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The paper aims to develop an improved Fog-related Intelligent Driver Model (FIDM) that reproduces drivers’ car-following behaviour features by taking into account unobserved driver heterogeneity in fog condition. A multi-user driving simulator experiment was performed, and a vehicle fleet consisting of nine vehicles was tested in different fog and speed limits conditions. The experimental results showed that the unobserved driver heterogeneity (the combination of intra-driver heterogeneity and inter-driver heterogeneity) tended to increase as the fog density decreased. The average following distance tended to increase with the decrease of fog density and increase of speed limit. Two indexes were proposed to verify the performance of the FIDM. The results showed that FIDM performed better in reproducing unobserved driver heterogeneity and average following distance compared to the current popular car-following models. This study contributes to an improved car-following model for better understanding traffic flow phenomena under foggy conditions.

Automated Vehicles (AVs) have been receiving increasing attention as a potential highly mechanised, intelligent, self-regulating futuristic mode of transport. AVs are predicted to address limitations and human factors associated with traditional modes of transportation. Beyond the typical operations of AVs which can perform rudimentary tasks, the intelligent embedded program fit in to process challenging scenarios and deep multi-dimensional/ agent intents and interaction of the roadway is the grey area yet to be explored to design an exclusive encoding of social functionality and operation in order to address human factors causing road crashes. The aim of this study is to design a data-driven prediction framework for AVs that utilises multiple inputs to prove a multimodal, probabilistic estimate of the future intentions and trajectories of surrounding vehicles in freeway operation. Our proposed framework is a deep multi-agent learning-based system designed to effectively capture social interactions between vehicles without relying on map information. Our approach excels in capturing the high-level behaviours of multiple vehicles and generating a multi-modal trajectory forecast. It employs a multi-headed neural architecture to learn from social interactions between vehicle pairs and generates diverse trajectories proportional to predicted target intents, thus enabling feature fusion. Additionally, a multi-head self-attention mechanism is incorporated for prediction refinement. We achieved a good prediction performance with a lower prediction error in real traffic data at highways. Evaluation of the proposed framework using the NGSIM (US-101 and I-80) and HighD datasets shows satisfactory prediction performance for long-term trajectory prediction of multiple surrounding vehicles. Additionally, the proposed framework has higher prediction accuracy and generalisability than state-of-the-art approaches.

Improving the safety of autonomous vehicles (AVs) by making driving decisions in accordance with traffic rules is a complex task. Traffic rules are often expressed in a way that allows for interpretation and exceptions, making it difficult for AVs to follow them. This paper proposes a novel methodology for driving decision making in AVs based on defeasible deontic logic (DDL). We use DDL to formalize traffic rules and facilitate automated reasoning, allowing for the effective handling of rule exceptions and the resolution of vague terms in rules. To supplement the information provided by traffic rules, we incorporate an ontology for AV driving behaviour and environment information. By applying automated reasoning to formalized traffic rules and ontology-based AV driving information, our methodology enables AVs to make driving decisions in accordance with traffic rules. We present a case study focussing on the overtaking traffic rule to illustrate the usefulness of our methodology. Our evaluation demonstrates the effectiveness of the proposed driving decision-making methodology, highlighting its potential to improve the safety of AVs on the road.

This paper presents an early prediction framework to classify drivers’ intended intersection movements in a connected vehicle environment. Intersections are considered accident blackspots with major traffic violations that cause property damage, injuries and fatalities. An accurate perception of drivers’ intended movements at intersections is required for advanced red-light (ARLW) or turning warnings for vulnerable road users (TWVR). Early prediction of intersection movement and adequate warning assistance will ensure road users’ safety at the intersection. In this study, we adopted recurrent neural networks (RNN): long short-term memory (LSTM) and gated recurrent units (GRU) networks to predict driver intended movements at intersections using the vehicle kinematics extracted from the Cooperative Awareness Messages (CAMs). We used naturalistic driving data of the Ipswich Connected Vehicle Pilot (ICVP) project, Queensland, which was collected from 351 participants who drove their connected vehicles during the pilot period. The pilot study installed roadside equipment at 29 signalised intersections to enable the Cooperative Intelligent Transportation System (C-ITS) use cases. Vehicle speed, speed limit, longitudinal acceleration, lateral acceleration, and yaw rate are used as predictors and monitored in 100-millisecond intervals for 1s to 4s at different warning distances from the stop line. Separate prediction models are trained based on different monitoring windows. Furthermore, drivers’ intended intersection movements are predicted at two individual intersections to evaluate intersection-specific prediction performance and are found with improved prediction accuracy than overall prediction models trained with all 29 intersections data. Overall prediction models are useful for some intersections which lack available data for individual intersection-based prediction.

Human errors contribute to 94%(±2.2%) of road crashes resulting in fatal/non-fatal causalities, vehicle damages and a predicament in the pathway to safer road systems. Automated Vehicles (AVs) have been a potential attempt in lowering the crash rate by replacing human drivers with an advanced computer-aided decision-making approach. However, AVs are yet to progress in handling the unprecedented situations involving interactions with other road users. This raises a need for a sophisticated and robust methodological framework to predict human driver interaction and intention. It is of prime importance to develop a constructive knowledge on the existing literature for a proficient forward leap in the field. To address this, we aim to conduct a comprehensive review on motion prediction methods in automated driving context with a special emphasis on model-based and data-driven approaches. Over a hundred studies related to the motion prediction for AVs have been extensively reviewed. This study recommends that the field requires more intricate classification of motion prediction methods, as the conventional three-level categorisation scheme should be upgraded to a profound and present-day context. Therefore, we attempt to provide a clear categorisation of existing motion prediction solutions by adopting four principal strategies: 1. Prediction methods, 2. Classes, 3. Algorithms and 4. Datasets. An all-inclusive summary of the reviewed studies with their respective pros and cons are also presented. Furthermore, we summarise the standard evaluation metrics applied for road users’ intention estimation and trajectory prediction tasks. It is found that the recent studies are built upon multi-agent learning systems with interaction among multiple road users in the same road environment. These methods can provide reliable prediction performance in highly interactive situations over long periods of time. However, the limitation could be at the cost of higher computational complexity in comparison to conventional methods, which are simpler to design and computationally effective. It is also observed that the conventional methods can only operate over a narrow prediction horizon and seldom consider the interactions among the road users. This review contributes to knowledge in validation, addresses the discrepancies, to explicate the ambiguities and to streamline current research for a futuristic perspective beneficiary in motion prediction field.

•Increased road and rail traffic result in congestion at railway level crossings.•Level crossing congestion increases significantly road users’ travel time.•A range of technology treatment were evaluated with traffic simulation.•Treatments reducing the gap between estimated and actual train arrival at a crossing are the most beneficial.

ABSTRACTFoxing is a special case of fungal deterioration on paper, recognizable by spots of yellowish to brownish colours. Measuring the carboxymethyl cellulase activity (CMCase) by 3,5-dinitrosalicylic method was used to assess the presence of active cellulase inside foxing stains. This was done on foxed paper samples and compared with five fungal strains (belonging to the species Eurotium herbariorum, Talaromyces atroroseus,and Penicillium minioluteum) with cellulolytic capacity, previously identified inside foxing spots, and a highly cellulolytic strain of Trichoderma viride,as a reference. The highest activity of T. viride, expressed as the reducing equivalent glucose released (mg L−1), is achieved at 26°C and in Czapek medium supplemented with carboxymethyl cellulase and sucrose. In the same conditions, for all strains, the activities reached their highest value on the seventh day and then decreased. The method was sufficiently sensitive to indicate the presence of CMCase activity inside foxing spots. These cellulase activities cause paper degradation even after the fungus dies, as the excreted enzymes remain active on the substrate.

This study proposes to assess the vulnerability of banking sector’s credit portfolio under macroeconomic shocks and to evaluate its impact on banking system capitalization. Our method uses the Global Vector Autoregressive (GVAR) Model to generate adverse macroeconomic scenarios. The GVAR model is combining by the satellite credit risk equation to find the non-performing loan under stress conditions. The advantage of using GVAR model is that on the one hand, it captures the transmission of global, external and domestic macroeconomic shocks on banks non-performing loans. On the other hand, this model considers the nonlinear pattern between business cycle and the bank credit risk indicator during the extreme events as highlighting by the macro stress test literature. The forecast of non-performing loan is then used to obtain stress projections for capital requirement for the banking system level. This article attempts to fill the lacks concerning the stress testing works about Madagascar which study is a recent framework, whose no study on dynamic macro stress testing was treated before. The Results outline the interaction of aggregate non-performing loan with macroeconomic evolution. The horizon of capital prediction shows that banking sector reacts most to a GDP shock. Also, Madagascar banking sector is quite resilient and remains sufficiently capitalized under all macroeconomic scenarios designed with a solvency ratio higher than the minimum regulatory CAR ratio.

Rear-end crash is the most common type of on-road traffic crash, and cell phone use contributes to the increase of rear-end crashes. The effects of cell phone use on driving performance have been thoroughly investigated in previous research with various measurements. However, change in driver’s visual performance while using a cell phone in situations with high rear-end risk has not yet been fully understood. This driving simulator study investigated drivers’ eye movement performance in a rear-end collision avoidance maneuver during cell phone conversation. Eye movement data of 36 participants were collected in a car-following scenario featuring imminent rear-end collision. The whole collision avoidance process was divided into four stages for eye movement data analysis, including normal driving stage, brake response stage, deceleration adjusting stage, and speed recovering stage. Results showed that the average pupil size, fixation duration, and dwell time on the leading vehicle increased significantly during the brake response and deceleration adjusting stages. This indicated that the drivers’ cognitive workload increased during these stages. Drivers used blink inhibition and quick saccade as a visual compensation strategy to mitigate the increased workload from cell phone use during the brake response stage. However, in the deceleration adjusting stage, the cell phone use condition led to a lower fixation frequency on the leading vehicle than in the no phone use condition. Professional drivers were found to pay more visual attention to the leading vehicle than non-professional drivers in the normal driving stage.

Although hard factors such as availability, efficiency, safety, and comfort make up the core of the public transport quality of service, they are only a part of the bigger picture. This bigger picture is modified and shaped daily, where the increasing permeation of technology creates new possibilities for passengers to experience their journeys. Despite technology becoming ubiquitous in public transport, it has had only limited impact on how passengers connect affectively and symbolically with the service. This “affective gap” is at the core of what differentiates private and public transport. Being able to reduce this gap is therefore valuable to passengers and public transport service providers alike. In this paper, we build on existing and ongoing work to discuss the relevance of a passenger-centric approach to innovation in the field of public transport. This passenger-centric approach puts passengers at the centre of future solutions, where their evolving needs, desires, and values are used to guide how to enhance the existing core functionality of the service. To test our hypothesis that passenger-centric innovation is valuable to passengers and advantageous to service providers alike, we put forward a review on how innovation is being approached in both the aviation and car industries. This review is supported by our own insights, which have been based on data collected from urban rail commuters in Australia. We further support our argument by looking at particular examples of innovation in the field of public transport, and discussing the existing barriers and drivers that can, respectively, hinder or propel passenger-centric innovation.

Growing research progress in Internet of Things (IoT), automated/connected cars, Artificial Intelligence and person's data acquisition (Quantified Self) will help to reduce behavioral uncertainties in transport and unequivocally influence future transport landscapes. This vision paper argues that by capitalizing advances in data collection and methodologies from emerging research disciplines, we could make the driver amenable to a knowable and monitorable entity, which will improve road safety. We present an interdisciplinary framework, inspired by the Safe system, to extract knowledge from the large amount of available data during driving. The limitation of our approach is discussed.

Several intelligent transportation systems (ITS) were used with an advanced driving simulator to assess its influence on driving behavior. Three types of ITS interventions were tested: video in vehicle, audio in vehicle, and on-road flashing marker. The results from the driving simulator were inputs for a developed model that used traffic microsimulation (VISSIM 5.4) to assess the safety interventions. Using a driving simulator, 58 participants were required to drive through active and passive crossings with and without an ITS device and in the presence or absence of an approaching train. The effect of changes in driver speed and compliance rate was greater at passive crossings than at active crossings. The slight difference in speed of drivers approaching ITS devices indicated that ITS helped drivers encounter crossings in a safer way. Since the traffic simulation was not able to replicate a dynamic speed change or a probability of stopping that varied depending on ITS safety devices, some modifications were made to the traffic simulation. The results showed that exposure to ITS devices at active crossings did not influence drivers’ behavior significantly according to the traffic performance indicator, such as delay time, number of stops, speed, and stopped delay. However, the results of traffic simulation for passive crossings, where low traffic volumes and low train headway normally occur, showed that ITS devices improved overall traffic performance.

The number of pedestrian victims at Australian and foreign level crossings has remained stable over the past decade and it continues to be a significant problem. To examine the factors contributing to pedestrians’ unsafe crossing behaviours, direct observations were conducted at three black spot urban level crossings in Brisbane for a total of 45 h during morning and afternoon peak. In total, 129 pedestrians transgressed the active controls. More transgressions were observed at the crossings located in more populated suburbs in close proximity to large shopping centres and school zones, whereas the smallest number of transgressions were observed at the least populated locations. In addition to characteristics associated with the larger socio-economic area, the patterns of transgression could be associated with the properties of the existing safety equipment and the design of each level crossing (i.e. location of the platforms, number of rail tracks). Indeed, the largest number of crossed unoccupied but “at risk” rail tracks (where a train could have passed), was observed at the crossing with the least transgressions. Contrary to previous findings, younger adults were the most frequent transgressors. School children and elderly were most likely to transgress in groups. Potential directions for future research and more effective measures are discussed.

Several intelligent transportation systems (ITS) were used with an advanced driving simulator to assess its influence on driving behavior. Three types of ITS interventions were tested: video in vehicle, audio in vehicle, and on-road flashing marker. The results from the driving simulator were inputs for a developed model that used traffic micro-simulation (VISSIM 5.4) to assess the safety interventions. Using a driving simulator, 58 participants were required to drive through active and passive crossings with and without an ITS device and in the presence or absence of an approaching train. The effect of changes in driver speed and compliance rate was greater at passive crossings than at active crossings. The slight difference in speed of drivers approaching ITS devices indicated that ITS helped drivers encounter crossings in a safer way. Since the traffic simulation was not able to replicate a dynamic speed change or a probability of stopping that varied depending on ITS safety devices, some modifications were made to the traffic simulation. The results showed that exposure to ITS devices at active crossings did not influence drivers' behavior significantly according to the traffic performance indicator, such as delay time, number of stops, speed, and stopped delay. However, the results of traffic simulation for passive crossings, where low traffic volumes and low train headway normally occur, showed that ITS devices improved overall traffic performance.

Collisions between pedestrians and vehicles continue to be a major problem throughout the world. Pedestrians trying to cross roads and railway tracks without any caution are often highly susceptible to collisions with vehicles and trains. Continuous financial, human and other losses have prompted transport related organizations to come up with various solutions addressing this issue. However, the quest for new and significant improvements in this area is still ongoing. This work addresses this issue by building a general framework using computer vision techniques to automatically monitor pedestrian movements in such high-risk areas to enable better analysis of activity, and the creation of future alerting strategies. As a result of rapid development in the electronics and semi-conductor industry there is extensive deployment of CCTV cameras in public places to capture video footage. This footage can then be used to analyse crowd activities in those particular places. This work seeks to identify the abnormal behaviour of individuals in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM), Full-2D HMM and Spatial HMM to model the normal activities of people. The outliers of the model (i.e. those observations with insufficient likelihood) are identified as abnormal activities. Location features, flow features and optical flow textures are used as the features for the model. The proposed approaches are evaluated using the publicly available UCSD datasets, and we demonstrate improved performance using a Semi-2D Hidden Markov Model compared to other state of the art methods. Further we illustrate how our proposed methods can be applied to detect anomalous events at rail level crossings.

Every day we are confronted with social interactions with other people. Our social life is characterised by norms that manifest as attitudinal and behavioural uniformities among people. With greater awareness about our social context, we can interact more efficiently. Any theory or model of human interaction that fails to include social concepts could be suggested to lack a critical element. This paper identifies the construct of social concepts that need to be supported by future context-aware systems from an interdisciplinary perspective. It discusses the limitations of existing context-aware systems to support social psychology theories related to the identification and membership of social groups. We argue that social norms are among the core modelling concepts that future context-aware systems need to capture with the view to support and enhance social interactions. A detailed summary of social psychology theory relevant to social computing is given, followed by a formal definition of the process with each such norm advertised and acquired. The social concepts identified in this paper could be used to simulate agent interactions imbued with social norms or use ICT to facilitate, assist, enhance or understand social interactions. They also could be used in virtual communities modelling where the social awareness of a community as well as the process of joining and exiting a community are important.

Social and psychological theories have provided a plethora of evidence showing that the physical difficulty to express appropriate social interactions between drivers expresses itself in aggression, selfish driving and anti-social behaviour. Therefore there is a need to improve interactions between drivers and allow clearer collective decision making between them. Personal characteristics and the driving situations play strong roles in driver’s aggression. Our approach is centered around the driving situation as opposed to focusing on personality characteristics. It examines aggression and manipulates contextual variables such as driver’s eye contact exchanges. This paper presents a new unobtrusive in-vehicle system that aims at communicating drivers’ intentions, elicit social responses and increasing mutual awareness. It uses eye gaze as a social cue to affect collective decision making with the view to contribute to safe driving. The authors used a driving simulator to design a case control experiment in which eye gaze movements are conveyed with an avatar. Participants were asked to drive through different types of intersections. An avatar representing the head of the other driver was displayed and driver behaviour was analysed. Significant eye gaze pattern difference where observed when an avatar was displayed. Drivers cautiously refer to the avatar when information is required on the intention of others (e.g. when they do not have the right of way). The majority of participants reported the perception of “being looked at”. The number of glances and time spent gazing at the avatar did not indicate an unsafe distraction by standards of in-vehicle device ergonomic design. Avatars were visually consulted primarily in less demanding driving situations, which underlines their non-distractive nature.

Mould contamination in the indoor climate has been recognised in recent years as a major concern. In addition to damaging works of art, moulds also can be health hazards. Nowadays, the conventional method to detect and identify such micro-organisms is to put them in culture. Unfortunately, such a method is laborious and time-consuming. Bioluminescence, more particularly ATP methodology, has been widely applied in domains such as medicine, food quality control, environmental protection and pharmacology. Its success on bacteria and yeast has been demonstrated. Many commercial reagent kits are available and we tested some of them. Our early experiments have shown that the use of these kits did not give satisfactory results on mould, reasons being that the provided extraction buffer does not allow a proper extraction of ATP from the fungi’s cells. Therefore new reliable, simple and rapid extraction strategies specific to fungi are required. We compared 40 types of extraction protocols. Extraction with 90% of DMSO in Tris-acetate buffer at 100°C seems to be the best. The viability of cells is estimated by the adenylate energy charge (EC), which corresponds to the equilibrium value of the metabolic energy between the three forms of adenine-containing nucleotides (AT, ADP and AMP) . We applied our method on well-known species such as Aspergillus flavus, Aspergillus niger, Neosartorya fischeri, Fusarium solani, Eurotium chevalieri, Penicillium chrysogenum and Chaetomium globosum.The results presented here are related to species taken both individually and in a mixture. In order to demonstrate and validate our method, we treated each species with steam and ethylene oxide. The results show that the EC of viable cells is greater than 0.80; the EC of killed cells treated with steam is less than 0.50. We observed that the behaviour of cells treated with ethylene oxide is complex. For certain strains, the value of the EC varies between 0.5 and 0.8 according to the disinfection centre. So, our method can be used to rapidly monitor the efficiency of a process of disinfection with ethylene oxide. It has also been used on documents featuring suspicious stains. The results have been compared to conventional methods. It showed that the ATP technique provides a very simple and fast method.

Filamentous fungi are able to damage and even destroy archival and library materials. Nowadays the conventional method for detecting such micro-organisms is to put them in cultures but such methods are laborious and time-consuming. ATP methodology has been widely applied in other domains and its success on bacteria and yeast has been demonstrated. Several commercial reagent kits are available but they did not give satisfactory results on spores mould. We have elaborated new extraction strategies specific to fungi. A comparison of 42 extraction protocols of ATP from fungal spores was carried out. Extraction at 100°C with DMSO 90% in a Tris–acetate–EDTA buffer proved to be the best method. The viability of cells is estimated by the determination of adenylate energy charge (EC). We applied our method successfully on well-known species such as Aspergillus flavus, A. niger, A. fumigatus, A. versicolor, Neosartorya fischeri, Eurotium chevalieri, Penicillium chrysogenum, Chaetomium globosum and Ulocladium spp. The results suggest that the ATP bioluminescence assay provides a sensitive and time-saving method for detecting viable fungal spores. The validity of the procedure was also tested on spores killed by steam and on spores treated with ethylene oxide. We showed that EC determination could be used for a rapid control of the effectiveness of a disinfection process performed with ethylene oxide. Copyright © 2003 John Wiley & Sons, Ltd.

Filamentous fungi are able to damage and even destroy archival and library materials. Nowadays the conventional method for detecting such micro‐organisms is to put them in cultures but such methods are laborious and time‐consuming. ATP methodology has been widely applied in other domains and its success on bacteria and yeast has been demonstrated. Several commercial reagent kits are available but they did not give satisfactory results on spores mould. We have elaborated new extraction strategies specific to fungi. A comparison of 42 extraction protocols of ATP from fungal spores was carried out. Extraction at 100°C with DMSO 90% in a Tris–acetate–EDTA buffer proved to be the best method. The viability of cells is estimated by the determination of adenylate energy charge (EC). We applied our method successfully on well‐known species such as Aspergillus flavus, A. niger, A. fumigatus, A. versicolor, Neosartorya fischeri, Eurotium chevalieri, Penicillium chrysogenum, Chaetomium globosumand Ulocladiumspp. The results suggest that the ATP bioluminescence assay provides a sensitive and time‐saving method for detecting viable fungal spores. The validity of the procedure was also tested on spores killed by steam and on spores treated with ethylene oxide. We showed that EC determination could be used for a rapid control of the effectiveness of a disinfection process performed with ethylene oxide. Copyright © 2003 John Wiley & Sons, Ltd.

In previous work, zein was plasticized with oleic acid to obtain flexible films. However, it was observed that film properties were affected by the preparation method employed. Our objective was to investigate the effect of processing methods on thermal behavior of zein films by differential scanning calorimetry (DSC). Films containing 41% oleic acid were prepared by casting ethanol solutions of its components on flat surfaces or by extrusion of zein resins prepared by cold water precipitation of ethanol solutions of zein and oleic acid. Extrusion was carried out in single-screw or twin-screw extruders. Zein films were finished by hot rolling or heated in a Carver press. DSC thermograms showed large oleic acid melting peaks for cast films, smaller peaks for resin films, and no apparent peaks for heat-treated samples. It was suggested that the resin formation process enhanced zein-oleic acid interactions and promoted plasticization. All samples showed glass transitions at low temperatures.

Glycoglycerolipids constitute a family of glycolipids with apparently very restricted expression in human tissues. They have previously been detected only in the testis and the nervous system. In the present study, two glycoglycerolipids were isolated from the HT29 human colon carcinoma cell line. The glycoglycerolipids were structurally characterized as a monogalactosylglycerolipid (1-O-alkyl-2-O-acyl-3-O-(β-galactosyl)-sn-glycerol) and a digalactosylglycerolipid (1-O-alkyl-2-O-acyl-3-O-(β-galactosyl(1–4)α-galactosyl)-sn-glycerol) using NMR and mass spectrometry. This digalactosylglycerolipid has not previously been structurally characterized. When HT29 cells were allowed to differentiate into more enterocyte-like cells by culture in glucose-free medium, expression of both of these glycoglycerolipids was greatly diminished. The presence of glycoglycerolipids in a human colon carcinoma cell line indicates that expression of this family of glycolipids may not be as restricted as previously thought. Instead this class of glycolipids may serve as differentiation antigens in various normal tissues and in tumor development. The Galα1–4Gal epitope was previously identified as a receptor for bacterial adhesins and toxins. The finding that this epitope is also linked to a glycerolipid moiety opens up new possible roles for this carbohydrate receptor in intracellular signaling.

AbstractFour sesquiterpenes have been isolated from the hydrocarbon fraction of Patchouli (Pogostemon cablin)essential oil using column chromatography. The structure formulae of the compounds, α-guaiene, α-bulnesene, α-patchoulene and seychellene were determined using one and two dimensional NMR and GC/MS. Such results will be useful in the structure determination of sesquiterpenic compounds having guaiane, seychellane and patchoulane skeletons.

La malnutrition chronique constitue le principal problème de malnutrition à Madagascar. Depuis près de 25ans, sa prévalence chez les enfants âgés de moins de 5ans persiste à environ 50 %. Notre étude se propose d’identifier les déterminants de la malnutrition chronique chez les enfants âgés de 6 à 59 mois à Moramanga afin de mieux prioriser les interventions.