Prediction Of Timing Research Articles

CN7 Prediction of onset timing of breakthrough pain using deep learning model

Breakthrough cancer pain (BTcP), a transitory flare of pain that occurs on a background of relatively well-controlled baseline pain, is a challenging clinical problem in managing cancer pain. We hypothesized that the breakthrough pain could be predictable according to the patients’ previous observed patterns. In this study, we propose a deep learning model that predicts a patient’s level of BTcP per hour.

MP54-01 A SYSTEMATIC REVIEW OF CLINICAL SIGNS AND SYMPTOMS, PREDICTION RULES AND IMAGING MODALITIES IN PATIENTS WITH SUSPECTED RENAL COLIC

You have accessJournal of UrologyStone Disease: Epidemiology & Evaluation II (MP54)1 Sep 2021MP54-01 A SYSTEMATIC REVIEW OF CLINICAL SIGNS AND SYMPTOMS, PREDICTION RULES AND IMAGING MODALITIES IN PATIENTS WITH SUSPECTED RENAL COLIC Philipp Dahm, Alex Koziarz, Charles J. Gerardo, Simranjeet Benipal, Daniel K. Nishijima, Jae Hung Jung, and Ali S. Raja Philipp DahmPhilipp Dahm More articles by this author , Alex KoziarzAlex Koziarz More articles by this author , Charles J. GerardoCharles J. Gerardo More articles by this author , Simranjeet BenipalSimranjeet Benipal More articles by this author , Daniel K. NishijimaDaniel K. Nishijima More articles by this author , Jae Hung JungJae Hung Jung More articles by this author , and Ali S. RajaAli S. Raja More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002084.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Suspected renal colic frequently prompts computed tomography (CT) imaging, which is resource-intense, exposes patients to ionizing radiation, and may not always be necessary. We performed this systematic review (SR) and meta-analysis (MTA) to assess the diagnostic accuracy of clinical signs and symptoms, laboratory investigations, prediction rules and imaging modalities commonly used in patients with clinically suspected renal colic. METHODS: We conducted a SR and MTA according to an a priori, registered protocol (PROSPERO CRD42017055153). Literature search was performed across several databases from inception to July 2, 2020. Two independent reviewers performed study screening, selection, quality assessment and data abstraction using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. RESULTS: Among 7641 references screened, 53 were included in the MTA. The overall pooled prevalence for ureteral stones among patients with clinically suspected renal colic was 63% (95% CI: 58-67%). No individual demographic feature, symptom, or sign when present had an LR+≥2.0. The presence of any RBCs on the UA increased the likelihood of a ureteral stone (sensitivity 0.83; specificity 0.59; LR 2.2, 95% CI 1.3-3.6). A score of ≥10 for the Sex, Timing, Origin, Nausea and Erythrocytes (STONE) clinical prediction rule (range: 0–13) had a sensitivity of 0.49; specificity of 0.91; LR+ of 5.3, 95% CI, 4.1-6.7) whereas a score ≥6 had a sensitivity of 0.94, specificity of 0.43; and was able to rule out stones accurately when <6 (LR of 0.15, 95% CI 0.10-0.22). Low dose CT scanning was the most useful imaging technique for identifying patients with or without ureteral stones (sensitivity 0.93; specificity 0.94; LR+ 17, 95% CI, 8.8-31; LR-0.08; 95% CI, 0.03-0.19), ultrasound (sensitivity 0.74; specificity 0.89; LR+ 7.0, 95% CI, 3.4-14; LR- 0.31; 95% CI, 0.16-0.49), and plain x-ray radiographs (sensitivity 0.55; specificity 0.79; LR+ 2.7, 95% CI, 1.9-3.7; LR- 0.57; 95% CI, 0.46-0.68). CONCLUSIONS: Individual signs, symptoms, or the presence of microscopic hematuria do not substantially impact the likelihood of ureteral stones in patients with clinically suspected renal colic. The STONE score may sufficiently guide physicians’ decision to obtain imaging, however further research is required. Low dose non-contrast CT-imaging provides superior diagnostic accuracy compared to all other imaging index tests. Source of Funding: Departmental © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e948-e948 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Philipp Dahm More articles by this author Alex Koziarz More articles by this author Charles J. Gerardo More articles by this author Simranjeet Benipal More articles by this author Daniel K. Nishijima More articles by this author Jae Hung Jung More articles by this author Ali S. Raja More articles by this author Expand All Advertisement Loading ...

NUMA-aware memory coloring for multicore real-time systems

Non-uniform memory access (NUMA) systems are characterized by varying memory latencies so that execution times may become unpredictable in a multicore real-time system. This results in overly conservative scheduling with low utilization due to loose bounds on a task’s worst-case execution time (WCET). This work contributes a controller/node-aware memory coloring (CAMC) allocator inside the Linux kernel for the entire address space to reduce access conflicts and latencies by isolating tasks from one another. CAMC improves timing predictability and performance over Linux’ buddy allocator and prior coloring methods. It provides core isolation with respect to banks and memory controllers for real-time systems. This work is the first to consider multiple memory controllers in real-time systems, combine them with bank coloring, and assess its performance on a NUMA architecture, to the best of our knowledge.

Calculation and Analysis of Aircraft Pollutant Emission Based on Time Wake Separation Mode under Coastal Airport and Headwind Conditions

During the final approach, the headwind leads to a reduction of landing rate, which affects the achieved capacity and the predictability of operation, time, fuel efficiency, and environmental pollution. Under headwind conditions, ground speed decrease results in increased flight time. Time-based separation (TBS) changes the separation rule of the final approach, which changes the distance separation between two aircrafts into a time separation. This paper introduces the time-based separation (TBS) based on the distance-based separation (DBS). According to the aircraft landing schedule of each airport, the ICAO (International Civil Aviation Organization) aircraft engine emission database, Boeing Fuel Flow Method 2 (BFFM2), and meteorological data of Pu-dong airport, this study uses the modified P3-T3 aviation pollutant emission model to calculate, respectively, the fuel consumption and pollutant emissions based on distance separation mode and time separation mode. According to the calculation results, TBS operation mode can save 32.52%, 19.12%, and 30.41% fuel, reduce 28.93%, 17.9%, and 29.29% CO, 31.02%, 19.36%, and 33.78% HC, 30.85%, 16.42%, and 28.67% NOx, respectively, compared with the DBS operation mode at three stages of the day. It ends that TBS has an obvious optimization effect on fuel consumption and pollutant emission compared with DBS from data.

Data-driven prediction of decannulation probability and timing in patients with severe acquired brain injury

Background and objectivesFrom a rehabilitation perspective, removal of tracheostomy in patients with severe acquired brain injuries (sABI) is a crucial step. Predictive parameters for a successful decannulation are currently still a focus of the research for sABI patients, especially for those presenting a disorder of consciousness. For this reason, we adopted a data-driven approach predicting decannulation probability and timing using ensemble learning models in patients in intensive rehabilitation units. Methods327 patients, 186 of which were successfully decannulated during their intensive rehabilitative stay, were recruited in a non-concurrent retrospective study. Decannulation probability and timing were predicted using data available within one week from admission at the rehabilitation unit. Two predictive models were trained and cross-validated independently, with the first being an ensemble of a Support Vector Machine and Random Forests and the second an Adaptive Boosting with a Support Vector Regression as weak learner. Confusion matrix, accuracy and AUC were considered as evaluation metrics for the classifier and median absolute error was considered for the regressor. To quantify the advantages in the clinical practice of using the latter prediction, we compared timing estimation with a timing guess (median) calculated on available data. The comparison was based on a Wilcoxon signed rank test. ResultsDecannulation probability was successfully predicted with an accuracy of 84.8% (AUC = 0.85) and timing with a median absolute error of 25.7 days [IQR = 25.6]. This resulted in a significant improvement with respect to the weaning time guess (p<0.05) with an effect size of 71.7%. Furthermore, dichotomizing the regression prediction with a threshold (3 months from the event), resulted in a prediction accuracy of 77.5% (AUC = 0.82) on the test set. DiscussionsA model capable of providing a prediction on decannulation probability and timing was developed and cross-validated, built on data taken at admission to the intensive rehabilitation unit. Translated in clinical practice, this information can support the clinical decision process and provide a mean to improve both in-hospital and domiciliary care organization.

O-188 Participation in a video-based fertility awareness program advances the desire to have children sooner

Abstract Study question Participating in a fertility awareness program accelerates the intention to have children 6 to 12 months after video- fertility awareness? Summary answer In combination with positive childbearing motivation, those in the video condition anticipated their intentions to have children at 6-12 months follow-up What is known already Fertility awareness education and initiatives have been focused on increasing fertility awareness and knowledge. However, the role of fertility awareness on reproductive decisions is less explored in literature. There are few studies showing that participating in fertility awareness education is related to higher intentions to have children or decreased time until trying to conceive regarding beforehand planned. Since intentions seem to be a good predictor of behaviour, we were interested in exploring the variables which might predict higher intentions to have children after participating in a fertility awareness education study based on a video intervention Study design, size, duration This study is part of a randomised controlled trial initiated in October 2016. Participants were randomly allocated into the intervention (IG) and control group (CG) at baseline (TO). The follow-up was evaluated 6 to 12 months later. IG participants were exposed to a 5-min video delivering information on age-related fertility decline, infertility risk factors, and pregnancy chances after having filled-in the T0 questionnaire: Participants in CG received no stimulus. Participants/materials, setting, methods Three hundred sixty-five individuals (65% women) were recruited through social media, gynaecology clinics, and religious pre-marital courses. From those, 128 responded to follow-up (between 6 and 12 months). Childbearing Motivation Scale (positive and negative) and a question about childbearing timing intentions were used. Regression analysis was conducted to explore the role of video intervention and childbearing motivation on childbearing timing intention. Main results and the role of chance Participants were, on average, 29.5 years old (SD = 5.13). From baseline to 6-12 months later, participants significantly anticipated their desire to have children (p&amp;lt;.005). Correlations showed that childbearing timing intention was positively and significantly associated with the following subscales of the positive motivations: personal fulfilment, continuity, and couple relationship; negative motivations were not associated with the childbearing timing intention. The regression analysis revealed that only the childbearing motivation regarding couple relationship (having a child would strengthening partnership ties, fulfilling partner’s project, growing as a couple, fulfilling a shared project) and the visualization of the educational video significantly contributed to predicting at follow-up the increased desire to have children sooner at follow-up. Limitations, reasons for caution Other factors, not considered, might contribute to the prediction of childbearing motivation timing. Although we had randomly allocated people to CG and IG, the high attrition rate may limit the generalization of our results. Wider implications of the findings This study seems to indicate that watching an educational video about fertility awareness might contribute to a significant change in childbearing timing. In addition, the importance of the couple relationship is highlighted. These findings reinforce the importance to assist people with informed reproductive decisions in social and healthcare settings. Trial registration number NCT02813993

Where Would You Go this Weekend? Time-Dependent Prediction of User Activity Using Social Network Data

Predicting user activities and interests has many applications, for example, in contextual recommendations. Although the problem of predicting interests in general has been studied extensively, the problem of predicting when the users are likely to act on those interests has received considerably less attention. Such predictions of timing are extremely important when the application itself is time-sensitive (e.g., travel recommendations are irrelevant too far in advance and after reservations have already been made). Particularly important is the ability to predict likely future activities long in advance (as opposed to short-term prediction of imminent activities). In this paper we describe a comprehensive study that addresses this problem of making long-term time-dependent predictions of user interest. We have conducted this study on a large collection of visits to various venues of interest performed by users of Foursquare. We have built models that, given a user's history, can predict whether or not the user will visit a venue of a particular type on a given day. These models provide useful prediction accuracy of up to 75% for up to several weeks into the future. Our study explores and compares various feature sets and prediction methods. Of particular interest is the fact that venues interact with each other: to predict visits to one type of venue, it helps to use the history of visits to all venue types.

Spatial and Temporal Patterns of Ross River Virus in Queensland, 2001-2020.

Ross River virus (RRV), the most common human arbovirus infection in Australia, causes significant morbidity and substantial medical costs. About half of Australian cases occur in Queensland. We describe the spatial and temporal patterns of RRV disease in Queensland over the past two decades. RRV notifications, human population data, and weather data from 2001 to 2020 were analysed by the Statistical Area Level 2 (SA2) area. Spatial interpolation or linear extrapolation were used for missing weather values and the estimated population in 2020, respectively. Notifications and incidence rates were analysed through space and time. During the study period, there were 43,699 notifications in Queensland. The highest annual number of notifications was recorded in 2015 (6182), followed by 2020 (3160). The average annual incidence rate was 5 per 10,000 people and the peak period for RRV notifications was March to May. Generally, SA2 areas in northern Queensland had higher numbers of notifications and higher incidence rates than SA2 areas in southern Queensland. The SA2 areas with high incidence rates were in east coastal areas and western Queensland. The timely prediction may aid disease prevention and routine vector control programs, and RRV management plans are important for these areas.

A Measurement-Based Message-Level Timing Prediction Approach for Data-Dependent SDFGs on Tile-Based Heterogeneous MPSoCs

Fast yet accurate performance and timing prediction of complex parallel data flow applications on multi-processor systems remains a very difficult discipline. The reason for it comes from the complexity of the data flow applications w.r.t. data dependent execution paths and the hardware platform with shared resources, like buses and memories. This combination may lead to complex timing interferences that are difficult to express in pure analytical or classical simulation-based approaches. In this work, we propose the combination of timing measurement and statistical simulation models for probabilistic timing and performance prediction of Synchronous Data Flow (SDF) applications on MPSoCs with shared memories. We exploit the separation of computation and communication in our SDF model of computation to set-up simulation-based performance prediction models following different abstraction approaches. We especially propose a message-level communication model driven by a data-dependent probabilistic execution phase timing model. We compare our work against measurement on two case-studies from the computer vision domain: a Sobel filter and a JPEG decoder. We show that the accuracy and execution time of our modeling and evaluation framework outperforms existing approaches and is suitable for a fast yet accurate design space exploration.

Study of Mental Disorder Prediction Model approaches its issues and Challenges

Mental disorder is becoming one of the major health issues in society today. (WHO) depression will be the leading mental disorder all over the world by 2030. The timely prediction of such disorders is very essential for maintaining the health of human beings. These mental disorders are associated with different symptoms, some of these symptoms are visible in the form of facial expressions, gestures, change in voice modulation, etc. and some of them even cannot be noticed by naked eyes. Therefore, it is highly important to collect appropriate verbal and non-verbal symptom details about any individual along with using best-suited algorithms for accurate prediction using information technology. This paper explores different types of such symptoms associated with different types of mental disorders, their causes, and existing prediction-based solutions. The paper further presents the critical analysis of these aspects and proposes usages of biometrics-based traits for building better prediction systems for mental disorders.

Potential of water balance and remote sensing-based evapotranspiration models to predict yields of spring barley and winter wheat in the Czech Republic

Indicators based on evapotranspiration (ET) provide useful information about surface water status, response of vegetation to drought stress, and potential growth limitations. The capability of ET-based indicators, including actual ET and the evaporative stress index (ESI), to predict crop yields of spring barley and winter wheat was analyzed for 33 districts of the Czech Republic. In this study, the ET-based indicators were computed using two different approaches: (i) a prognostic model, SoilClim, which computes the water balance based on ground weather observations and information about soil and land cover; (ii) the diagnostic Atmosphere–Land Exchange Inverse (ALEXI) model based primarily on remotely sensed land surface temperature data. The capability of both sets of indicators to predict yields of spring barley and winter wheat was tested using artificial neural networks (ANNs) applied to the adjusting number and timeframe of inputs during the growing season. Yield predictions based on ANNs were computed for both crops for all districts together, as well as for individual districts. The root mean square error (RMSE) and coefficient of determination (R2) between observed and predicted yields varied with date within the growing season and with the number of ANN inputs used for yield prediction. The period with the highest predictive capability started from early-June to mid-June. This optimal period for yield prediction was identifiable already at the lower number of ANN inputs, nevertheless, the accuracy of the prediction improved as more inputs were included within ANNs.The RMSE values for individual districts varied between 0.4 and 0.7 t ha–1 while R2 reached values of 0.5–0.8 during the optimal period. Results of the study demonstrated that ET-based indicators can be used for yield prediction in real time during the growing season and therefore have great potential for decision making at regional and district levels.

Heart Disease Prediction Using Machine Learning

CVD is the one of the main reason of death compared to other diseases globally. Nearly 17.9 million people die every year because of this disease. WORLD HEALTH ORGANIZATION predicts that the deaths will increases by 24.5 million in 2030. Heart diseases involve diseases of the guts and blood vessels/artery. Four out of five deaths are occurs due to heart attacks. Nearly One-third of those deaths occur under the age of 70. The most of the people died in developing countries only. India is also comes under this category. For heart disease identification by examining the symptoms we need vascular specialists, which are very finite number in developing nations. Also, the medical tests/procedures for heart diseases are that’s a bit steep; At times out of the range for patient’s family. Early recognition is important in these type of diseases with low cost forecasting. In these days ML algorithms are used for forecasting of many other applications. These algorithms can be used for forecasting of CVD. The timely prediction of the heart diseases helped in taking decisions about the patients which resulted reduction of their risks. This project proposes a prediction model to predict whether patient have a heart disease or not by using entered symptoms in webform and to give an awareness on heart disease and some useful tips on heart disease.

Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches

Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take the appropriate preventive and treatment strategies. To improve the understanding of risk factors, we predict type 2 diabetes for Pima Indian women utilizing a logistic regression model and decision tree—a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and validate our analysis. The six-fold classification tree indicates glucose, BMI, and age are important factors, while the ten-node tree implies glucose, BMI, pregnancy, diabetes pedigree function, and age as the significant predictors. Our preferred specification yields a prediction accuracy of 78.26% and a cross-validation error rate of 21.74%. We argue that our model can be applied to make a reasonable prediction of type 2 diabetes, and could potentially be used to complement existing preventive measures to curb the incidence of diabetes and reduce associated costs.

Can Relative Blood Loss and Operative Time Predict Prolonged Length of Stay following Total Joint Arthroplasty? A Retrospective Study from Single Center in Saudi Arabia

Background: Total joint arthroplasty still causes a lot of blood loss, and many patients require blood transfusions thereafter. Blood transfusions can increase the risk of surgical site infection and periprosthetic joint infection, which can lengthen hospital stays and have a negative impact on patient outcomes. Objective: This study aimed to examine if relative blood loss and operative time could predict the postoperative increased length of stay following total joint arthroplasty. Patients and methods: A total of 187 procedures done during 2018–2021 were collected. Any revision case or any patient with hematological disorders was excluded. This led to a cohort of 181 procedures. The following data were collected: age, height, weight, body mass index (BMI), sex, procedure, surgery start time, surgery end time, preoperative hemoglobin and hematocrit levels, postoperative hemoglobin and hematocrit levels, number of packed red blood cells (PRBCs) transfused and the volume of each unit, and the length of stay (LOS). Results: In our study, age (p = 0.027, odds ratio = 1.076) and relative blood loss (p = 0.038, odds ratio = 1.036) were predictors of prolonged LOS. Similarly, there was a significant association between age, operative time (p < 0.001) and LOS. Conclusion: The operative time, relative blood loss, and age were found to significantly affect the LOS. However, no significant association was found between LOS and gender or BMI.

Searching for female reproductive aging and longevity biomarkers.

Female reproductive aging is, in a way, a biological phenomenon that develops along canonical molecular pathways; however, it has particular features. Recent studies revealed complexity of the interconnections between reproductive aging and aging of other systems, and even suggested a cause-effect uncertainty between them. It was also shown that reproductive aging can impact aging processes in an organism at the level of cells, tissues, organs, and systems. Women at the end of their reproductive lives are characterized by the accelerated incidence of age-related diseases. Timing of the onset of menarche and menopause and variability in the duration of reproductive life carry a latent social risk: not having enough information about the reproductive potential, women keep on postponing childbirth. Identification and use of the most accurate and sensitive aging biomarkers enable the prediction of menopause timing and quantification of the true biological and reproductive ages of an organism. We discuss current views on reproductive aging and peculiarities of using available biomarkers of aging. We also consider latest advances in the search for potential genetic markers of reproductive aging. Finally, we posit the importance of determining the female biological age and highlight potential research directions in this area.

Deformation and thinning field prediction for HFQ® formed panel components using convolutional neural networks

The novel Hot Forming and cold die Quenching (HFQ®) process can provide cost-effective and complex deep drawn solutions through high strength aluminium alloys. However, the unfamiliarity of the new process prevents its widescale adoption in industrial settings, while accurate Finite Element (FE) simulations using the most advanced material models take place late in design processes and require forming process expertise. Machine learning technologies have recently been proven successful in learning complex system behaviour from representative examples and have the potential to be used as design support tools for new forming technologies such as HFQ®. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate to predict the deformation and thinning fields for variable deep drawn geometries formed using HFQ® technology. A dataset based on deep drawn geometries and corresponding FE results is generated and used to train the model. The results show that near indistinguishable full field predictions in real time are obtained from the surrogate when compared with HFQ® simulations. This technique can be adopted in industrial settings to aid in both concept and detailed component design for complex-shaped panel components formed under HFQ® conditions, without underlying knowledge of the forming process.

A Transported Livengood–Wu Integral Model for Knock Prediction in Computational Fluid Dynamics Simulation

Abstract This work describes the development of a transported Livengood–Wu (L–W) integral model for computational fluid dynamics (CFD) simulation to predict autoignition and engine knock tendency. The currently employed L–W integral model considers both single-stage and two-stage ignition processes, thus can be generally applied to different fuels such as paraffin, olefin, aromatics, and alcohol. The model implementation is first validated in simulations of homogeneous charge compression ignition (HCCI) combustion for three different fuels, showing good accuracy in prediction of autoignition timing for fuels with either single-stage or two-stage ignition characteristics. Then, the L–W integral model is coupled with G-equation model to indicate end-gas autoignition and knock tendency in CFD simulations of a direct-injection spark-ignition engine. This modeling approach is about 10 times more efficient than the ones that based on detailed chemistry calculation and pressure oscillation analysis. Two fuels with same Research Octane Number (RON) but different octane sensitivity are studied, namely, Co-Optima alkylate and Co-Optima E30. Feed-forward neural network model in conjunction with multivariable minimization technique is used to generate fuel surrogates with targets of matched RON, octane sensitivity, and ethanol content. The CFD model is validated against experimental data in terms of pressure traces and heat release rate for both fuels under a wide range of operating conditions. The knock tendency—indicated by the fuel energy contained in the autoignited region—of the two fuels at different load conditions correlates well with the experimental results and the fuel octane sensitivity, implying the current knock modeling approach can capture the octane sensitivity effect and can be applied to further investigation on composition of octane sensitivity.

Regular and random judgements are not two sides of the same coin: Both representativeness and encoding play a role in randomness perception

The overalternating bias is that people rate sequences with an excess of alternation as more random than prescribed by information theory. There are two main explanations: the representativeness heuristic (Kahneman & Tversky Cognitive Psychology, 3, 430–454, 1972) and the implicit encoding hypothesis (Falk & Konold Psychological Review, 104, 301–318, 1997). These hypotheses are associated with different reaction times predictions. According to the encoding hypothesis, reaction times should increase as the complexity of the sequence increases, whereas the representativeness heuristic predicts fast reaction times only for more complex sequences that appear more random. We asked participants to guess the generating source of pairs of sequences of dichotomous elements in two different conditions: selecting the string generated by a random source or selecting the string generated by a nonrandom source. Results suggest that both the encoding strategy and the representativeness heuristic have a role in randomness perception and that the two criteria may have a different weight when determining the randomness versus the regularity of a string.

Time-Sensitive Networking in automotive embedded systems: State of the art and research opportunities

The functionality advancements and novel customer features that are currently found in modern automotive systems require high-bandwidth and low-latency in-vehicle communications, which become even more compelling for autonomous vehicles. In a recent effort to meet these requirements, the IEEE Time-Sensitive Networking (TSN) task group has developed a set of standards that introduce novel features in Switched Ethernet. TSN standards offer, for example, a common notion of time through accurate and reliable clock synchronization, delay bounds for real-time traffic, time-driven transmissions, improved reliability, and much more. In order to fully utilize the potential of these novel protocols in the automotive domain, TSN should be seamlessly integrated into the state-of-the-art and state-of-practice model-based development processes for automotive embedded systems. Some of the core phases in these processes include software architecture modeling, timing predictability verification, simulation, and hardware realization and deployment. Moreover, throughout the development of automotive embedded systems, the safety and security requirements specified on these systems need to be duly taken into account. In this context, this work provides an overview of TSN in automotive applications and discusses the recent technological developments relevant to the adoption of TSN in automotive embedded systems. The work also points at the open challenges and future research directions.

Prediction of Spark Timing to Achieve a Specified Torque Profile in Spark-Ignition Engines Using Time-Dependent Metamodeling

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The internal combustion engine is a source of unwanted vibration on the vehicle body. The unwanted vibration comes from forces on the engine mounts which depend on the engine torque during a transient maneuver. In particular, during a tip-in or a tip-out maneuver, different torque profiles result in different magnitudes of vibration. A desired engine torque shape can be thus obtained to minimize the unwanted vibration. The desired torque shape can be achieved by controlling a set of engine calibration parameters. This paper provides a methodology to determine the spark timing profile to achieve a desired engine torque profile during a tip-out maneuver. The spark timing profiles are described by a third-order polynomial as a function of time. A set of coefficients to define a third-order polynomial (design sites) are first generated using design of experiments (DOE). Each design site defines a spark timing profile which is then used to generate an engine torque profile using a CAE tool. The set of design sites and the corresponding engine torque profiles are used to construct time-dependent metamodels using Singular Value Decomposition (SVD) and Kriging. An example shows that the appropriate spark timing profile to achieve a desired engine torque profile can be successfully determined. The results are validated using the GT-Power engine software.&lt;/div&gt;&lt;/div&gt;