Relevant bibliographies by topics / Predictive HCI models

Academic literature on the topic 'Predictive HCI models'

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Published: 11 March 2023

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Journal articles on the topic "Predictive HCI models"

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Paton, Chris, Andre W. Kushniruk, Elizabeth M. Borycki, Mike English, and Jim Warren. "Improving the Usability and Safety of Digital Health Systems: The Role of Predictive Human-Computer Interaction Modeling." Journal of Medical Internet Research 23, no. 5 (May 27, 2021): e25281. http://dx.doi.org/10.2196/25281.

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In this paper, we describe techniques for predictive modeling of human-computer interaction (HCI) and discuss how they could be used in the development and evaluation of user interfaces for digital health systems such as electronic health record systems. Predictive HCI modeling has the potential to improve the generalizability of usability evaluations of digital health interventions beyond specific contexts, especially when integrated with models of distributed cognition and higher-level sociotechnical frameworks. Evidence generated from building and testing HCI models of the user interface (UI) components for different types of digital health interventions could be valuable for informing evidence-based UI design guidelines to support the development of safer and more effective UIs for digital health interventions.
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Segkouli, Sofia, Ioannis Paliokas, Dimitrios Tzovaras, Thanos Tsakiris, Magda Tsolaki, and Charalampos Karagiannidis. "Novel Virtual User Models of Mild Cognitive Impairment for Simulating Dementia." Computational and Mathematical Methods in Medicine 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/358638.

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Virtual user modeling research has attempted to address critical issues of human-computer interaction (HCI) such as usability and utility through a large number of analytic, usability-oriented approaches as cognitive models in order to provide users with experiences fitting to their specific needs. However, there is demand for more specific modules embodied in cognitive architecture that will detect abnormal cognitive decline across new synthetic task environments. Also, accessibility evaluation of graphical user interfaces (GUIs) requires considerable effort for enhancing ICT products accessibility for older adults. The main aim of this study is to develop and test virtual user models (VUM) simulating mild cognitive impairment (MCI) through novel specific modules, embodied at cognitive models and defined by estimations of cognitive parameters. Well-established MCI detection tests assessed users’ cognition, elaborated their ability to perform multitasks, and monitored the performance of infotainment related tasks to provide more accurate simulation results on existing conceptual frameworks and enhanced predictive validity in interfaces’ design supported by increased tasks’ complexity to capture a more detailed profile of users’ capabilities and limitations. The final outcome is a more robust cognitive prediction model, accurately fitted to human data to be used for more reliable interfaces’ evaluation through simulation on the basis of virtual models of MCI users.
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Isiaka, Fatima, Kassim S. Mwitondi, and Adamu M. Ibrahim. "Detection of natural structures and classification of HCI-HPR data using robust forward search algorithm." International Journal of Intelligent Computing and Cybernetics 9, no. 1 (March 14, 2016): 23–41. http://dx.doi.org/10.1108/ijicc-08-2015-0029.

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Purpose – The purpose of this paper is to proposes a forward search algorithm for detecting and identifying natural structures arising in human-computer interaction (HCI) and human physiological response (HPR) data. Design/methodology/approach – The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models used in classification algorithms. Data were also simulated based on multivariate normal distribution method and used to compare and validate the original data. Findings – Results shows that the forward search method provides robust features that are capable of repelling over-fitting in physiological and eye movement data. Research limitations/implications – One of the limitations of the robust forward search algorithm is that when the number of digits for residuals value is more than the expected size for stack flow, it normally yields an error caution; to counter this, the data sets are normally standardized by taking the logarithmic function of the model before running the algorithm. Practical implications – The authors conducted some of the experiments at individual residence which may affect environmental constraints. Originality/value – The novel approach to this method is the detection of outliers for data sets based on the Mahalanobis distances on HCI and HPR. And can also involve a large size of data with p possible parameters. The improvement made to the algorithm is application of more graphical display and rendering of the residual plot.
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Krabbe, Laura-Maria, Aditya Bagrodia, Ahmed Q. Haddad, Payal Kapur, Dina Khalil, Linda S. Hynan, Christopher G. Wood, et al. "Multi-institutional validation of the predictive value of Ki-67 in patients with high-grade urothelial carcinoma of the upper urinary tract." Journal of Clinical Oncology 33, no. 7_suppl (March 1, 2015): 371. http://dx.doi.org/10.1200/jco.2015.33.7_suppl.371.

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371 Background: To validate the independent predictive value of Ki-67 in patients with high-grade upper tract urothelial carcinoma (UTUC). Methods: 475 patients from the international UTUC collaboration who underwent extirpative surgery for high-grade UTUC were included in this study. Immunohistochemical staining for Ki-67 was performed on tissue microarray (TMA) formed from this patient cohort. Ki-67 expression was assessed in a semi-quantitative fashion and considered overexpressed at a cut-off of 20%. Multivariate analyses (MVA) were performed to assess independent predictors of oncological outcomes and Harrell’s C indices (HCI) were calculated for predictive models. Results: Median age of the cohort was 69.7 years and 55.2% of patients were male. Ki-67 was overexpressed in 25.9% of patients. Ki-67 overexpression was significantly associated with ureteral tumor location, higher pT-stage, lymphovascular invasion, sessile tumor architecture, tumor necrosis, concomitant carcinoma in situ (CIS), and regional lymph node metastases. In Kaplan-Meier analyses, overexpressed Ki-67 was associated with worse recurrence-free (RFS) (HR 12.6, p<0.001) and cancer-specific survival (CSS) (HR 15.8, p<0.001). In MVA, Ki-67 was an independent predictor of RFS (HR 1.6, 95% CI 1.07-2.30, p=0.021) and CSS (HR 1.9, 95% CI 1.29-2.90, p=0.001). Ki-67 improved HCI from 0.66 to 0.70 (p<0.0001) for both RFS and CSS in our preoperative model, and from 0.81 to 0.82 (p=0.0018) for RFS and 0.81 to 0.83 (p=0.005) for CSS in our post-operative model. Conclusions: Ki-67 was validated as an independent prognostic predictor of RFS and CSS in patients treated with extirpative surgery for high-grade UTUC in a large, multi-institutional cohort.
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Bakaev, Maxim, Sebastian Heil, and Martin Gaedke. "Reasonable Effectiveness of Features in Modeling Visual Perception of User Interfaces." Big Data and Cognitive Computing 7, no. 1 (February 8, 2023): 30. http://dx.doi.org/10.3390/bdcc7010030.

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Training data for user behavior models that predict subjective dimensions of visual perception are often too scarce for deep learning methods to be applicable. With the typical datasets in HCI limited to thousands or even hundreds of records, feature-based approaches are still widely used in visual analysis of graphical user interfaces (UIs). In our paper, we benchmarked the predictive accuracy of the two types of neural network (NN) models, and explored the effects of the number of features, and the dataset volume. To this end, we used two datasets that comprised over 4000 webpage screenshots, assessed by 233 subjects per the subjective dimensions of Complexity, Aesthetics and Orderliness. With the experimental data, we constructed and trained 1908 models. The feature-based NNs demonstrated 16.2%-better mean squared error (MSE) than the convolutional NNs (a modified GoogLeNet architecture); however, the CNNs’ accuracy improved with the larger dataset volume, whereas the ANNs’ did not: therefore, provided that the effect of more data on the models’ error improvement is linear, the CNNs should become superior at dataset sizes over 3000 UIs. Unexpectedly, adding more features to the NN models caused the MSE to somehow increase by 1.23%: although the difference was not significant, this confirmed the importance of careful feature engineering.
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Lee, Jae Seung, Tae Seop Lim, Hye Won Lee, Seung Up Kim, Jun Yong Park, Do Young Kim, Sang Hoon Ahn, et al. "Suboptimal Performance of Hepatocellular Carcinoma Prediction Models in Patients with Hepatitis B Virus-Related Cirrhosis." Diagnostics 13, no. 1 (December 20, 2022): 3. http://dx.doi.org/10.3390/diagnostics13010003.

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This study aimed to evaluate the predictive performance of pre-existing well-validated hepatocellular carcinoma (HCC) prediction models, established in patients with HBV-related cirrhosis who started potent antiviral therapy (AVT). We retrospectively reviewed the cases of 1339 treatment-naïve patients with HBV-related cirrhosis who started AVT (median period, 56.8 months). The scores of the pre-existing HCC risk prediction models were calculated at the time of AVT initiation. HCC developed in 211 patients (15.1%), and the cumulative probability of HCC development at 5 years was 14.6%. Multivariate Cox regression analysis revealed that older age (adjusted hazard ratio [aHR], 1.023), lower platelet count (aHR, 0.997), lower serum albumin level (aHR, 0.578), and greater LS value (aHR, 1.012) were associated with HCC development. Harrell’s c-indices of the PAGE-B, modified PAGE-B, modified REACH-B, CAMD, aMAP, HCC-RESCUE, AASL-HCC, Toronto HCC Risk Index, PLAN-B, APA-B, CAGE-B, and SAGE-B models were suboptimal in patients with HBV-related cirrhosis, ranging from 0.565 to 0.667. Nevertheless, almost all patients were well stratified into low-, intermediate-, or high-risk groups according to each model (all log-rank p < 0.05), except for HCC-RESCUE (p = 0.080). Since all low-risk patients had cirrhosis at baseline, they had unneglectable cumulative incidence of HCC development (5-year incidence, 4.9–7.5%). Pre-existing risk prediction models for patients with chronic hepatitis B showed suboptimal predictive performances for the assessment of HCC development in patients with HBV-related cirrhosis.
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Nishida, Nao, Jun Ohashi, Goki Suda, Takehiro Chiyoda, Nobuharu Tamaki, Takahiro Tomiyama, Sachiko Ogasawara, et al. "Prediction Model with HLA-A*33:03 Reveals Number of Days to Develop Liver Cancer from Blood Test." International Journal of Molecular Sciences 24, no. 5 (March 1, 2023): 4761. http://dx.doi.org/10.3390/ijms24054761.

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The development of liver cancer in patients with hepatitis B is a major problem, and several models have been reported to predict the development of liver cancer. However, no predictive model involving human genetic factors has been reported to date. For the items incorporated in the prediction model reported so far, we selected items that were significant in predicting liver carcinogenesis in Japanese patients with hepatitis B and constructed a prediction model of liver carcinogenesis by the Cox proportional hazard model with the addition of Human Leukocyte Antigen (HLA) genotypes. The model, which included four items—sex, age at the time of examination, alpha-fetoprotein level (log10AFP) and presence or absence of HLA-A*33:03—revealed an area under the receiver operating characteristic curve (AUROC) of 0.862 for HCC prediction within 1 year and an AUROC of 0.863 within 3 years. A 1000 repeated validation test resulted in a C-index of 0.75 or higher, or sensitivity of 0.70 or higher, indicating that this predictive model can distinguish those at high risk of developing liver cancer within a few years with high accuracy. The prediction model constructed in this study, which can distinguish between chronic hepatitis B patients who develop hepatocellular carcinoma (HCC) early and those who develop HCC late or not, is clinically meaningful.
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Lim, Jihye, Young Eun Chon, Mi Na Kim, Joo Ho Lee, Seong Gyu Hwang, Han Chu Lee, and Yeonjung Ha. "Cirrhosis, Age, and Liver Stiffness-Based Models Predict Hepatocellular Carcinoma in Asian Patients with Chronic Hepatitis B." Cancers 13, no. 22 (November 9, 2021): 5609. http://dx.doi.org/10.3390/cancers13225609.

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Objectives: Predicting hepatocellular carcinoma (HCC) in patients with chronic hepatitis B who received long-term therapy with potent nucleos(t)ide analogs is of utmost importance to refine the strategy for HCC surveillance. Methods: We conducted a multicenter retrospective cohort study to validate the CAGE-B and SAGE-B scores, HCC prediction models developed for Caucasian patients receiving entecavir (ETV) or tenofovir (TFV) for >5 years. Consecutive patients who started ETV or TFV at two hospitals in Korea from January 2009 to December 2015 were identified. The prediction scores were calculated, and model performance was assessed using receiver operating characteristics (ROC) curves. Results: Among 1557 patients included, 57 (3.7%) patients had HCC during a median follow-up of 93 (95% confidence interval, 73–119) months. In the entire cohort, CAGE-B predicted HCC with an area under the ROC curve of 0.78 (95% CI, 0.72–0.84). Models that have “liver cirrhosis” in the calculation, such as AASL (0.79 (0.72–0.85)), CU-HCC (0.77 (0.72–0.82)), and GAG-HCC (0.79 (0.74–0.85)), showed accuracy similar to that of CAGE-B (p > 0.05); however, models without “liver cirrhosis”, including SAGE-B (0.71 (0.65–0.78)), showed a lower predictive ability than CAGE-B. CAGE-B performed well in subgroups of patients treated without treatment modification (0.81 (0.73–0.88)) and of male sex (0.79 (0.71–0.86)). Conclusions: This study validated the clinical usefulness of the CAGE-B score in a large number of Asian patients treated with long-term ETV or TFV. The results could provide the basis for the reappraisal of HCC surveillance strategies and encourage future prospective validation studies with liver stiffness measurements.
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Li, Huikai, Han Mu, Yajie Xiao, Zhikun Zhao, Xiaoli Cui, and Dongfang Wu. "Comprehensive Analysis of Histone Modifications in Hepatocellular Carcinoma Reveals Different Subtypes and Key Prognostic Models." Journal of Oncology 2022 (August 1, 2022): 1–20. http://dx.doi.org/10.1155/2022/5961603.

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Histone modification, an important epigenetic mechanism, is related to the carcinogenesis of hepatocellular carcinoma (HCC). In three datasets, we screened 88 epigenetic-dysregulated PCGs (epi-PCGs) , which were significantly associated with HCC survival and could cluster HCC into three molecular subtypes. These subtypes were associated with prognosis, immunomodulatory alterations, and response to different treatment strategies. Based on 88 epi-PCGs in the TCGA training set, a risk prediction model composed of 4 epi-PCGs was established. The model was closely related to the clinicopathological features and showed a strong predictive ability in different clinical subgroups. In addition, the risk prediction model was an independent prognostic factor for patients with HCC. The significance of epi-PCGs in HCC is revealed by our data analysis.
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Li, Yao, Wei Wang, Weisheng Zeng, Jianjun Wang, and Jinghui Meng. "Development of Crown Ratio and Height to Crown Base Models for Masson Pine in Southern China." Forests 11, no. 11 (November 19, 2020): 1216. http://dx.doi.org/10.3390/f11111216.

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Crown ratio (CR) and height to crown base (HCB) are important crown characteristics influencing the behavior of forest canopy fires. However, the labor-intensive and costly measurement of CR and HCB have hindered their wide application to forest fire management. Here, we use 301 sample trees collected in 11 provinces in China to produce predictive models of CR and HCB for Masson pine forests (Pinus massoniana Lamb.), which are vulnerable to forest canopy fires. We first identified the best basic model that used only diameter at breast height (DBH) and height (H) as independent variables to predict CR and HCB, respectively, from 11 of the most used potential candidate models. Second, we introduced other covariates into the best basic model of CR and HCB and developed the final CR and HCB predictive models after evaluating the model performance of different combinations of covariates. The results showed that the Richards form of the candidate models performed best in predicting CR and HCB. The final CR model included DBH, H, DBH0.5 and height-to-diameter ratio (HDR), while the final HCB model was the best basic model (i.e., it did not contain any other covariates). We hope that our CR and HCB predictive models contribute to the forest crown fire management of Masson pine forests.
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Dissertations / Theses on the topic "Predictive HCI models"

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Miniukovich, Aliaksei. "Computational Aesthetics in HCI: Towards a Predictive Model of Graphical User Interface Aesthetics." Doctoral thesis, Università degli studi di Trento, 2016. https://hdl.handle.net/11572/368110.

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This thesis describes the development and validation of a predictive model of graphical user interface (GUI) aesthetics. The development was informed by the processing-fluency theory of aesthetic pleasure and involved outlining several visual dimensions of GUI designs, which could affect aesthetics impression. Each of the dimensions was grounded in theory and represents a unique visual aspect of GUI design. The resulting model automatically evaluates the design dimensions and combines them in an estimate of the average impression that GUI appearance would make on the user population. The model was validated in a number of user studies proving high validity and reliability. The model outputs an aesthetics score ofuser impression and could inform the creation of more beautiful GUIs by highlighting which of the design dimensions could be improved. The thesis describes the studies that validated the model on several types of GUIs and demonstrated a potential application of the model in future research and practice.
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Newman, Randa E. "Hypertrophic Cardiomyopathy Genotype Prediction Models in a Pediatric Population." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1459529335.

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Puttige, Anjan Rao. "Improvements on HCM Model for g-function Generation and Illustration of Fluid Temperature Prediction." Thesis, KTH, Energiteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-201032.

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Ground coupled heat pump (GCHP) is a promising solution to the problem of efficient heating/cooling of residential and commercial buildings. Long term modelling of borehole heat exchanger (BHE) is necessary for better design and operation of GCHP. The definition of the boundary condition at the borehole wall has been of particular interest in the modelling of BHE. Equal and uniform heat flux at all the borehole walls is the simplest boundary condition that is used in most analytical models and in some numerical models as well. Uniform temperature at the borehole wall with an imposed total heat flux is more accurate boundary condition since the boreholes are generally hydraulically connected in parallel and the variation of average fluid temperature is negligible along the depth of the borehole, but it is difficult to implement.This thesis presents improvements to a numerical model that uses a fictitious Highly Conductive Material (HCM) that is embedded in the boreholes is used to implement the uniform temperature boundary condition at the borehole wall. A high variation of heat flux at the ends of the borehole was observed in the HCM model. The presence of borehole resistance between the circulating fluid and the borehole wall means that the uniform fluid temperature boundary condition does not imply uniform temperature at the borehole wall. A thin thermally resistive layer was introduced between the HCM and the borehole wall to represent borehole resistance and a semi-circular part was added at the bottom of the borehole to have even distribution of heat flux. The improved model (enhanced HCM, EHCM ) was validated by comparing the g-function generated by the EHCM model with an analytical solution.The EHCM model was illustrated by simulating the operation of a monitored installation located in Baiona, Spain. The model predicted the daily average fluid temperature with reasonable accuracy in comparison with measured data. The reason for some of the inaccuracy was explained. The EHCM model was compared with other models including a commercial software.
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Högye, Sebastian, and Tommie Andersson. "Konkursprediktionsmodeller Inom Tillverknings- och detaljhandelsbranschen." Thesis, Södertörns högskola, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-41318.

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Research question: Three models, Z``-score, O-score and Skogsvik HCA model, will be used in this study to examine Swedish companies who has gone bankrupt over the last decade within the manufacturing and retail branches. The study will examine how these models stand against each other when it comes to predict bankruptcy within these two branches one and two years in advance. Purpose: The purpose with this study is to examine these three models that are used for bankruptcy prediction and to get an understanding of why the accuracy differs between the models when it comes to predicting bankruptcy within the manufacturing and retail branches. Method: The study is based on a quantitative method with a deductive research approach to examine the accuracy of the three models when it comes to one and two years before bankruptcy. Conclusion: The study shows that Skogsvik’s model is the most accurate when it comes to predicting bankruptcy within the manufacturing and retail branches.
Problemställning: Tre modeller, Z``-scoremodellen, O-scoremodellen och skogsviks HCA modell, kommer att användas i vår studie för att undersöka svenska aktiebolag som gått i konkurs det senaste decenniet inom tillverkningsbranschen och detaljhandelsbranschen. Studien kommer undersöka hur dessa tre modeller står sig mot varandra när det kommer till att förutspå konkurser inom tillverknings- och detaljhandelsbranschen under en prediktionstid på både ett och två år i förväg. Syfte: Syftet med uppsatsen är att undersöka tre olika modeller som används för konkursprediktion och få en förståelse varför träffsäkerheten skiljer sig mellan de olika modellerna när det gäller att förutse konkurs inom tillverkningsbranschen och detaljhandelsbranschen. Metod: Studien bygger på en kvantitativ metod med en deduktiv ansats för att undersöka hur stor träffsäkerhet som redan befintliga modeller har vid förutsägelser av framtida konkurser på upp till två år. Slutsats: Studien visar att Skogsviks modell är den som är mest träffsäker när det gäller att förutse konkurser inom tillverknings- och detaljhandelsbranschen.
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Salvatore, Veronica <1982&gt. "Changes in tumor stiffness for early prediction of tumor response to sorafenib: a proof-of-concept study with elastosonography in an animal model of Hepatocellular Carcinoma (HCC)." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amsdottorato.unibo.it/3481/1/salvatore_veronica_tesi.pdf.

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Background and aims: Sorafenib is the reference therapy for advanced Hepatocellular Carcinoma (HCC). No method exists to predict in the very early period subsequent individual response. Starting from the clinical experience in humans that subcutaneous metastases may rapidly change consistency under sorafenib and that elastosonography a new ultrasound based technique allows assessment of tissue stiffness, we investigated the role of elastonography in the very early prediction of tumor response to sorafenib in a HCC animal model. Methods: HCC (Huh7 cells) subcutaneous xenografting in mice was utilized. Mice were randomized to vehicle or treatment with sorafenib when tumor size was 5-10 mm. Elastosonography (Mylab 70XVG, Esaote, Genova, Italy) of the whole tumor mass on a sagittal plane with a 10 MHz linear transducer was performed at different time points from treatment start (day 0, +2, +4, +7 and +14) until mice were sacrified (day +14), with the operator blind to treatment. In order to overcome variability in absolute elasticity measurement when assessing changes over time, values were expressed in arbitrary units as relative stiffness of the tumor tissue in comparison to the stiffness of a standard reference stand-off pad lying on the skin over the tumor. Results: Sor-treated mice showed a smaller tumor size increase at day +14 in comparison to vehicle-treated (tumor volume increase +192.76% vs +747.56%, p=0.06). Among Sor-treated tumors, 6 mice showed a better response to treatment than the other 4 (increase in volume +177% vs +553%, p=0.011). At day +2, median tumor elasticity increased in Sor-treated group (+6.69%, range –30.17-+58.51%), while decreased in the vehicle group (-3.19%, range –53.32-+37.94%) leading to a significant difference in absolute values (p=0.034). From this time point onward, elasticity decreased in both groups, with similar speed over time, not being statistically different anymore. In Sor-treated mice all 6 best responders at day 14 showed an increase in elasticity at day +2 (ranging from +3.30% to +58.51%) in comparison to baseline, whereas 3 of the 4 poorer responders showed a decrease. Interestingly, these 3 tumours showed elasticity values higher than responder tumours at day 0. Conclusions: Elastosonography appears a promising non-invasive new technique for the early prediction of HCC tumor response to sorafenib. Indeed, we proved that responder tumours are characterized by an early increase in elasticity. The possibility to distinguish a priori between responders and non responders based on the higher elasticity of the latter needs to be validated in ad-hoc experiments as well as a confirmation of our results in humans is warranted.
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Salvatore, Veronica <1982&gt. "Changes in tumor stiffness for early prediction of tumor response to sorafenib: a proof-of-concept study with elastosonography in an animal model of Hepatocellular Carcinoma (HCC)." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amsdottorato.unibo.it/3481/.

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Background and aims: Sorafenib is the reference therapy for advanced Hepatocellular Carcinoma (HCC). No method exists to predict in the very early period subsequent individual response. Starting from the clinical experience in humans that subcutaneous metastases may rapidly change consistency under sorafenib and that elastosonography a new ultrasound based technique allows assessment of tissue stiffness, we investigated the role of elastonography in the very early prediction of tumor response to sorafenib in a HCC animal model. Methods: HCC (Huh7 cells) subcutaneous xenografting in mice was utilized. Mice were randomized to vehicle or treatment with sorafenib when tumor size was 5-10 mm. Elastosonography (Mylab 70XVG, Esaote, Genova, Italy) of the whole tumor mass on a sagittal plane with a 10 MHz linear transducer was performed at different time points from treatment start (day 0, +2, +4, +7 and +14) until mice were sacrified (day +14), with the operator blind to treatment. In order to overcome variability in absolute elasticity measurement when assessing changes over time, values were expressed in arbitrary units as relative stiffness of the tumor tissue in comparison to the stiffness of a standard reference stand-off pad lying on the skin over the tumor. Results: Sor-treated mice showed a smaller tumor size increase at day +14 in comparison to vehicle-treated (tumor volume increase +192.76% vs +747.56%, p=0.06). Among Sor-treated tumors, 6 mice showed a better response to treatment than the other 4 (increase in volume +177% vs +553%, p=0.011). At day +2, median tumor elasticity increased in Sor-treated group (+6.69%, range –30.17-+58.51%), while decreased in the vehicle group (-3.19%, range –53.32-+37.94%) leading to a significant difference in absolute values (p=0.034). From this time point onward, elasticity decreased in both groups, with similar speed over time, not being statistically different anymore. In Sor-treated mice all 6 best responders at day 14 showed an increase in elasticity at day +2 (ranging from +3.30% to +58.51%) in comparison to baseline, whereas 3 of the 4 poorer responders showed a decrease. Interestingly, these 3 tumours showed elasticity values higher than responder tumours at day 0. Conclusions: Elastosonography appears a promising non-invasive new technique for the early prediction of HCC tumor response to sorafenib. Indeed, we proved that responder tumours are characterized by an early increase in elasticity. The possibility to distinguish a priori between responders and non responders based on the higher elasticity of the latter needs to be validated in ad-hoc experiments as well as a confirmation of our results in humans is warranted.
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DE, GOTZEN Amalia. "An audio perspective over predictive HCI models." Doctoral thesis, 2007. http://hdl.handle.net/11562/337781.

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In our everyday life we perform pointing tasks hundred times a day. These tasks are very different according to the purpose of the gesture. We might be quite accurate and fast when we are putting a stamp on a precise place of a number of documents, while we might be just fast and less accurate when we are indicating the direction to take in a crossing. There are many examples of pointing tasks, and each of them is characterized by a precise feedback (visual, haptic or auditory) and by a precise trade off between accuracy and speed. This thesis concentrates in particular on Fitts’ tasks performed under continuous auditory feedback: it hypothesizes the influence of the feedback on velocity profiles and explores possible transpositions of Fitts’ parameters in the auditory domain. The thesis is organized in 7 chapters. The first chapter (cf. 1) introduces the history of the field which constitutes the background of this thesis, starting from the definitions of Human Factors and its historical links with Human-Computer Interaction discipline. Focusing on this branch of the computer science the relationship with Human Performance and Human Information processing is described. Some predictive models used in HCI are presented as a conclusion of the chapter. This short presentation introduces the second chapter, (cf. 2) which is devoted to the discussion of some predictive HCI models, in particular Fitt’s law, Schmidt’s law and the steering law. All these predictive models are used to evaluate the user performance with particular interfaces — visual interfaces in the large majority of cases. In the first part of the chapter the origins of Fitts’law and its appearence inside the Information theory are presented. Through the discussion of the main aspects under debate is then described the formultion of the law that is currently adopeted in the ISO9621. Different derivation of the law are then reported with the main formulation that have been developed along the years of Human Performance research. Particular attention is given to the deterministic iterativecorrection model, the impulse variability model and the optimized initial impulse model. Shifting from the literature in Human Performance to the one in Human Computer Interaction the last part of the chapter is devoted to the main topics still under discussion on Fitt’s law, the open problems and the main achievements of the last years of research. A presentation of Schmidt’s law and of the steering law conclude the chapter. Chapter three (cf. 3) is devoted to the interface/control part: focusing on the audio domain, it explains what an interface is and how audio feedback can be used beyond the design of musical instruments. Different control metaphors are introduced as well as a definition of gesture. The gesture of the user is the means to produce audio feedback and the gesture that is analyzed in this thesis is a typical Fitts’ task–like gesture. The literature of the “sounding gesture” is discussed through the the main european projects that in the last years studied in different perspective the conjunction of gesture and sound, taking into account two different aspects: the control and the expressiveness. Chapter four (cf. 4) explores how audio feedback has been used to date in interface design in Human-Computer Interaction: starting from the auditory display research about auditory icons and earcons through the new field of research on interactive sonification if naturally reaches the literature on the specific topic of the thesis wich is the use the auditory feedback in conjunction with Fitts’ law studies. The main achievements in this respect are summarized there including the open problems that this thesis has set to address. Chapter five (cf. 5) and six (cf. 6) are devoted to experiments: in particular, chapter five describes four different experiments with four different interfaces. These experiments allow to draw some conclusions and to suggest further experiments. A couple of final experiments are then summarized in chapter 6. The data analysis of these tests drives the conclusions of chapter seven (cf. 7) with which this work ends.
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Books on the topic "Predictive HCI models"

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Cheng shi di zhen zai hai feng xian fen xi mo xing yan jiu: Yi Fuzhou Shi wei li. Beijing Shi: Beijing li gong da xue chu ban she, 2012.

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Alvin, Toffler. Wealth 3.0: Tuo fo le cai fu ge ming. Tai bei shi: Shi bao wen hua, 2007.

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Oulasvirta, Antti, and Andreas Karrenbauer. Combinatorial Optimization for User Interface Design. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0005.

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Combinatorial optimization offers a rigorous but powerful approach to user interface design problems, defining problems mathematically such that they can be algorithmically solved. Design is defined as algorithmic combination of design decisions to obtain an optimal solution defined by an objective function. There are strong rationale for this method. First, core concepts such as ’design task’, ’design objective’, and ’optimal design’ become explicit and actionable. Second, solutions work well in practice, even for some problems traditionally out of reach of manual solutions. The method can assist in the generation, refinement, and adaptation of design. However, mathematical expression of HCI problems has been challenging and curbed applications. This chapter introduces combinatorial optimisation from user interface design point of view, and addresses two core challenges: 1) mathematical definition of design problems and 2) expression of evaluative knowledge such as design heuristics and predictive models of interaction.
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O’Mahony, Constantinos. Hypertrophic cardiomyopathy: prevention of sudden cardiac death. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198784906.003.0354.

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Sudden cardiac death (SCD) secondary to ventricular arrhythmias is the most common mode of death in hypertrophic cardiomyopathy (HCM) and can be effectively prevented with an implantable cardioverter defibrillator (ICD). The risk of SCD in HCM relates to the severity of the phenotype and regular risk stratification is an integral part of routine clinical care. For the primary prevention of SCD, risk stratification involves the assessment of seven readily available clinical parameters (age, maximal left ventricular wall thickness, left atrial diameter, left ventricular outflow tract gradient, non-sustained ventricular tachycardia, unexplained syncope, and family history of SCD) which are used to estimate the risk of SCD within 5 years of clinical evaluation using a statistical risk prediction model (HCM Risk-SCD). The 2014 European Society of Cardiology Guidelines provide a framework to aid clinical decisions and consider patients with a 5-year risk of SCD of less than 4% as low risk and recommend regular assessment while those with a risk of 6% or higher should be considered for an ICD. In patients with an intermediate risk (4% to <6%) ICD implantation may also be considered after taking into account age, co-morbid conditions, socioeconomic factors, and the psychological impact of therapy. Survivors of ventricular fibrillation arrest should receive an ICD for secondary prevention unless their life expectancy is less than 1 year. Following device implantation, patients should be followed up for device- and disease-related complications, particularly heart failure and cerebrovascular disease.
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Book chapters on the topic "Predictive HCI models"

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Freitas, Rômulo, and Luciana Salgado. "Educators in the Loop: Using Scenario Simulation as a Tool to Understand and Investigate Predictive Models of Student Dropout Risk in Distance Learning." In Artificial Intelligence in HCI, 255–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50334-5_17.

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Johansson, Rolf, Per Tunestål, and Anders Widd. "Modeling and Model-based Control of Homogeneous Charge Compression Ignition (HCCI) Engine Dynamics." In Automotive Model Predictive Control, 89–104. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-071-7_6.

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Halbrügge, Marc. "Explaining and Predicting Sequential Error in HCI with Cognitive User Models." In Predicting User Performance and Errors, 53–85. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60369-8_6.

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Lee, Yen-Hsien, Tin-Kwang Lin, Yu-Yang Huang, and Tsai-Hsin Chu. "An Ensemble Learning Method for Constructing Prediction Model of Cardiovascular Diseases Recurrence." In HCI in Business, Government and Organizations, 208–20. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05544-7_16.

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Mijović, Pavle, Miloš Milovanović, Miroslav Minović, Ivan Mačužić, Vanja Ković, and Ivan Gligorijević. "Towards Creation of Implicit HCI Model for Prediction and Prevention of Operators’ Error." In Human-Computer Interaction: Interaction Technologies, 341–52. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20916-6_32.

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Hu, Yi-Chung, Hang Jiang, Peng Jiang, and Peiyi Kong. "An Improved Grey Multivariable Verhulst Model for Predicting CO2 Emissions in China." In HCI in Business, Government and Organizations. Information Systems and Analytics, 354–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22338-0_29.

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Barradas, Isabel, Reinhard Tschiesner, and Angelika Peer. "Towards a Dynamic Model for the Prediction of Emotion Intensity from Peripheral Physiological Signals." In HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments, 18–35. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-17618-0_2.

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Oppedal, A. L., H. El Kadiri, C. N. Tomé, J. C. Baird, S. C. Vogel, and M. F. Horstemeyer. "Limitation of current hardening models in predicting anisotropy by twinning in hcp metals: Application to a rod-textured AM30 magnesium alloy." In Magnesium Technology 2011, 313–20. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-319-48223-1_59.

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Oppedal, A. L., H. El Kadiri, C. N. Tomé, J. C. Baird, S. C. Vogel, and M. F. Horstemeyer. "Limitation of Current Hardening Models in Predicting Anisotropy by Twinning in HCP Metals: Application to a Rod-textured AM30 Magnesium Alloy." In Magnesium Technology 2011, 313–20. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118062029.ch59.

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Cuong, Nguyen Huy, and Pham Huy Giao. "A New Rainfall Threshold Model for Predicting Ground Movement Occurrences Based on Geotechnical Monitoring Data the at the Hai Van Pass." In Lecture Notes in Civil Engineering, 1209–16. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-2184-3_159.

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Conference papers on the topic "Predictive HCI models"

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Ajenaghughrure, Ighoyota Ben, Sonia C. Sousa, Ilkka Johannes Kosunen, and David Lamas. "Predictive model to assess user trust." In IndiaHCI '19: India HCI 2019. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3364183.3364195.

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Ma, Lijuan, Zhaoxing Chen, Xiao-li Ji, Feng Yan, and Yi Shi. "Multi-parameter model for HCI lifetime prediction." In 2012 IEEE 11th International Conference on Solid-State and Integrated Circuit Technology (ICSICT). IEEE, 2012. http://dx.doi.org/10.1109/icsict.2012.6467651.

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Zhu, Lin, Jiaxing Lu, and Yihong Chen. "HDI-Forest: Highest Density Interval Regression Forest." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/621.

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By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical problems. Currently, the state-of-the-art quality-based PI estimation methods are based on deep neural networks or linear models. In this paper, we propose Highest Density Interval Regression Forest (HDI-Forest), a novel quality-based PI estimation method that is instead based on Random Forest. HDI-Forest does not require additional model training, and directly reuses the trees learned in a standard Random Forest model. By utilizing the special properties of Random Forest, HDI-Forest could efficiently and more directly optimize the PI quality metrics. Extensive experiments on benchmark datasets show that HDI-Forest significantly outperforms previous approaches, reducing the average PI width by over 20% while achieving the same or better coverage probability.
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Chen, Tao, Yabin Sun, Yanling Shi, Xiaojin Li, and Yun Liu. "An Applied Model for HCI and Lifetime Prediction of LDMOSFET." In 2022 IEEE 16th International Conference on Solid-State & Integrated Circuit Technology (ICSICT). IEEE, 2022. http://dx.doi.org/10.1109/icsict55466.2022.9963390.

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Cacho, F., P. Mora, W. Arfaoui, X. Federspiel, and V. Huard. "HCI/BTI coupled model: The path for accurate and predictive reliability simulations." In 2014 IEEE International Reliability Physics Symposium (IRPS). IEEE, 2014. http://dx.doi.org/10.1109/irps.2014.6860673.

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Gupta, Shriya, and Geeta Sikka. "Explaining HCV prediction using LIME model." In 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC). IEEE, 2021. http://dx.doi.org/10.1109/icsccc51823.2021.9478092.

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Kongsereeparp, Paitoon, Behzad Kashani, and M. David Checkel. "A Stand-Alone Multi-Zone Model for Combustion in HCCI Engines." In ASME 2005 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/icef2005-1241.

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Because they have the potential for ultra low NOx emissions and high efficiency, Homogeneous Charge Compression Ignition (HCCI) engines have the potential to develop a significant niche. However, a narrow operating range, (bracketed by severe knock and misfire problems), presents a formidable obstacle to developing usable HCCI combustion systems. HCCI combustion is influenced by a complex array of operating variables including fuel octane quality, intake preheating temperature, compression ratio, equivalence ratio, exhaust gas recirculation and engine component temperature. These variables affect the two critical combustion parameters: ignition timing and combustion duration. If these two parameters can be controlled by appropriate settings of the operating variables, a good HCCI combustion scheme could be achieved. Therefore, the theoretical prediction of these two combustion parameters as a function of the key operating variables is necessary for development of HCCI combustion. This paper describes a stand-alone, single-zone and multi-zone combustion model which have been developed for the specific purpose of investigating HCCI combustion control. In the multi-zone model, temperature and composition in each zone were adjusted in order to study the effect of in-homogeneity which is critical to understanding ignition timing and combustion duration in real HCCI engines. The models simulated HCCI combustion using two fuels: hydrogen, (11 species, 23 reactions- from CHEMKIN library), and natural gas, (53 species, 325 reactions- from GRI mech). The capabilities of the two models to predict ignition timing, combustion duration and peak pressure were verified against experimental and simulation results of Fiveland et al [2, 11]. The models were then used to study the effect of different in-homogeneity levels of equivalence ratio, intake temperature and residual fraction. The single zone model could only predict ignition timing while the multi-zone model shows the capability to mimic realistic HCCI combustion phenomena. The study showed that some degree of in-homogeneity is critical to predicting performance of the homogeneous charge compression ignition engine. Further, stratification of equivalence ratio was relatively ineffective at changing combustion while stratification of mixture temperature was very effective. Stratification of the residual fraction proved to be the most promising method of controlling combustion parameters and the mechanism was primarily thermal.
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Szasz, Christoph, and Sven Lauer. "Sensitivity Analysis of Cylinder Head Fatigue Life Prediction Using Statistical Models." In ASME 2014 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/icef2014-5559.

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For the efficient virtual development of combustion engine cylinder heads in terms of high cycle fatigue (HCF) it is highly important to have a reliable development process that represents reality in the best possible way. Most of today’s standard HCF procedures are capable of delivering high quality results for a specific load combination. However, loads are usually subject to variation. This is also valid for loads the cylinder head is subjected to. Assembly loads and operating loads considered during the virtual development process are widely determined by the production process which again is subject to variation due to certain tolerances, wear of the tooling equipment etc. As it is highly important to ensure the fatigue design of a cylinder head, there is the need for new analysis models capable of capturing every possible load variation. Within the framework of this paper the influence of different variable loading parameters on the cylinder head HCF margin of a heavy duty diesel engine will be discussed. A design of experiments (DoE) analysis is used together with the 3-d finite element method (FEM) for the investigations. Furthermore a methodology for the probabilistic assessment of the cylinder head HCF margin based on stochastic loading data is introduced. At the same time an effective methodology for the identification of the worst case boundary conditions for HCF analysis will be presented. With the presented probabilistic method it is possible to achieve a highly accurate prediction of the HCF design margin. Due to the probabilistic approach a better understanding of the entire system is possible, as the interaction between input and output parameters can be illustrated. Therefore HCF optimization problems can be encountered more effectively. Furthermore the presented methodology can be used for error estimation of analysis results and assessment of the result sensitivity. Thus, a borderline layout of the cylinder head can be achieved. Also the minimum input information quality, which is required for a profound HCF analysis, can be assessed by using the sensitivity analysis presented. Therefore the proposed methods enable a fast and reliable development of cylinder heads and other combustion engine components.
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Sun, Xiaohua, Jinglu Li, and Weiwei Guo. "A design approach of proactive HMI based on smart interaction." In Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002823.

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As AI advances, intelligent systems are gaining the ability to collaborate with humans to accomplish everyday tasks proactively. In proactive HMI design, the accuracy of the user intention prediction model in the mechanism becomes the key to affecting the quality of the proactive HMI experience. However, there are three issues that caused the lack of effective ways to improve the prediction accuracy of user prediction models. In this paper, we analyze the Information for improving user prediction accuracy, the Intervention stage, and the required contents for smart interaction. Then, we develop an approach of the proactive HMI based on smart interaction, which is the method that robots learn from the users through interactions. We propose the elements, the framework, and the guidelines. This paper also provides how to use this approach in design case. With this approach, the accuracy of user intention prediction of proactive HMI can be improved and then can be achieved the goal of improving the design effect and the user experience of proactive HMI can be achieved.
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Thiele, Marcus, Swen Weser, Uwe Gampe, Roland Parchem, and Samuel Forest. "Advancement of Experimental Methods and Cailletaud Material Model for Life Prediction of Gas Turbine Blades Exposed to Combined Cycle Fatigue." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68452.

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The European project PREMECCY has been conducted to enhance predictive methods for combined cycle fatigue (CCF) of gas turbine blades, i.e. interaction of low cycle fatigue (LCF) and high cycle fatigue (HCF). While design of CCF feature tests, comprising specimen and test rig design, has already been reported, this paper presents experimental HCF/ CCF test results and progress in life prediction. Besides standard lab specimen tests for characterization of single crystal and conventional cast material, also advanced specimens representing critical rotor blade features were tested in a hot gas rig. Based on these experimental data an extended Cailletaud material model for stress-strain analysis has been calibrated and combined with a modified ONERA damage model for creep-fatigue interaction to estimate the lifetime of the advanced test specimens. The model extensions address the effect of ratcheting, which is typical for CMSX-4 at asymmetric cyclic loading at elevated temperature. Caused by limitations of the Armstrong-Frederick kinematic hardening rule regarding ratcheting, three models for improved ratcheting simulation of isotropic material were adopted to anisotropic material. In addition multiple Norton-flow rules for the viscous part of the model are combined with time recovery terms in the kinematic hardening evolution to represent the behaviour of single crystal material in high temperature environment at a wide range of strain rates. Hence, an improved model for stress-strain and lifetime prediction for single crystals has been developed.
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Reports on the topic "Predictive HCI models"

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Schutt, Timothy, and Manoj Shukla. Predicting the impact of aqueous ions on fate and transport of munition compounds. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41481.

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A model framework for natural water has been developed using computational chemistry techniques to elucidate the interactions between solvated munition compounds and eight common ions in naturally occurring water sources. The interaction energies, residence times, coordination statistics, and surface preferences of nine munition related compounds with each ion were evaluated. The propensity of these interactions to increase degradation of the munition compound was predicted using accelerated replica QM/MM simulations. The degradation prediction data qualitatively align with previous quantum mechanical studies. The results suggest that primary ions of interest for fate and transport modeling of munition compounds in natural waters may follow the relative importance of SO₄²⁻, Cl⁻ ≫ HCO₃⁻, Na⁺, Mg²⁺ > Ca²⁺, K⁺, and NH₄⁺.
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