Study of functional and physiological response of co-occurring shrub species to the Mediterranean climate

The Mediterranean basin is characterised by increasingly dry summers and the study of the adaptive traits developed by plants living in this stressful environment is of great interest, also in relation to climate projections for this area. Cistus monspeliensis, Myrtus communis and Phillyrea angustifolia are three co-occurring shrubs typical of the Mediterranean maquis. Their functional and physiological parameters were studied in spring, summer and autumn in order to highlight adjustments of these traits and to test eventual different adaptive strategies. Soil and leaf chemical characteristics were determined in the different seasons. Leaf area, specific leaf area, leaf dry matter content, succulence index, pigment contents hydric status and main markers of oxidative stress and antioxidant response were detected. The stressful summer season induced disturbance in hydric balance, decrease in succulence index and chlorophyll content and high contents of hydrogen peroxide. Thanks to higher enzymatic activities and total glutathione content, in the two evergreen species M. communis and P. angustifolia oxidative damage remained at levels equal to or lower than the other seasons. Only in the semideciduous C. monspeliensis both functional and biochemical traits showed a higher stress condition in summer. The higher stability of functional traits in the two evergreen species may be explained by the sclerophyllous nature of their leaves. Four environmental variables – Tmax, Tmin, soil conductivity and organic matter – mostly influenced NMDS segregation of these species

Exploration of sex differences in Rhes effects in dopamine mediated behaviors

Studies have shown that Ras homolog enriched in striatum (Rhes) proteins are highly expressed in areas of the central nervous system that have high dopaminergic innervation. In this study, we used Rhes mutant mice (Wild type, Rhes KO, Rhes Heterozygous) of both sexes to explore differences in the effects of Rhes protein levels in basal levels of activity, anxiety, and stereotypy, in relation to sex. Adult male and female mice were evaluated in an open field test for measuring basal levels of activity and anxiety for 5 consecutive days, and they were tested in the apomorphine-induced stereotypy paradigm. Rhes protein levels affected basal levels of activity but it was not found to be related to sex differences. Moreover, a decrease in Rhes protein levels was linked to a nonsignificant anxiolytic effect, mainly in female mice. Finally, a decrease in Rhes protein levels does not affect dopamine D1 and D2 receptor (D1/D2) synergism in female or male mice. Together, these results suggest that Rhes protein levels affect locomotion activity, and have an influence in anxiety depending on sex; Rhes protein levels do not affect D1/D2 synergism in both sexes

Involvement of the sigma factor sigma H in the regulation of a small heat shock protein gene in Lactobacillus plantarum WCFS1

The relative expression of the heat shock protein hsp18.55 gene was monitored in Lactobacillus plantarum ΔctsR and ΔftsH mutant strains. Transcription of the hsp18.55 gene was drastically repressed in the ΔctsR mutant of L. plantarum; conversely, significant transcriptional induction of the hsp18.55 gene was noted in the L. plantarum ΔftsH strain. Overall, these results suggest a possible regulation of the hsp18.55 gene by the alternative sigma H factor in L. plantarum. We also noted a similarity with the small heat shock gene (shs) locus of Lactobacillus brevis, which might indicate a possible evolutionary relationship

Development of an Adaptive Model Predictive Control for Platooning Safety in Battery Electric Vehicles

The recent and continuous improvement in the transportation field provides several different opportunities for enhancing safety and comfort in passenger vehicles. In this context, Adaptive Cruise Control (ACC) might provide additional benefits, including smoothness of the traffic flow and collision avoidance. In addition, Vehicle-to-Vehicle (V2V) communication may be exploited in the car-following model to obtain further improvements in safety and comfort by guaranteeing fast response to critical events. In this paper, firstly an Adaptive Model Predictive Control was developed for managing the Cooperative ACC scenario of two vehicles; as a second step, the safety analysis during a cut-in maneuver was performed, extending the platooning vehicles’ number to four. The effectiveness of the proposed methodology was assessed for in different driving scenarios such as diverse cruising speeds, steep accelerations, and aggressive decelerations. Moreover, the controller was validated by considering various speed profiles of the leader vehicle, including a real drive cycle obtained using a random drive cycle generator software. Results demonstrated that the proposed control strategy was capable of ensuring safety in virtually all test cases and quickly responding to unexpected cut-in maneuvers. Indeed, different scenarios have been tested, including acceleration and deceleration phases at high speeds where the control strategy successfully avoided any collision and stabilized the vehicle platoon approximately 20–30 s after the sudden cut-in. Concerning the comfort, it was demonstrated that improvements were possible in the aggressive drive cycle whereas different scenarios were found in the random cycle, depending on where the cut-in maneuver occurred

Exploitation of a Particle Swarm Optimization Algorithm for Designing a Lightweight Parallel Hybrid Electric Vehicle

The dramatic global climate change has driven governments to drastically tackle pollutant emissions. In the transportation field, one of the technological responses has been powertrain electrification for passengers’ cars. Nevertheless, the large amount of possible powertrain designs does not help the development of an exhaustive sizing process. In this research, a multi-objective particle swarm optimization algorithm is proposed to find the optimal layout of a parallel P2 hybrid electric vehicle powertrain with the aim of maximizing fuel economy capability and minimizing production cost. A dynamic programming-based algorithm is used to ensure the optimal vehiclelevel energy management. The results show that diverse powertrain layouts may be suggested when different weights are assigned to the sizing targets related to fuel economy and production cost, respectively. Particularly, upsizing the power sources and increasing the number of gears might be advised to enhance HEV fuel economy capability through the efficient exploitation of the internal combustion engine (ICE) operation. On the other hand, reduction of the HEV production cost could be achieved by downsizing the power sources and limiting the number of gears with respect to conventional ICE-powered vehicles thanks to the interaction between ICE and electric motor

Improving Computational Efficiency for Energy Management Systems in Plug-in Hybrid Electric Vehicles Using Dynamic Programming Based Controllers

Reducing computational time has become a critical issue in recent years, particularly in the transportation field, where the complexity of scenarios demands lightweight controllers to run large simulations and gather results to study different behaviors. This study proposes two novel formulations of the Optimal Control Problem (OCP) for the Energy Management System of a Plug-in Hybrid Electric Vehicle (PHEV) and compares their performance with a benchmark found in the literature. Dynamic Programming was chosen as the optimization algorithm to solve the OCP in a Matlab environment, using the DynaProg toolbox. The objective is to address the optimality of the fuel economy solution and computational time. In order to improve the computational efficiency of the algorithm, an existing formulation from the literature was modified, which originally utilized three control inputs. The approach involves leveraging the unique equations that describe the Input-Split Hybrid powertrain, resulting in a reduction of control inputs firstly to two and finally to one in the proposed solutions. The aforementioned formulations are referred to as 2-Controls and a 1-Control. Virtual tests were conducted to evaluate the performance of the two formulations. The simulations were carried out in various scenarios, including urban and highway driving, to ensure the versatility of the controllers. The results demonstrate that both proposed formulations achieve a reduction in computational time compared to the benchmark. The 2-Controls formulation achieved a reduction in computational time of approximately 40 times, while the 1-Control formulation achieved a remarkable reduction of approximately 850 times. These reductions in computational time were achieved while obtaining a maximum difference in fuel economy of approximately 1.5% for the 1-Control formulation with respect to the benchmark solution. Overall, this study provides valuable insights into the development of efficient and optimal controllers for PHEVs, which can be applied to various transportation scenarios. The proposed formulations reduce computational time without sacrificing the optimality of the fuel economy solution, making them a promising approach for future research in this area

Battery Electric Vehicles Platooning: Assessing Capability of Energy Saving and Passenger Comfort Improvement

Techniques exploiting the communication between vehicles, infrastructure or anything capable of, are being developed in the recent years due to their effectiveness in improving energy efficiency, comfort and safety. The scenario analyzed in this paper is of four vehicles platooning, in which the leader (i.e. the first in the platoon) is set to travel through different drive cycles and is followed by three other vehicles. An optimization-based algorithm based on Dynamic Programming (DP) is implemented to find the benchmark optimal control solution for the speed trajectory of the three following Battery Electric Vehicles (BEVs). Optimal control targets for planning the three automated vehicle velocity profiles involve both reducing aggressive changes in velocity, thus enhancing passenger comfort, and decreasing energy consumption. Results show a potential range of 1.8 – 7.6 % energy reduction when comparing the energy consumptions of the lead and first follower vehicle, whereas the implemented optimization-based velocity planner predicts enhanced energy economy for the second and third follower BEVs. In general, the highest advantages both in energy consumption and comfort are predicted in the urban scenarios due to the high number of acceleration/deceleration phases

Optimal Real-Time Velocity Planner of a Battery Electric Vehicle in V2V Driving

Autonomous driving systems are among the most interesting technologies in the transportation field nowadays, ensuring a high level of safety and comfort while also enhancing energy saving. For the following case study, a Battery Electric Vehicle (BEV) is considered able to communicate with other vehicles through vehicle-to-vehicle (V2V) technology by exchanging information on position and velocity. In this framework, an innovative real-time velocity planner has been developed aiming at maximizing the battery energy savings while improving the passenger comfort as well. This controller uses the principles of the equivalent consumption minimization strategy (ECMS) when the preceding vehicle is accelerating. Simulation results demonstrate improvements in comfort ranging from 26% to 42% ca. and in energy consumption (from 0.4% to 1.3%) when performing different drive cycles in V2V automated driving mode thanks to the proposed controller

Plant adaptation to extreme environments: The example of Cistus salviifolius of an active geothermal alteration field

Cistus salviifolius is able to colonise one of the most extreme active geothermal alteration fields in terms of both soil acidity and hot temperatures. The analyses of morphofunctional and physiological characters, investigated in leaves of plants growing around fumaroles (G leaves) and in leaves developed by the same plants after transfer into growth chamber under controlled conditions (C leaves) evidenced the main adaptive traits developed by this pioneer plant in a stressful environment. These traits involved leaf shape and thickness, mesophyll compactness, stomatal and trichome densities, chloroplast size. Changes of functional and physiological traits concerned dry matter content, peroxide and lipid peroxidation, leaf area, relative water and pigment contents. A higher reducing power and antioxidant enzymatic activity were typical of G leaves. Though the high levels of stress parameters, G leaves showed stress-induced specific morphogenic and physiological responses putatively involved in their surviving in active geothermal habitats

Cooperative Adaptive Cruise Control: A Gated Recurrent Unit Approach

Embedded artificial intelligence solutions are promising controllers for future sustainable and automated road vehicles. This study presents a deep learning-based approach combined with vehicle communication technology for the design of a real-time cooperative adaptive cruise control (CACC). A particular type of recurrent neural network has been selected, namely a gated recurrent unit (GRU). GRU exhibits improved learning performance in control problems such as the CACC since it avoids the vanishing gradient problems that characterize long time series. A GRU has been trained using ad-hoc CACC datasets build-up according to an optimal control policy, i.e. dynamic programming (DP), for a battery electric vehicle. In particular, DP optimizes the longitudinal speed trajectory of the Ego (Following) vehicle in CACC so to achieve energy savings and passenger comfort improvement. Results demonstrate that the Ego vehicle controlled by the trained GRU can achieve an eco-friendly driving in CACC without compromising passenger comfort and safety requirements. Unlike DP, GRU holds strong real-time potential. The performance of the proposed GRU approach for CACC is verified by benchmarking with the optimal performance obtained off-line using DP in several driving missions