Oral Hygiene Facilitators and Barriers in Greek 10 Years Old Schoolchildren

Aim: The aim of this study was to determine the oral hygiene facilitators and barriers for 10 years old Greek children, via a questionnaire and clinical examination. Materials and Methods: This was a cross-sectional study of 266, 10 years old, children recruited from schools in 3 locations in Greece. Data were collected via questionnaires and clinical examination. Questionnaires referred to children\u27s oral hygiene knowledge, behavior and attitude as well as parents\u27 oral hygiene behavior and educational level. Children were clinically examined by two calibrated pediatric dentists using a WHO probe and artificial light to assess dental plaque (hygiene index-HI), gingivitis (simplified gingival index-GIs) and dental caries (DMFT-BASCD criteria). Results: Regarding oral hygiene knowledge, although 80% of the children were literate of the proper means of oral hygiene, only 58.64% brushed their teeth twice daily and 36.84% used dental floss. Children\u27s oral hygiene knowledge was positively correlated with both parental brushing frequency (ρ = 0.175, p \u3c 0.05) and educational level (ρ = -0.216, p \u3c 0.05). Toothpaste use was reported by 92.11% of the children. Regarding children\u27s attitude, 62.28% were concerned whether their teeth were clean, with girls showing greater concern than boys (p \u3c 0.001). Their reported beliefs regarding brushing avoidance were boredom (84.06%), low oral health literacy (73.91%) and forgetfulness (56.52%). Conclusion: Oral hygiene facilitators were found to be the concern about how clean were their teeth, oral health literacy of both children and parents and toothpaste appeal to children. Oral hygiene barriers were children\u27s boredom, low oral health literacy, forgetfulness and low socioeconomic level

Optimization Based Partitioning Selection for Improved Contaminant Detection Performance

Indoor Air Quality monitoring is an essential ingredient of intelligent buildings. The release of various airborne contaminants into the buildings, compromises the health and safety of occupants. Therefore, early contaminant detection is of paramount importance for the timely activation of proper contingency plans in order to minimize the impact of contaminants on occupants health. The objective of this work is to enhance the performance of a distributed contaminant detection methodology, in terms of the minimum detectable contaminant release rates, by considering the joint problem of partitioning selection and observer gain design. Towards this direction, a detectability analysis is performed to derive appropriate conditions for the minimum guaranteed detectable contaminant release rate for specific partitioning configuration and observer gains. The derived detectability conditions are then exploited to formulate and solve an optimization problem for jointly selecting the partitioning configuration and observer gains that yield the best contaminant detection performance

Fault diagnosis for uncertain networked systems

Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: (a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; (b) the modeling of uncertainty by developing an adaptive learning scheme; (c) the uncertainty issues emerging when considering networked systems such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems such as the physical environment, the sensor level, the fault diagnosers, and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated

Fault diagnosis based on set membership identification using output-error models

International audienceSUMMARY The objective of this article is the design of a fault diagnosis method based on set membership identification for systems subject to abrupt parametric faults. The proposed method assumes an output-error, linearly parametrizable model, with unknown but bounded noise corrupting the measurement data. The set membership identification's objective is to compute (a) the smallest volume-wise ellipsoid that contains the nominal parameter vector, which concurrently resides within a data-hypersector and (b) the monotonically nonin-creasing parametric set arisen from the orthotopes that tightly outer bound the ellipsoids. The fault detection mechanism is based on consistency tests between the estimated ellipsoids and the data-hypersectors and the intersection of support orthotopes. At the sample instant of the fault detection, set-theoretic operations are applied for the subsequent fault isolation and identification. The fault isolation is based on the projections of the intersections of orthotopes, while the distance of their centers is used for fault identification. Simulations studies are used to verify the efficiency of the suggested method applied on an electrostatic microactuator subject to several abrupt failure modes

Multi-Objective Cooperative Control for a Ship-Towing System in Congested Water Traffic Environments

This paper proposes a multi-objective cooperative control method for a ship-towing system in congested water traffic environments. The control objectives are to coordinate multiple autonomous tugboats for transporting a ship to: (i) follow the waypoints, (ii) adjust the heading, (iii) track the speed profile, and (iv) resolve collisions. The problem is tackled by the design of multiple control agents distributed in two control layers. Based on the strategy of model predictive control (MPC), the supervisory controller in the higher layer calculates the towing angles and forces of the ship, the tug controller in the lower layer computes the tug thruster forces and moment. The consensus between the lower and higher layer control is achieved by using the altering direction method of multipliers (ADMM) that makes the predicted tug position and heading approach to the desired tug trajectory. Simulation experiments indicate that the proposed method coordinates multiple autonomous tugboats to transport a ship smoothly and effectively and succeeds in multiple control objectives, in the meantime, the avoidance operation complies with COLREGS rules.</p

Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles

This paper presents a novel model-reference reinforcement learning control method for uncertain autonomous surface vehicles. The proposed control combines a conventional model-based control method with deep reinforcement learning. With the conventional model-based control, we can ensure the learning-based control law provides closed-loop stability for the trajectory tracking control of the overall system, and increase the sample efficiency of the deep reinforcement learning. With reinforcement learning, we can directly learn a control law to compensate for modeling uncertainties. In the proposed control, a nominal system is employed for the design of a baseline control law using a conventional control approach. The nominal system also defines the desired performance for uncertain autonomous vehicles to follow. In comparison with traditional deep reinforcement learning methods, our proposed learning-based control can provide stability guarantees and better sample efficiency. We demonstrate the performance of the new algorithm via extensive simulation results.</p

A distributed virtual sensor scheme for marine fuel engines

This paper proposes a virtual sensor scheme designed to compensate for sensor fault effects in marine fuel engines. The proposed scheme design follows a distributed approach, where the marine fuel engine is decomposed in several subsystems. Then, for each subsystem we design a monitoring agent that can actively compensate for the effects of sensor faults occurring in the specific subsystem. This is realized using virtual sensors that can estimate the sensor fault in order to reconstruct the faulty measurements. Due to the Differential-Algebraic mathematical description of marine fuel engine dynamics, we design three types of virtual sensors; using adaptive observers, Set Inversion via Interval Analysis (SIVIA) and static models. Simulation results are used to illustrate the efficiency of the method.Transport Engineering and Logistic

Dynamic Coordination of Multiple Vessels for Offshore Platform Transportation

This paper proposes a novel dynamic coordination control scheme for a physically connected multi-vessel towing system to transport an offshore platform. The transportation process is executed by four tugboats, and each of them has a leading or following role. To render the transportation faster, the roles of the tugboats can be switched in the towing process. The dynamic coordination decision mechanism is designed to allocate in real-time a combination of roles to the tugs by comparing the position and heading of the offshore platform to the next waypoint position. A control allocation strategy is developed to optimally control the position and heading of the tugboats considering multiple constraints. The reference trajectory of the tugboats is dynamically calculated based on the assigned role of each tugboat. A simulation experiment indicates that the proposed control scheme can enhance the maneuver-ability of the physically connected multi-vessel towing system and increase the efficiency of offshore platform transportation. </p

Review of floating object manipulation by autonomous multi-vessel systems

The regulatory endorsement of the International Maritime Organization (IMO) and the support of pivotal shipping market players in recent years motivate the investigation of the potential role that autonomous vessels play in the shipping industry. As the complexity and scale of the envisioned applications increase, research works gradually transform the focus from single-vessel systems to multi-vessel systems. Thus, autonomous multi-vessel systems applied in the shipping industry are becoming a promising research direction. One of the typical research directions is floating object manipulation by multiple tugboats. This paper offers a comprehensive literature review of the existing research on floating object manipulation by autonomous multi-vessel systems. Based on the prior knowledge of object manipulation problems in multi-robot systems, four typical ways of maritime object manipulation are summarized: attaching, caging, pushing, and towing. The advantages and disadvantages of each manipulation way are discussed, including its typical floating object and application scenarios. Moreover, the aspects of control objective, control architecture, collision avoidance operation, disturbances consideration, and role of each involved vessel are analyzed for gaining insight into the approaches for solving these problems. Finally, challenges and future directions are highlighted to give possible inspiration.</p

Distributed model-based sensor fault diagnosis of marine fuel engines

This paper proposes a distributed model-based methodology for the detection and isolation of sensor faults in marine fuel engines. The proposed method considers a Mean Value First Principle model and a wide selection of heterogeneous sensors for monitoring the engine components. The detection of faults is realised based on residuals generated using nonlinear Differential Algebraic estimators combined with adaptive thresholds. The isolation of faults is, then, realised in two levels; local sensor fault detection and isolation agents are designed to monitor specific sensor sets and aim to detect faults in these sets; and a global decision logic is designed to isolate multiple sensor faults that may be propagated between the local monitoring agents. Finally, simulation results are used to illustrate the application of this method and its efficiency.Transport Engineering and Logistic