of parameter estimation. Considering that the damage levels of beams and columns are usually different, identification of time-varying structural parameters of frame structure under seismic excitation using wavelet multi-resolution approximation is studied in this article. A time-varying dynamical model including both the translational and rotational degrees of freedom is established so as to estimate the stiffness coefficients of beams and columns separately. By decomposing each time-varying structural parameter using one wavelet multi-resolution approximation, the time-varying parametric identification problem is transformed into a time-invariant non-parametric one. In solving the high number of regressors in the non-parametric regression program, the modified orthogonal forward regression algorithm is proposed for significant term selection and parameter estimation. This work is demonstrated through numerical examples which consider both gradual variation and abrupt changes in the structural parameters.
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Cestari, José Marcelo Almeida Prado, Eduardo de Freitas Rocha Loures, and Eduardo Alves Portela Santos. "A Method to Diagnose Public Administration Interoperability Capability Levels Based on Multi-Criteria Decision-Making." International Journal of Information Technology & Decision Making 17, no. 01 (January 2018): 209–45. http://dx.doi.org/10.1142/s0219622017500365.
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Interoperability is a critical factor for public administration-related entities operating in collaborative/cooperative environments. Thus, performing an interoperability diagnosis, with respect to other usual assessment approaches, provides a more adequate and extended view in establishing qualities and gaps, and helping to prioritize actions to increase performance and maturity of a government-related organization. This paper presents a diagnosis method called Public Administration Interoperability Diagnosis Method (PAIDM), using Analytic Hierarchy Process (AHP) as multi-criteria decision-making structure to calculate the capability levels diagnosed. A proposed development framework guides a systematic literature review, followed by a survey of experts and a set of quantitative and qualitative methods related to the extraction and modeling of the knowledge from the public administration domain mapped into theoretical, conceptual, and practical outputs devoted to PAIDM execution. The paper also raises a public administration interoperability capability model used as a reference for the diagnosis and presents general results of two public administration application cases regarding their capability levels.
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Bijjahalli, Suraj, Alessandro Gardi, Nichakorn Pongsakornsathien, Roberto Sabatini, and Trevor Kistan. "A Unified Airspace Risk Management Framework for UAS Operations." Drones 6, no. 7 (July 21, 2022): 184. http://dx.doi.org/10.3390/drones6070184.
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Collision risk modelling has a long history in the aviation industry, with mature models currently utilised in the strategic planning of airspace sectors and air routes. However, the progressive introduction of Unmanned Aircraft Systems (UAS) and other forms of air mobility poses new challenges, compounded by a growing need to address both offline and online operational requirements. To address the existing gaps in the current airspace risk assessment models, this article proposes a comprehensive risk management framework, which relies on a novel methodology to model UAS collision risk in all classes of airspace. This methodology inherently accounts for the performance of Communication, Navigation and Surveillance (CNS) systems, and, as such, it can be applied to both strategic and tactical operational timeframes. Additionally, the proposed approach can be applied inversely to determine CNS performance requirements given a target value of collision probability. This new risk assessment methodology is based on rigorous analysis of the CNS error characteristics and the transformation of the associated models into the spatial domain to generate a protection volume around each predicted collision. Additionally, a methodology to quickly and conservatively evaluate the multi-integral formulation of collision probability is introduced. The validity of the proposed framework is tested using representative CNS performance parameters in two simulation case studies targeting a terminal manoeuvring area and an enroute scenario, respectively.
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Yamada, Yuji, Takuma Inagawa, Yuma Yokoi, Aya Shirama, Kazuki Sueyoshi, Ayumu Wada, Naotsugu Hirabayashi, Hideki Oi, and Tomiki Sumiyoshi. "Efficacy and Safety of Multi-Session Transcranial Direct Current Stimulation on Social Cognition in Schizophrenia: A Study Protocol for an Open-Label, Single-Arm Trial." Journal of Personalized Medicine 11, no. 4 (April 19, 2021): 317. http://dx.doi.org/10.3390/jpm11040317.
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Backgrounds: Social cognition is defined as the mental operations underlying social behavior. Patients with schizophrenia elicit impairments of social cognition, which is linked to poor real-world functional outcomes. In a previous study, transcranial direct current stimulation (tDCS) improved emotional recognition, a domain of social cognition, in patients with schizophrenia. However, since social cognition was only minimally improved by tDCS when administered on frontal brain areas, investigations on the effect of tDCS on other cortical sites more directly related to social cognition are needed. Therefore, we present a study protocol to determine whether multi-session tDCS on superior temporal sulcus (STS) would improve social cognition deficits of schizophrenia. Methods: This is an open-label, single-arm trial, whose objective is to investigate the efficacy and safety of multi-session tDCS over the left STS to improve social cognition in patients with schizophrenia. The primary outcome measure will be the Social Cognition Screening Questionnaire. Neurocognition, functional capacity, and psychotic symptoms will also be evaluated by the Brief Assessment of Cognition in Schizophrenia, UCSD Performance-Based Skills Assessment-Brief, and Positive and Negative Syndrome Scale, respectively. Data will be collected at baseline, and 4 weeks after the end of intervention. If social cognition is improved in patients with schizophrenia by tDCS based on this protocol, we may plan randomized controlled trial.
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Wyrwich, Kathleen W., Ariane K. Kawata, Christine Thompson, Stefan Holmstrom, Malcolm Stoker, and Ingela Wiklund. "Validation of the Self-Assessment of Treatment Questionnaire among Patients with Postherpetic Neuralgia." Pain Research and Treatment 2012 (August 13, 2012): 1–15. http://dx.doi.org/10.1155/2012/621619.
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Introduction. A five-item Self-Assessment of Treatment (SAT) was developed to assess improvement and satisfaction with treatment associated with the application of a novel high concentration 8% capsaicin topical patch in clinical trials in patients with postherpetic neuralgia (PHN). This study evaluated the item performance and psychometric properties of the SAT. Methods. The SAT, Brief Pain Inventory, SF-36v2, Short-Form McGill Pain Questionnaire, and Patient and Clinician Global Impression of Change (PGIC; CGIC) scores were measured in two 12-week Phase 3 clinical trials. Factor analysis assessed the underlying factor structure, followed by examination of the reliability and validity of the multi-item domain. Results. Pooled data from 698 patients completing SAT after 12 weeks of treatment were analyzed. A one-factor model combining three of the five items emerged as the optimal solution. Internal consistency reliability of this treatment efficacy factor was high (Cronbach's alpha = 0.89). Construct validity was demonstrated by moderate to high correlations with change in other study endpoints. SAT mean scores consistently discriminated between patient change groups defined by PGIC and CGIC. Conclusions. The measurement properties of the three-item version of SAT are valid and reliable for assessment of treatment with a high concentration capsaicin patch among patients with PHN.
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Eriņš, Matīss, Oļesja Minejeva, Zigurds Markovičs, Juris Lauznis, and Raivis Kivlenieks. "RESEARCH OF HUMAN FATIGUE AND MEASUREMENT PARAMETERS FOR WORKABILITY ASSESSMENT." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 20, 2019): 38. http://dx.doi.org/10.17770/etr2019vol2.4148.
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Human fatigue is reduced working capability for certain period of time as the result of unusual or prolonged workload. Fatigue arises when the body’s energy requirements exceed its supply. Fatigue first manifests as reduced concentration capability causing movement coordination and precision disruption leading to decreasing workability. Fatigue is an object of research in physiology, psychology, work ergonomics, medicine, and biotechnology where each domain has a focus on mental fatigue. The functional state in the context of professional activity is defined as a complex of characteristics of functions and qualities that determine the fulfilment of labour activity. Furthermore, a comprehensive estimation of subject functional state in combination with other factors like subject self-assessment and objective performance tests (cognitive load tests) is a necessary input for the evaluation of workability and efficiency on task. The heterogeneous nature of fatigue as a systemic manifestation requires analysis of multiple key parameters which are relevant to the specific type. The current feasibility study focuses on human biological signal from electrical activity of heart, brain, muscles and skin potentials as well as temperature, position, and respiration to obtain diagnostic parameters reflecting the state of cardiovascular, muscles, and central nervous systems for physiological monitoring of vital signs. The fatigue physiological parameter and feature formalization aim to support the development of a platform with complex passive multi-level fatigue monitoring system and workability evaluation system designed in order to provide an integrated service.
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Chinchilla-Romero, Lorena, Jonathan Prados-Garzon, Pablo Ameigeiras, Pablo Muñoz, and Juan M. Lopez-Soler. "5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0." Sensors 22, no. 1 (December 29, 2021): 229. http://dx.doi.org/10.3390/s22010229.
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Fifth Generation (5G) is expected to meet stringent performance network requisites of the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G network slicing capabilities might not be enough in terms of degree of isolation for many private 5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network slicing, which refers to the use of dedicated and well isolated resources for each network slice at every network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of infrastructure slicing to provide isolation among production lines (PLs) in an industrial private 5G network. To that end, we develop a queuing theory-based model to estimate the end-to-end (E2E) mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic from PLs. Also we evaluate the use of Time-Sensitive Networking (TSN) against bare Ethernet to provide layer 2 connectivity among the 5G system components. We use a complete and realistic setup based on experimental and simulation data of the scenario considered. Our results support the effectiveness of infrastructure slicing to provide isolation in performance among the different slices. Then, using dedicated slices with segregated resources for each PL might reduce the number of the production downtimes and associated costs as the malfunctioning of a PL will not affect the network performance perceived by the performance-sensitive traffic from other PLs. Last, our results show that, besides the improvement in performance, TSN technology truly provides full isolation in the transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation, and bandwidth reservation capabilities.
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Shi, Hua, Lin Huang, Ke Li, Xiang-Hu Wang, and Hu-Chen Liu. "An Extended Multi-Attributive Border Approximation Area Comparison Method for Emergency Decision Making with Complex Linguistic Information." Mathematics 10, no. 19 (September 21, 2022): 3437. http://dx.doi.org/10.3390/math10193437.
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In recent years, different types of emergency events have taken place frequently around the world. Emergencies need to be addressed in the shortest possible time since inappropriate or delayed decisions may result in severe secondary disasters and economic losses. To make emergency decisions effectively within a limited time, a new emergency decision-making model is proposed in this study based on double hierarchy hesitant linguistic term sets (DHHLTSs) and the multi-attributive border approximation area comparison (MABAC) method. First, the performance assessment information on emergency solutions provided by domain experts is represented by the DHHLTSs, which are very useful for managing complex linguistic expressions in a prominent manner. Then, we make an extension of the MABAC method to determine the priority of alternative solutions and find out the optimal one for an emergency event. Furthermore, the criteria weights for emergency decision making are determined objectively with a maximum comprehensive method. Finally, a practical public health example is provided and a comparative analysis is performed to illustrate the applicability and advantages of the proposed emergency decision-making model.
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Bonelli, Nicola, Stefano Giordano, and Gregorio Procissi. "Enif-Lang: A Specialized Language for Programming Network Functions on Commodity Hardware." Journal of Sensor and Actuator Networks 7, no. 3 (August 7, 2018): 34. http://dx.doi.org/10.3390/jsan7030034.
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The maturity level reached by today’s commodity platforms makes even low-cost PCs viable alternatives to dedicated hardware to implement real network functions without sacrificing performance. Indeed, the availability of multi-core processing packages and multi-queue network interfaces that can be managed by accelerated I/O frameworks, provides off-the-shelf servers with the necessary power capability for running a broad variety of network applications with near hardware-class performance. At the same time, the introduction of the Software Defined Networks (SDN) and the Network Functions Virtualization (NFV) paradigms call for new programming abstractions and tools to allow this new class of network devices to be flexibly configured and functionally repurposed from the network control plane. The paper presents the ongoing work towards Enif-Lang (Enhanced Network processIng Functional Language), a functional language for programming network functions over generic middleboxes running the Linux operating system. The language addresses concurrent programming by design and is targeted at developing simple stand-alone applications as well as pre-processing stages of packet elaborations. Enif-Lang is implemented as a Domain Specific Language embedded in the Haskell language and inherits the main principles of its ancestor, including the strong typedness and the concept of function compositions. Complex network functions are implemented by composing a set of elementary operations (primitives) by means of a compact yet expressive language grammar. Throughout the paper, the description of the design principles and features of Enif-Lang are accompanied by examples and use cases. In addition, a preliminary performance assessment is carried out to prove the effectiveness of the language for developing practical applications with the performance level required by 5G systems and the Tactile Internet.
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Ntlholang, Ontefetse, Kevin McCarroll, Eamon Laird, Anne M. Molloy, Mary Ward, Helene McNulty, Leane Hoey, et al. "The relationship between adiposity and cognitive function in a large community-dwelling population: data from the Trinity Ulster Department of Agriculture (TUDA) ageing cohort study." British Journal of Nutrition 120, no. 5 (July 30, 2018): 517–27. http://dx.doi.org/10.1017/s0007114518001848.
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AbstractPrevious reports investigating adiposity and cognitive function in the population allude to a negative association, although the relationship in older adults is unclear. The aim of this study was to investigate the association of adiposity (BMI and waist:hip ratio (WHR)) with cognitive function in community-dwelling older adults (≥60 years). Participants included 5186 adults from the Trinity Ulster Department of Agriculture ageing cohort study. Neuropsychological assessment measures included the Mini-Mental State Examination (MMSE), Frontal Assessment Battery (FAB) and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Multi-variable linear regression models were used to assess the association between adiposity and cognitive function adjusting for insulin resistance, inflammation and cerebrovascular disease. The mean ages were 80·3 (sd6·7), 71·0 (sd7·3) and 70·2 (sd6·3) years on the cognitive, bone and hypertensive cohorts, respectively. In the cognitive cohort, BMI was positively associated with immediate and delay memory, visuospatial/constructional ability, language and MMSE, and negatively with FAB (log-transformed), whereas WHR was negatively associated with attention. In the bone cohort, BMI was not associated with any cognitive domain, whereas WHR was negatively associated with visuospatial/constructional ability, attention and MMSE. In the hypertensive cohort, BMI was not associated with any cognitive domain, whereas WHR was negatively associated with immediate and delayed memory, visuospatial/constructional ability, language and MMSE and positively with FAB (log-transformed). In the cognitive and bone cohorts, the association of WHR and attention disappeared by further controlling for C-reactive protein and HbA1C. In this study of older adults, central adiposity was a stronger predictor of poor cognitive performance than BMI. Older adults could benefit from targeted public health strategies aimed at reducing obesity and obeseogenic risk factors to avoid/prevent/slow cognitive dysfunction.
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Bickel, Kathleen Elizabeth, Kristen K. McNiff, Mary K. Buss, Arif Kamal, Dale Lupu, Amy Pickar Abernethy, Michael S. Broder, et al. "Defining high-quality palliative care in oncology practice: An ASCO/AAHPM Guidance Statement." Journal of Clinical Oncology 33, no. 29_suppl (October 10, 2015): 108. http://dx.doi.org/10.1200/jco.2015.33.29_suppl.108.
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108 Background: When integrated into routine oncology care, palliative care can improve symptom burden, quality of life, and patient and caregiver satisfaction. However, not all oncology practices have access to subspecialty palliative medicine for referral. This project endeavored to define what constitutes high-quality primary palliative care as delivered by medical oncology practices. Methods: An expert steering committee developed a list of 966 palliative care service items, divided into nine domains, each describing an aspect of palliative care delivery for patients with advanced cancer. Using a modified Delphi methodology, 31 multi-disciplinary panelists ranked each service item according to importance, feasibility and scope. Results: Panelists endorsed the highest proportion of palliative care service items in the domains of End-of-Life Care (81%); Communication and Shared Decision-Making (79%); and Care Planning (78%). Lowest proportions were in: Spiritual and Cultural Assessment and Management (35%) and Psychosocial Assessment and Management (39%). In the largest domain, Symptom Assessment and Management, there was consensus that all symptoms should be assessed and managed at a basic level with more comprehensive management for common symptoms such as nausea, vomiting, diarrhea, dyspnea and pain. Under the domain of Appropriate Palliative Care and Hospice Referral, there was consensus that oncology practices should be able to describe the difference between palliative care and hospice to patients and refer patients with an expected survival of under 3 months or poor performance status (Zubrod 3-4) to hospice. Conclusions: This statement describes high-quality palliative care for patients with advanced cancer or high symptom burden, as delivered by oncology practices. Oncology providers wishing to enhance palliative care delivery may find the guidance useful to inform operational changes and quality improvement efforts.
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Flemotomos, Nikolaos, Victor R. Martinez, Zhuohao Chen, Torrey A. Creed, David C. Atkins, and Shrikanth Narayanan. "Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations." PLOS ONE 16, no. 10 (October 22, 2021): e0258639. http://dx.doi.org/10.1371/journal.pone.0258639.
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During a psychotherapy session, the counselor typically adopts techniques which are codified along specific dimensions (e.g., ‘displays warmth and confidence’, or ‘attempts to set up collaboration’) to facilitate the evaluation of the session. Those constructs, traditionally scored by trained human raters, reflect the complex nature of psychotherapy and highly depend on the context of the interaction. Recent advances in deep contextualized language models offer an avenue for accurate in-domain linguistic representations which can lead to robust recognition and scoring of such psychotherapy-relevant behavioral constructs, and support quality assurance and supervision. In this work, we propose a BERT-based model for automatic behavioral scoring of a specific type of psychotherapy, called Cognitive Behavioral Therapy (CBT), where prior work is limited to frequency-based language features and/or short text excerpts which do not capture the unique elements involved in a spontaneous long conversational interaction. The model focuses on the classification of therapy sessions with respect to the overall score achieved on the widely-used Cognitive Therapy Rating Scale (CTRS), but is trained in a multi-task manner in order to achieve higher interpretability. BERT-based representations are further augmented with available therapy metadata, providing relevant non-linguistic context and leading to consistent performance improvements. We train and evaluate our models on a set of 1,118 real-world therapy sessions, recorded and automatically transcribed. Our best model achieves an F1 score equal to 72.61% on the binary classification task of low vs. high total CTRS.