A Deep-Learning Framework to Predict the Dynamics of a Human-Driven Vehicle Based on the Road Geometry

Many trajectory forecasting methods, implementing deterministic and stochastic models, have been presented in the last decade for automotive applications. In this work, a deep-learning framework is proposed to model and predict the evolution of the coupled driver-vehicle system dynamics. Particularly, we aim to describe how the road geometry affects the actions performed by the driver. Differently from other works, the problem is formulated in such a way that the user may specify the features of interest. Nonetheless, we propose a set of features that is commonly used for automotive control applications to practically show the functioning of the algorithm. To solve the prediction problem, a deep recurrent neural network based on Long Short-Term Memory autoencoders is designed. It fuses the information on the road geometry and the past driver-vehicle system dynamics to produce context-aware predictions. Also, the complexity of the neural network is constrained to favour its use in online control tasks. The efficacy of the proposed approach was verified in a case study centered on motion cueing algorithms, using a dataset collected during test sessions of a non-professional driver on a dynamic driving simulator. A 3D track with complex geometry was employed as driving environment to render the prediction task challenging. Finally, the robustness of the neural network to changes in the driver and track was investigated to set guidelines for future works.Comment: 10 pages, 9 figures, 3 tables. This work has been submitted to the IEEE Transactions on Intelligent Transportation Systems for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Ecological indicators and biological resources for hydrocarbon rhizoremediation in a protected area

Spillage from oil refineries, pipelines, and service stations consistently leads to soil, food and groundwater contamination. Bacterial-assisted phytoremediation is a non-invasive and sustainable solution to eliminate or decrease the concentration of xenobiotic contaminants in the environment. In the present study, a protected area interested by a fuel discharge was considered to assess a bioremediation intervention. From the spill point, a plume of contamination flowed South-West into the aquifer, eventually reaching a wetland area. Soils, groundwaters and plants belonging to the species Scirpus sylvaticus (L.) were sampled. In the majority of the soil samples, concentrations of total petroleum hydrocarbons, both C ≤ 12 and C > 12, exceeded legal limits set forth in Directive 2000/60/EC. The analysis of diatom populations, used as ecological indicators, evidenced morphology alterations and the presence of Ulnaria ulna and Ulnaria biceps species, previously detected in hydrocarbon-polluted waters. Tests for phytotoxicity and phytodegradation, carried out in soil mesocosms, planted with Zea mays and Helianthus annuus, demonstrated that both species significantly contributed to the removal of total petroleum hydrocarbons. Removal of C ≤ 12 and C > 12 petroleum hydrocarbons was in the range of 80%–82% for Z. mays and 71%–72% for H. annuus. Microbial communities inhabiting high organic carbon and vegetated soils were more active in hydrocarbon degradation than those inhabiting subsoils, as evidenced by soil slurry experiments. The abundance of functional genes encoding toluene-benzene monooxygenase (tbmD) and alkane hydroxylase (alkB), quantified in environmental samples, confirmed that the plant rhizosphere recruited a microbial community with higher biodegradation capacity. Bacterial strains isolated from the sampling site were able to grow on model hydrocarbons (hexane, hexadecane and o-, m-, p-xylene) as sole carbon and energy sources, indicating that a natural bio-attenuation process was on-going at the site. The bacterial strains isolated from rhizosphere soil, rhizoplane and endosphere showed plant growth promoting traits according to in vitro and in vivo tests on Z. mays and Oryza sativa, allowing to forecast a possible application of bacterial assisted rhizoremediation to recover the protected area

SHREC'20: Shape correspondence with non-isometric deformations

Estimating correspondence between two shapes continues to be a challenging problem in geometry processing. Most current methods assume deformation to be near-isometric, however this is often not the case. For this paper, a collection of shapes of different animals has been curated, where parts of the animals (e.g., mouths, tails & ears) correspond yet are naturally non-isometric. Ground-truth correspondences were established by asking three specialists to independently label corresponding points on each of the models with respect to a previously labelled reference model. We employ an algorithmic strategy to select a single point for each correspondence that is representative of the proposed labels. A novel technique that characterises the sparsity and distribution of correspondences is employed to measure the performance of ten shape correspondence methods

トクシュウ トクシマケン ノ キュウキュウ イリョウ ト チイキ イリョウ : ゲンジョウ ト テンボウ : カントウゲン

We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds. GFrames are based on the computation of the intrinsic gradient of a scalar field defined on top of the input shape. The resulting tangent vector field defines a repeatable tangent direction of the local frame at each point; importantly, it directly inherits the properties and invariance classes of the underlying scalar function, making it remarkably robust under strong sampling artifacts, vertex noise, as well as non-rigid deformations. Existing local descriptors can directly benefit from our repeatable frames, as we showcase in a selection of 3D vision and shape analysis applications where we demonstrate state-of-the-art performance in a variety of challenging settings

A Study of the Lateral Stability of Self-Propelled Fruit Harvesters

Self-propelled fruit harvesters (SPFHs) are agricultural machines designed to facilitate fruit picking and other tasks requiring operators to stay close to the foliage or to the upper part of the canopy. They generally consist of a chassis with a variable height working platform that can be equipped with lateral extending platforms. The positioning of additional masses (operators, fruit bins) and the maximum height of the platform (up to three meters above the ground) strongly affect machine stability. Since there are no specific studies on the lateral stability of SPFHs, this study aimed to develop a specific test procedure to fill this gap. A survey of the Italian market found 20 firms manufacturing 110 different models of vehicles. Observation and monitoring of SPFHs under real operational conditions revealed the variables mostly likely to affect lateral stability: the position and mass of the operators and the fruit bin on the platform. Two SPFHs were tested in the laboratory to determine their centre of gravity and lateral stability in four different settings reproducing operational conditions. The test setting was found to affect the stability angle. Lastly, the study identified two specific settings reproducing real operational conditions most likely to affect the lateral stability of SPFHs: these should be used as standard, reproducible settings to enable a comparison of results

Co-Graft of Allogeneic Immune Regulatory Neural Stem Cells (NPC) and Pancreatic Islets Mediates Tolerance, while Inducing NPC-Derived Tumors in Mice

Data available on the immunomodulatory properties of neural stem/precursor cells (NPC) support their possible use as modulators for immune-mediated process. The aim of this study was to define whether NPC administered in combination with pancreatic islets prevents rejection in a fully mismatched allograft model.Diabetic Balb/c mice were co-transplanted under the kidney capsule with pancreatic islets and GFP(+) NPC from fully mismatched C57BL/6 mice. The following 4 groups of recipients were used: mice receiving islets alone; mice receiving islets alone and treated with standard immunosuppression (IL-2Ralpha chain mAbs + FK506 + Rapamycin); mice receiving a mixed islet/NPC graft under the same kidney capsule (Co-NPC-Tx); mice receiving the islet graft under the left kidney capsule and the NPC graft under the right kidney capsule (NPC-Tx). Our results demonstrate that only the co-transplantation and co-localization of NPC and islets (Co-NPC-Tx) induce stable long-term graft function in the absence of immunosuppression. This condition is associated with an expansion of CD4(+)CD25(+)FoxP3(+) T regulatory cells in the spleen. Unfortunately, stable graft function was accompanied by constant and reproducible development of NPC-derived cancer mainly sustained by insulin secretion.These data demonstrate that the use of NPC in combination with islets prevents graft rejection in a fully mismatched model. However, the development of NPC-derived cancer raises serious doubts about the safety of using adult stem cells in combination with insulin-producing cells outside the original microenvironment

Allo Beta Cell transplantation: specific features, unanswered questions, and immunological challenge

Type 1 diabetes (T1D) presents a persistent medical challenge, demanding innovative strategies for sustained glycemic control and enhanced patient well-being. Beta cells are specialized cells in the pancreas that produce insulin, a hormone that regulates blood sugar levels. When beta cells are damaged or destroyed, insulin production decreases, which leads to T1D. Allo Beta Cell Transplantation has emerged as a promising therapeutic avenue, with the goal of reinstating glucose regulation and insulin production in T1D patients. However, the path to success in this approach is fraught with complex immunological hurdles that demand rigorous exploration and resolution for enduring therapeutic efficacy. This exploration focuses on the distinct immunological characteristics inherent to Allo Beta Cell Transplantation. An understanding of these unique challenges is pivotal for the development of effective therapeutic interventions. The critical role of glucose regulation and insulin in immune activation is emphasized, with an emphasis on the intricate interplay between beta cells and immune cells. The transplantation site, particularly the liver, is examined in depth, highlighting its relevance in the context of complex immunological issues. Scrutiny extends to recipient and donor matching, including the utilization of multiple islet donors, while also considering the potential risk of autoimmune recurrence. Moreover, unanswered questions and persistent gaps in knowledge within the field are identified. These include the absence of robust evidence supporting immunosuppression treatments, the need for reliable methods to assess rejection and treatment protocols, the lack of validated biomarkers for monitoring beta cell loss, and the imperative need for improved beta cell imaging techniques. In addition, attention is drawn to emerging directions and transformative strategies in the field. This encompasses alternative immunosuppressive regimens and calcineurin-free immunoprotocols, as well as a reevaluation of induction therapy and recipient preconditioning methods. Innovative approaches targeting autoimmune recurrence, such as CAR Tregs and TCR Tregs, are explored, along with the potential of stem stealth cells, tissue engineering, and encapsulation to overcome the risk of graft rejection. In summary, this review provides a comprehensive overview of the inherent immunological obstacles associated with Allo Beta Cell Transplantation. It offers valuable insights into emerging strategies and directions that hold great promise for advancing the field and ultimately improving outcomes for individuals living with diabetes

Experimental characterization and modelling of a side buffer for freight trains

A detailed investigation performed by European Rail Research Institute (ERRI) relevant to the safe running of heavy freight trains revealed how levels of buffer forces exceeding 240 kN can deeply reduce the safety margins of a train-set. Even if the analysis pointed out the role played by buffers in affecting running safety, little is known about their response; according to the UIC 526-1, buffers mounted on freight trains should satisfy general requirements in terms of stored and dissipated energy under quasi-static load cycles and during impacts between adjacent vehicles at given relative speeds. This work aims at characterizing the buffer response under working conditions close to the ones experienced during critical manoeuvres like braking while cornering or when passing over switches. A special test bench was thus set up to impose static and dynamics loads on a buffer through hydraulic actuators, considering also misaligned axial forces and the application of tangential forces reproducing the effect of friction between adjacent buffers; data collected through experimentation were then used to develop a complex rheological model of the component. The model of buffer was eventually introduced into a simulation code to investigate the effect of its response on the train-set dynamics