Environmental Impact Assessment, on the Operation of Conventional and More Electric Large Commercial Aircraft

Global aviation is growing exponentially and there is a great emphasis on trajectory optimization to reduce the overall environmental impact caused by aircraft. Many optimization techniques exist and are being studied for this purpose. The CLEAN SKY Joint Technology Initiative for aeronautics and Air transport, a European research activity run under the Seventh Framework program, is a collaborative initiative involving industry, research organizations and academia to introduce novel technologies to improve the environmental impact of aviation. As part of the overall research activities, "green" aircraft trajectories are addressed in the Systems for Green Operations (SGO) Integrated Technology Demonstrator. This paper studies the impact of large commercial aircraft trajectories optimized for different objectives applied to the on board systems. It establishes integrated systems models for both conventional and more electric secondary power systems and studies the impact of fuel, noise, time and emissions optimized trajectories on each configuration. It shows the significant change in the fuel burn due to systems operation and builds up the case as to why a detailed aircraft systems model is required within the optimization loop. Typically, the objective in trajectory optimization is to improve the mission performance of an aircraft or reduce the environmental impact. Hence parameters such as time, fuel burn, emissions and noise are key optimization objectives. In most instances, trajectory optimization is achieved by using models that represent such parameters. For example aircraft dynamics models to describe the flight performance, engine models to calculate the fuel burn, emissions and noise impact, etc. Such techniques have proved to achieve the necessary level of accuracy in trajectory optimization. This research enhances previous techniques by adding in the effect of systems power in the optimization process. A comparison is also made between conventional power systems and more electric architectures. In the conventional architecture, the environmental control system and the ice protection system are powered by engine bleed air while actuators and electrics are powered by engine shaft power off-takes. In the more electric architecture, bleed off take is eliminated and the environmental control system and ice protection system are also powered electrically through engine shaft power off takes

Proline affects the size of the root meristematic zone in Arabidopsis

We reported previously that root elongation in Arabidopsis is promoted by exogenous proline, raising the possibility that this amino acid may modulate root growth. To evaluate this hypothesis we used a combination of genetic, pharmacological and molecular analyses, and showed that proline specifically affects root growth by modulating the size of the root meristem. The effects of proline on meristem size are parallel to, and independent from, hormonal pathways, and do not involve the expression of genes controlling cell differentiation at the transition zone. On the contrary, proline appears to control cell division in early stages of postembryonic root development, as shown by the expression of the G2/M-specific CYCLINB1;1 (CYCB1;1) gene. The overall data suggest that proline can modulate the size of root meristematic zone in Arabidopsis likely controlling cell division and, in turn, the ratio between cell division and cell differentiation

Assessing GNSS integrity augmentation techniques in UAV sense-and-avoid architectures

The integration of Global Navigation Satellite System (GNSS) integrity augmentation functionalities in Unmanned Aerial Vehicles (UAV) Sense-and-Avoid (SAA) architectures has the potential to provide an integrity-augmented SAA solutiThe integration of Global Navigation Satellite System (GNSS) integrity augmentation functionalities in Unmanned Aerial Vehicles (UAV) Sense-and-Avoid (SAA) architectures has the potential to provide an integrity-augmented SAA solution suitable for cooperative and non-cooperative scenarios. In this paper, we evaluate the opportunities offered by this integration, proposing a novel approach that maximizes the synergies between Avionics Based Integrity Augmentation (ABIA) and UAV cooperative/non-cooperative SAA architectures. In the proposed architecture, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area in both cooperative and non-cooperative cases. When the specified thresholds are exceeded, an avoidance manoeuvre is performed by implementing a heading-based Differential Geometry (DG) or Pseudospectral (PS) optimization to generate a set of optimal trajectory solutions free of near mid-air conflicts. The selection of DG or PS is based on the time available to perform the avoidance maneuver as dictated by the relative dynamics of the host and intruder platforms. The trajectory is optimized by implementing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. The optimised avoidance trajectory also considers the constraints imposed by the ABIA in terms of UAV platform dynamics and GNSS constellation satellite elevation angles and thus prevents degradation or loss of navigation data during Track, Decide and Avoid (TDA) processes of a SAA loop. Therefore, real-time trajectory corrections are performed to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place. The performance of this Integrity- Augmented SAA (IAS) architecture was evaluated by simulation case studies involving cooperative and non-cooperative scenarios. The selected host platform was the AEROSONDE UAV and the simulation cases were performed in a representative crosssection of the UAV operational flight envelope. Simulation results demonstrate that the proposed IAS architecture is capable of performing high-integrity conflict detection and resolution when GNSS is considered as the primary source of navigation data

GNSS avionics-based integrity augmentation for RPAS detect-and-avoid applications

Taking the move from our recent research on GNSS Avionics Based Integrity Augmentation (ABIA), this article investigates the synergies of ABIA with a novel Detect-and-Avoid (DAA) architecture for Remotely Piloted Aircraft System (RPAS). Based on simulation and experimental data collected on a variety of manned and unmanned aircraft, it was observed that the integration of ABIA with DAA has the potential to provide an integrity-augmented DAA for both cooperative and non-cooperative applications. The candidate DAA system uses various Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) in addition to TCAS/ASAS for the cooperative case. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuvre is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of RPAS platform dynamics and GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole DAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Cooperative and non-cooperative simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented DAA (IAS) architecture. The selected host platform was the AEROSONDE RPAS and the simulation cases were performed in a representative cross-section of the RPAS operational flight envelope. The simulation results show that the proposed IAS architecture is capable of performing high-integrity conflict detection and resolution when GNSS is the primary source of navigation data

Avionics-based GNSS integrity augmentation for unmanned aerial systems sense-and-avoid

This paper investigates the synergies between a GNSS Avionics Based Integrity Augmentation (ABIA) system and a novel Unmanned Aerial System (UAS) Sense-and-Avoid (SAA) architecture for cooperative and non-cooperative scenarios. The integration of ABIA with SAA has the potential to provide an integrity-augmented SAA solution that will allow the safe and unrestricted access of UAS to commercial airspace. The candidate SAA system uses Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) for the cooperative case. In the non-cooperative scenario, the system employs navigation-based image stabilization with image morphology operations and a multi-branch Viterbi filter for obstacle detection, which allows heading estimation. The system utilizes a Track-to-Track (T3) algorithm for data fusion that allows combining data from different tracks obtained with FLS and/or ADS-B depending on the scenario. Successively, it utilizes an Interacting Multiple Model (IMM) algorithm to estimate the state vector allowing a prediction of the intruder trajectory over a specified time horizon. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuver is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole SAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Various simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented SAA (IAS) architecture. The selected host platform was the AEROSONDE Unmanned Aerial Vehicle (UAV) and the simulation cases addressed a variety of cooperative and non-cooperative scenarios in a representative cross-section of the AEROSONDE operational flight envelope. The simulation results show that the proposed IAS architecture is an excellent candidate to perform high-integrity Collision Detection and Resolution (CD&R) utilizing GNSS as the primary source of navigation data, providing solid foundation for future research and developments in this domain

Avionics-based GNSS integrity augmentation for unmanned aerial systems sense-and-avoid

This paper investigates the synergies between a GNSS Avionics Based Integrity Augmentation (ABIA) system and a novel Unmanned Aerial System (UAS) Sense-and-Avoid (SAA) architecture for cooperative and non-cooperative scenarios. The integration of ABIA with SAA has the potential to provide an integrity-augmented SAA solution that will allow the safe and unrestricted access of UAS to commercial airspace. The candidate SAA system uses Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) for the cooperative case. In the non-cooperative scenario, the system employs navigation-based image stabilization with image morphology operations and a multi-branch Viterbi filter for obstacle detection, which allows heading estimation. The system utilizes a Track-to-Track (T3) algorithm for data fusion that allows combining data from different tracks obtained with FLS and/or ADS-B depending on the scenario. Successively, it utilizes an Interacting Multiple Model (IMM) algorithm to estimate the state vector allowing a prediction of the intruder trajectory over a specified time horizon. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuver is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole SAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Various simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented SAA (IAS) architecture. The selected host platform was the AEROSONDE Unmanned Aerial Vehicle (UAV) and the simulation cases addressed a variety of cooperative and non-cooperative scenarios in a representative cross-section of the AEROSONDE operational flight envelope. The simulation results show that the proposed IAS architecture is an excellent candidate to perform high-integrity Collision Detection and Resolution (CD&R) utilizing GNSS as the primary source of navigation data, providing solid foundation for future research and developments in this domain

GNSS avionics-based integrity augmentation for RPAS detect-and-avoid applications

Taking the move from our recent research on GNSS Avionics Based Integrity Augmentation (ABIA), this article investigates the synergies of ABIA with a novel Detect-and-Avoid (DAA) architecture for Remotely Piloted Aircraft System (RPAS). Based on simulation and experimental data collected on a variety of manned and unmanned aircraft, it was observed that the integration of ABIA with DAA has the potential to provide an integrity-augmented DAA for both cooperative and non-cooperative applications. The candidate DAA system uses various Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) in addition to TCAS/ASAS for the cooperative case. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuvre is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of RPAS platform dynamics and GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole DAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Cooperative and non-cooperative simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented DAA (IAS) architecture. The selected host platform was the AEROSONDE RPAS and the simulation cases were performed in a representative cross-section of the RPAS operational flight envelope. The simulation results show that the proposed IAS architecture is capable of performing high-integrity conflict detection and resolution when GNSS is the primary source of navigation data

Avionics-Based GNSS Integrity Augmentation for UAS mission planning and real-time trajectory optimisation

This paper explores the potential of integrating Global Navigation Satellite System (GNSS) Avionics Based Integrity Augmentation (ABIA) functionalities in Unmanned Aerial Systems (UAS) to perform mission planning and real-time trajectory optimisation tasks. In case of mission planning, a pseudo-spectral optimization technique is adopted. For real-time trajectory optimisation a Direct Constrained Optimisation (DCO) method is employed. In this method the aircraft dynamics model is used to generate a number of feasible flight trajectories that also satisfy the GNSS integrity constraints. The feasible trajectories are calculated by initialising the aircraft dynamics model with a manoeuvre identification algorithm. The performance of the proposed GNSS integrity augmentation and trajectory optimisation algorithms was evaluated in representative simulation case studies. Additionally, the ABIA performance was compared with Space-Based and Ground-Based Augmentation Systems (SBAS/GBAS). Simulation results show that the proposed integration scheme is capable of performing safety-critical UAS tasks (CAT III precision approach, UAS Detect-and-Avoid, etc.) when GNSS is used as the primary source of navigation data. There is a synergy with SBAS/GBAS in providing suitable (predictive and reactive) integrity flags in all flight phases. Therefore, the integration of ABIA with SBAS/GBAS is a clear opportunity for future research towards the development of a Space-Ground-Avionics Augmentation Network (SGAAN) for UAS SAA and other safety-critical aviation applications

The antimicrobial photodynamic therapy in the treatment of peri-implantitis

The aim of this study is to demonstrate the effectiveness of addition of the antimicrobial photodynamic therapy to the conventional approach in the treatment of peri-implantitis. Materials and Methods. Forty patients were randomly assigned to test or control groups. Patients were assessed at baseline and at six (T1), twelve (T2), and twenty-four (T3) weeks recording plaque index (PlI), probing pocket depth (PPD), and bleeding on probing (BOP); control group received conventional periodontal therapy, while test group received photodynamic therapy in addition to it. Result. Test group showed a 70% reduction in the plaque index values and a 60% reduction in PD values compared to the baseline. BOP and suppuration were not detectable. Control group showed a significative reduction in plaque index and PD. Discussion. Laser therapy has some advantages in comparison to traditional therapy, with faster and greater healing of the wound. Conclusion. Test group showed after 24 weeks a better value in terms of PPD, BOP, and PlI, with an average pocket depth value of 2 mm, if compared with control group (3 mm).Our results suggest that antimicrobial photodynamic therapy with diode laser and phenothiazine chloride represents a reliable adjunctive treatment to conventional therapy. Photodynamic therapy should, however, be considered a coadjuvant in the treatment of peri-implantitis associated with mechanical (scaling) and surgical (grafts) treatments

Assessing avionics-based GNSS integrity augmentation performance in UAS mission- and safety-critical tasks

The integration of Global Navigation Satellite System (GNSS) integrity augmentation functionalities in Unmanned Aerial Systems (UAS) has the potential to provide an integrity-augmented Sense-and-Avoid (SAA) solution suitable for cooperative and non-cooperative scenarios. In this paper, we evaluate the opportunities offered by this integration, proposing a novel approach that maximizes the synergies between Avionics Based Integrity Augmentation (ABIA) and UAS cooperative/non-cooperative SAA architectures. When the specified collision risk thresholds are exceeded, an avoidance manoeuvre is performed by implementing a heading-based differential geometry or pseudospectral optimization to generate a set of optimal trajectory solutions free of mid-air conflicts. The optimal trajectory is selected using a cost function with minimum time constraints and fuel penalty criteria weighted for separation distance. The optimal avoidance trajectory also considers the constraints imposed by the ABIA in terms of UAS platform dynamics and GNSS satellite elevation angles (plus jamming avoidance when applicable), thus preventing degradation or loss of navigation data during the Track, Decision and Avoidance (TDA) process. The performance of this Integrity-Augmented SAA (IAS) architecture was evaluated by simulation case studies involving cooperative and non-cooperative platforms. Simulation results demonstrate that the proposed IAS architecture is capable of performing high-integrity conflict detection and resolution when GNSS is used as the primary source of navigation data