An intelligent curve warning system for powered two wheel vehicles

This article illustrates a novel Curve Warning system for motorcycles which has been developed in the SAFERIDER project (www.saferider-eu.org) of the 7th EU FP, among other Advanced Rider Assistance Systems. The Curve Warning function (CW) described here follows a holistic approach, which combines road geometry, motorcycle dynamics, rider input and riding styles. The warning strategy is based on the correction of longitudinal dynamics derived from a previewed ideal manoeuvre (reference manoeuvre) continuously computed from the actual state of the vehicle. Under normal driving conditions the reference manoeuvre matches the rider's and no correction is needed and no warning is given. But if large differences between actual and ideal accelerations are found the rider is warned to decelerate or brake. As soon as the correct value of deceleration is achieved the warning disappears, improving system acceptability. Warnings are given to the rider via an HMI, which uses a haptic accelerator throttle, a vibrating glove and helmet, and a visual display

Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

This position paper introduces the concept of artificial “co-drivers” as an enabling technology for future intelligent transportation systems. In Sections I and II, the design principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV, we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research

Appendectomy during the COVID-19 pandemic in Italy: a multicenter ambispective cohort study by the Italian Society of Endoscopic Surgery and new technologies (the CRAC study)

Major surgical societies advised using non-operative management of appendicitis and suggested against laparoscopy during the COVID-19 pandemic. The hypothesis is that a significant reduction in the number of emergent appendectomies was observed during the pandemic, restricted to complex cases. The study aimed to analyse emergent surgical appendectomies during pandemic on a national basis and compare it to the same period of the previous year. This is a multicentre, retrospective, observational study investigating the outcomes of patients undergoing emergent appendectomy in March-April 2019 vs March-April 2020. The primary outcome was the number of appendectomies performed, classified according to the American Association for the Surgery of Trauma (AAST) score. Secondary outcomes were the type of surgical technique employed (laparoscopic vs open) and the complication rates. One thousand five hundred forty one patients with acute appendicitis underwent surgery during the two study periods. 1337 (86.8%) patients met the inclusion criteria: 546 (40.8%) patients underwent surgery for acute appendicitis in 2020 and 791 (59.2%) in 2019. According to AAST, patients with complicated appendicitis operated in 2019 were 30.3% vs 39.9% in 2020 (p = 0.001). We observed an increase in the number of post-operative complications in 2020 (15.9%) compared to 2019 (9.6%) (p < 0.001). The following determinants increased the likelihood of complication occurrence: undergoing surgery during 2020 (+ 67%), the increase of a unit in the AAST score (+ 26%), surgery performed > 24 h after admission (+ 58%), open surgery (+ 112%) and conversion to open surgery (+ 166%). In Italian hospitals, in March and April 2020, the number of appendectomies has drastically dropped. During the first pandemic wave, patients undergoing surgery were more frequently affected by more severe appendicitis than the previous year's timeframe and experienced a higher number of complications. Trial registration number and date: Research Registry ID 5789, May 7th, 202

Colorectal Cancer Stage at Diagnosis Before vs During the COVID-19 Pandemic in Italy

IMPORTANCE Delays in screening programs and the reluctance of patients to seek medical attention because of the outbreak of SARS-CoV-2 could be associated with the risk of more advanced colorectal cancers at diagnosis. OBJECTIVE To evaluate whether the SARS-CoV-2 pandemic was associated with more advanced oncologic stage and change in clinical presentation for patients with colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study included all 17 938 adult patients who underwent surgery for colorectal cancer from March 1, 2020, to December 31, 2021 (pandemic period), and from January 1, 2018, to February 29, 2020 (prepandemic period), in 81 participating centers in Italy, including tertiary centers and community hospitals. Follow-up was 30 days from surgery. EXPOSURES Any type of surgical procedure for colorectal cancer, including explorative surgery, palliative procedures, and atypical or segmental resections. MAIN OUTCOMES AND MEASURES The primary outcome was advanced stage of colorectal cancer at diagnosis. Secondary outcomes were distant metastasis, T4 stage, aggressive biology (defined as cancer with at least 1 of the following characteristics: signet ring cells, mucinous tumor, budding, lymphovascular invasion, perineural invasion, and lymphangitis), stenotic lesion, emergency surgery, and palliative surgery. The independent association between the pandemic period and the outcomes was assessed using multivariate random-effects logistic regression, with hospital as the cluster variable. RESULTS A total of 17 938 patients (10 007 men [55.8%]; mean [SD] age, 70.6 [12.2] years) underwent surgery for colorectal cancer: 7796 (43.5%) during the pandemic period and 10 142 (56.5%) during the prepandemic period. Logistic regression indicated that the pandemic period was significantly associated with an increased rate of advanced-stage colorectal cancer (odds ratio [OR], 1.07; 95%CI, 1.01-1.13; P = .03), aggressive biology (OR, 1.32; 95%CI, 1.15-1.53; P < .001), and stenotic lesions (OR, 1.15; 95%CI, 1.01-1.31; P = .03). CONCLUSIONS AND RELEVANCE This cohort study suggests a significant association between the SARS-CoV-2 pandemic and the risk of a more advanced oncologic stage at diagnosis among patients undergoing surgery for colorectal cancer and might indicate a potential reduction of survival for these patients

Symbolic-Numeric Efficient Solution of Optimal Control Problems for Multibody Systems

This paper presents an efficient symbolic-numerical approach for generating and solving the Boundary Value Problem - Differential Algebraic Equation (BVP-DAE) originating from the variational form of the Optimal Control Problem (OCP). This paper presents the Method for the symbolic derivation, by means of symbolic manipulation software (Maple), of the equations of the OCP applied to a generic multibody system. The constrained problem is transformed into a non-constrained problem, by means of the Lagrange multipliers and penalty functions. From the first variation of the nonconstrained problem a BVP-DAE is obtained, and the finite difference discretization yields a non-linear systems. For the numerical solution of the non-linear system a damped Newton scheme is used. The sparse and structured jacobians is quickly inverted by exploiting the sparsity pattern in the solution strategy. The proposed method is implemented in an object oriented fashion, and coded in C++ language. Efficiency is ensured in core routines by using Lapack and Blas for linear algebra

Vehicle and driver modeling and threat assessment for driving support functions

The article reports a novel method to assess the driving risk level and design a human friendly warning strategy. The method is built on a Receding Horizon (RH) approach that is instanced for a set of predefined driving scenarios such as driving in the lane, change lane, etc. In control field, the RH is a technique that solves a sequence of optimization problemin real-time and, at each time step, applies only the first value of the control plan to steer the system towards a desired behavior. In this work, differentlythan in the control application, the initial value of the each control plan is used as a measure of the correction that the rider should apply to conform to the computed optimal maneuver. This choice has the advantage to provide an homogenous measure of the threat independently from the scenario and it is directly linked with the control variable that the rider should use to accordingly changethevehicledynamics. Additionally,theRH approachnaturallyaccommodatesroadgeometry and attribute constraints, vehicle dynamics, driving input and styles. A proper development of the vehicle model and a quantitative characterization of the human driving skills play an important role in the method effectiveness. Additionally the method make use of a dedicated solver to compute the probleminrealtime. Themethodwas appliedwithsuccess todevelopdrivingsupportfunctionsboth for cars in the the FP6th European project PReVENT and the FP7th interactIVe and for motorcycles in the FP7th European Project SAFERIDER. The article introduces the RH approach as defined for the driving threat assessment. Then it discusses in details the vehicle modelling requirements and how human driving skills are included in the proposed method. Examplary use of how the system works in different driving scenario will be given. Finally, the experimental results of pilot tests are shown for all the developed applications

A hybrid ripple model and two hybrid observers for its estimation

International audienceIn this paper we propose a novel model of the ripple produced in different contexts involving switching power electronics. The novelty of this model is that the nonsmooth waveform characterizing the ripple is captured by a suitable hybrid dynamics performing state jumps at the switching instants. In addition to showing that this model is effective at representing the ripple waveform, we propose two hybrid schemes ensuring asymptotic observation of the ripple waveform, one of them using knowledge of the switching instants and a second one without knowledge of the switching instants. Simulation results illustrate the effectiveness of the proposed hybrid observation laws

A Spatial Machanism for the Measurement of the Inertia Tensor: Theory and Experimental Results

This paper deals with the problem of measuring the inertia tensor of rigid bodies. An original approach is adopted, different from classical modal analysis techniques. The rigid body is forced by a spatial mechanism to rotate around different axes. Once the mechanism is calibrated, i.e., its inertia and stiffness matrices are known, the inertia tensor of the rigid body may be determined by measuring the frequencies of the small oscillations around the selected axes and then solving a least-squares identification problem. Two prototypes of the spatial mechanism were built. The first was used to perform tests and to measure the inertia tensor of some compressors for domestic refrigeration. The second was constructed to measure the inertia tensor of large mechanical systems