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Zum Ende des Jahres 2018 wurde in Deutschland der Steinkohlenbergbau eingestellt. Damit beginnt auch in den bis zuletzt aktiven Revieren die Phase des Nachbergbaus inklusive einer dauerhaften Wasserhaltung. Diese beinhaltet typischerweise eine teilweise Grubenflutung und die Ableitung von Grubenwasser in nahegelegene Vorfluter. Im Blickpunkt der öffentlichen und wissenschaftlichen Diskussion stehen bergbauliche Schadstoffquellen und deren Risiken für die oberflächennahen Trinkwasserressourcen. Es existieren eine Reihe Kriterien und Kontrollmechanismen, um potenziellen Schadstoffaustrag zu erkennen und zu verhindern. Dazu zählen moderne Monitoringprogramme und Modellanwendungen, welche bestehende Schadstoffprognosen verbessern können. Daneben ist auch eine rechtlich und fachlich begründete Bewertung von Gefahrenszenarios eine entscheidende Größe für nachhaltiges Risikomanagement. Der Grubenwasseranstieg eröffnet jedoch auch Chancen, ehemalige Bergwerke für energetische Nutzungen umzurüsten. Bereits heute werden Heizkraftwerke mit Grubenwasser betrieben und es besteht großes Ausbaupotenzial. Dieser Beitrag diskutiert die Chancen und Risiken von Grubenflutungen in Deutschland und gibt einen Ausblick, welche Entwicklungen und Herausforderungen in Zukunft zu erwarten sind., Universität Greifswald (1032), Groundwater in former German coal mining areas—a scientific perspective on mine floodings

International audience; Water losses in Water Distribution Networks (WDNs) are inevitable. This is due to joints interconnections, ageing infrastructure and excessive pressure at lower demand. Pressure control has been showing promising results as a means of minimising water loss. Furthermore, it has been shown that pressure information at critical nodes is often adequate to ensure effective control in the system. In this work, a greedy algorithm for the identification of critical nodes is presented. An emulator for the WDN solution is put forward and used to simulate the dynamics of the WDN. A model-free control scheme based on reinforcement learning is used to interact with the proposed emulator to determine optimal pressure reducing valve settings based on the pressure information from the critical node. Results show that flows through the pipes and nodal pressure heads can be reduced using this scheme. The reduction in flows and nodal pressure leads to reduced leakage flows from the system. Moreover, the control scheme used in this work relies on the current operation of the system, unlike traditional machine learning methods that require prior knowledge about the system.

International audience; Underutilized or traditional leafy vegetables are grown in the wild and cultivated. They are consumed as nutritional accompaniments to staples, either raw (fresh), cooked, or in a dried form, through custom, habit, and tradition. These traditional leafy vegetables are natural rich sources of phytochemicals and nutritional compounds. Over time, the keenness for consumption of traditional vegetables has become less popular. Poor nutrient diets are the main cause of mortality and morbidity, especially in developing countries, where the problem is predominant due to poverty. Consumption of traditional vegetables can assist in the prevention of chronic disease development, as they contain various bioactive compounds that exhibit multiple health benefits. Traditional leafy vegetables play a vital role in combatting hunger, food insecurity, and malnutrition, and most are suitable for food intervention programs. African nightshade (Solanum family) is one such commonly consumed traditional leafy vegetable. During dry seasons, communities often face shortages of vegetables; thus, the preservation of edible leaves is one strategy to help overcome this problem. The adoption of solar drying and fermentation are traditional methods to extend the availability of African nightshade vegetables. Additionally, the agronomy practices and postharvest processing methods affect the phytochemicals and nutritional compounds of African nightshade accessions. This mini-review provides information on changes in phytochemicals, nutrition, and antinutritive compounds with different postharvest processing methods and irrigation. The review provides the justification to promote the cultivation for consumption, by identifying the potential African nightshade accessions that are rich in phytonutritional compounds. This mini-review summarizes and discusses the major information on (i) the micro- and macronutrients present in Solanum retroflexum, the most commonly consumed nightshade species compared with other traditional vegetables in Southern Africa, (ii) the composition of phytochemical compounds present in different nightshade accessions, (iii) the impact of irrigation on phytochemical composition in different nightshade species, and (iv) the impact of postharvest processing on phytochemicals and antinutritive compounds in S. retroflexum. Inclusion of African nightshade, especially S. retroflexum, with the main staple foods can improve protein, iron, and calcium levels in daily diets, which will help to improve people's health and well-being.

International audience; Pressure control in water distribution networks (WDNs) is one of the interventions commonly employed to improve the reliability and sustainability of water supply. Various approaches have been proposed to solve the problem of pressure control. However, most schemes that have been proposed rely on the accuracy of a model in order to precisely control a real WDN. Therefore, any deviation between a model and real WDN parameters could render the results of control schemes useless. As a result, this work proposes the utilisation of the reinforcement learning (RL) technique to control nodes pressure in WDNs without solving the model. Quadratic approximation emulators of WDNs and RL agents are used in the proposed scheme. The effectiveness of the proposed scheme is tested on two WDNs networks and the results are compared with the conventional optimisation scheme that is commonly used for simulation cases. The results show that the proposed scheme is able to achieve the desired results when compared to the benchmark optimisation procedure. However, unlike the optimisation procedure, the proposed scheme achieved the results without the numerical solution of the WDNs. Therefore, this scheme could be used in situations where the model of a network is not well defined.

International audience; In many developing countries, society is characterized by increasing population growth, urbanization, and industrialization, which have an effect on the environment. The growing population demands improved and safe ways of transporting people, goods, and services from one point to another at a minimal impact on the environment. The high demand for energy is another concern for any developing nation, most especially for a country where rail cars are powered by electricity. The weight of the rail cars contributes to the total carbon dioxide (CO2) emission. In order to reduce the energy demand, CO2 emission and increase return on investment, there is a need for improved composite materials. The current research focused on the production of composites made from polypropylene, sisal fiber and nanoparticle. The fiber was treated and the content was varied from 10, 20, 30 and 40 wt%, while the clay and the compatibilizer were kept constant at 3 and 5 wt% respectively. There was an improvement in the mechanical and thermal properties of the composites. Based on the improvement observed, the material can be used for the interior component of the rail vehicle.

The underutilised fruits including kei apple, cape gooseberry, marula and monkey orange are fruits widely found in the southern African region. These fruits have the potential to cut to the heart of Africa's great problems in rural development, hunger, malnutrition, and gender inequality. Kei apple, cape gooseberry, marula and monkey orange trees are drought resistant or tolerant plants. Therefore, the domestication of the underutilised fruits found in the southern African region could be considered to be a sustainable solution to enhance the fruit availability, thereby increasing the food security since the global warming currently affects the food production. The fruits are rich in macronutrients, micronutrients, and dietary phytochemicals and have several health benefits. Despite this and the existence of a broad and unlimited niche in terms of the use of these fruits in new product development (food products, medicinal products, etc.), they are mainly processed on a small scale for the production of a few food products. This review also covers food product development from these fruits based on their functional characteristics.

Non-heading Chinese cabbage (Brassica rapa L. subsp. chinensis) is a widely consumed leafy vegetable by the rural people in South Africa. Traditional blanching methods (5%, 10% or 20% lemon juice solutions in steam, microwave treatments and hot water bath at 95 &deg, C) on the changes of colour properties, phenolic metabolites, glucosinolates and antioxidant properties were investigated in this study. Blanching at 95 &deg, C in 5% lemon juice solution maintained the chlorophyll content, reduced the difference in colour change ∆E, and increased the total phenolic content and the antioxidant activities (ferric reducing-antioxidant power assay (FRAP) and Trolox equivalent antioxidant capacity (TEAC) assay). The highest concentration of kaempferol-dihexoside, kaempferol-sophoroside, kaempferol hexoside, and ferulic acid was noted in samples blanched in 5% lemon juice, at 95 &deg, C. However, concentrations of kaempferol O-sophoroside-O-hexoside was highest in raw leaf samples. Supervised Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) and the UPLC-MS and chemometric approach showed the acid protocatechuoyl hexose unique marker identified responsible for the separation of the blanching treatments (5% lemon juice at 95&deg, C) and raw leaves. However, other unidentified markers are also responsible for the separation of the two groups (the raw leaves and the hot water moist blanched samples) and these need to be identified. Blanching at 95 &deg, C in 10% lemon solution significantly increased the glucosinolate sinigrin content. Overall blanching at 95 &deg, C in 5% lemon juice solution can be recommended to preserve the functional compounds in Nightshade leaves.

This study aimed to investigate the influences of fermentation at 37 &deg, C for 3 days by different lactic acid bacterium strains, Lactobacillus plantarum (17a), Weissella cibaria (21), Leuconostoc pseudomesenteroides (56), W. cibaria (64) or L. plantarum (75), on color, pH, total soluble solids (TSS), phenolic compounds and antioxidant activity of African nightshade (leaves). Results indicated fermentation with L. plantarum 75 strain significantly decreased the pH and total soluble solids, and increased the concentration of ascorbic acid after 3 days. L. plantarum 75 strain limited the color modification in fermented nightshade leaves and increased the total polyphenol content and the antioxidant activity compared to the raw nightshade leaves. Overall, L. plantarum75 enhanced the functional potential of nightshade leaves and improved the bioavailability of gallic, vanillic acid, coumaric, ferulic ellagic acids, flavonoids (catechin, quercetin and luteolin) and ascorbic acid compared to the other lactic acid bacterium strains. Correlation analysis indicated that vanillic acid and p-coumaric acid were responsible for the increased antioxidant activity. Proximate analysis of the fermented nightshade leaves showed reduced carbohydrate content and low calculated energy.

International audience; The effect of thyme oil (96 μL L−1) vapours on the expression of phenylalanine ammonia-lyase (PAL) and lipoxygenase(LOX) genes, and control of anthracnose was investigated in naturally infected ‘Hass’ and ‘Ryan’avocado fruit stored for 21 d at 7–7.5 °C, and thereafter kept for 5 d at 20 °C, simulating the supply chainconditions. Exposure to thyme oil vapours prior to cold storage resulted in up-regulation of the PAL gene expressionin both ‘Hass’ and ‘Ryan’ avocado when compared with untreated fruit and those treated with prochloraz®. The higher expression of PAL gene was observed in ‘Ryan’. Thyme oil vapours exposure resulted in thehigher epicatechin content. At the same time, exposure to thyme oil vapours led to down-regulation of LOX geneexpression in both cultivars. Thus, simultaneous up-regulation of PAL gene and down-regulation of LOX gene inavocados exposed to thyme oil vapours prior to cold storage was associated with higher epicatechin content inthe fruit. Therefore, it can be concluded that postharvest application of thyme oil vapours increases anthracnosedisease resistance in ‘Hass’ and ‘Ryan’ avocado fruit.

International audience; Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject's brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the construction of a representation which is invariant to extrinsic perturbations. As covariance matrices should be estimated, this paper first presents a thorough study of all estimators conducted on real EEG recording. Working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in Riemannian geometry and matrix manifold to implement methods for Symmetric Positive-Definite (SPD) matrices. Nonetheless, existing classification algorithms in Riemannian spaces are designed for offline analysis. We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible. The assessment is conducted on real EEG recording: this is the first study on Steady-State Visually Evoked Potential (SSVEP) experimentations to exploit online classification based on Rie-mannian geometry. The proposed online algorithm is evaluated and compared with state-of-the-art SSVEP methods, which are based on Canonical Correlation Analysis (CCA). It is shown to improve both the classification accuracy and the information transfer rate in the online and asynchronous setup.

International audience; A simple device based on friction coefficient control was designed as a solution to the lack of compactness and simplicity encountered in the number of force feedback interfaces. The structure comprises a 64 × 38 × 3 mm copper-beryllium plate on which well-adjusted polarized piezoceramics are glued. The plate stands on four legs, each of which has a spherical end. By controlling the drive voltage, friction force may be varied as required by a user who moves the device on a flat surface, as he or she would do with a normal mouse. This adds the possibility of rendering simulated forces from objects manipulated on a PC screen. Friction forces obtained using Hertz contact theory compare well with the ones measured on an experimental setup, which demonstrate the validity of the approach with regard to force feedback application.

International audience; We describe the molecular epidemiology of cholera in South Africa during 2018-2020. Vibrio cholerae O1 sequence type (ST) 75 recently emerged and became more prevalent than the V. cholerae O1 biotype El Tor pandemic clone. ST75 isolates were found across large spatial and temporal distances, suggesting local ST75 spread.

This study evaluates the performance of commercial reverse osmosis (RO) and nanofiltration (NF) membranes for the removal of metal ions from synthetic water and surface water carried from the north-west of Lake Tanganyika in the city of Uvira, in the east of the Democratic Republic of Congo. Metal ion analyses were performed by the standardized ICP-MS and ICP-OES methods. The RO membrane showed higher metal ion rejection in high-concentration solutions (synthetic samples) prepared in the laboratory as well as in low-concentration samples from real raw water collected near Lake Tanganyika. Rejection levels were higher than 98% for Cr3+, Pb2+, Cd2+, As3+, Ni2+, and Sb+3 ions in the synthetic solutions, and 99.2, 98.8, 98.6, 99.2, 98.4, and 98.8%, respectively, in the real samples. The concentrations of metals in the permeate varied depending on the feed concentration and were 0.15 to 1.02 mg/L, 0.33 to 22 mg/L, and 0.11 to 22 mg/L in RO, NF90, and NF270 membranes, respectively. Regarding the NF membranes, the rejection of Cr, Ni, and Cd ions was interesting: 98.2, 97.8, and 92.3%, respectively. However, it was lower for Pb, As, and Sb ions: 76.9, 52.5 and 64.1%, respectively. The flux of NF was 329 to 375 L/m2.h, much higher than for RO membranes, which had a flux of 98 to 132 L/m2.h. The studied membranes are thus a feasible solution to remove the studied metals from real water sources at low concentrations since they meet the standards of the World Health Organization on specific values assigned to chemicals from industrial sources and human habitation areas where these ions are present in drinking water.

To optimize performance of ad hoc networks, the contradicting objectives: energy conservation and high data rate realization need to be jointly considered. In such a case, network interference need to be controlled in a manner that users transmit at minimum power enough to sustain the transmission. This paper proposes a joint power and rate adaptation in ad hoc network based on coupled interference minimization (PRIM) where the formulated network utility maximization (NUM) is solved using reverse engineering based on Karush-Kuhn-Tucker (KKT) conditions. In that way, users determine their transmit power and data rate based on their local observations (coupled interference). Pricing mechanism is employed in PRIM to restrict users from self-interest behaviours. It is demonstrated theoretically that PRIM satisfies the conditions of the super-modular games such that the local optimality is always the global optimality. Simulation results have shown that adapting transmits power and data rates based on the coupled interference (i.e. link dynamics) can improve the performance of ad hoc networks.

International audience

The present paper focuses on sharpness enhancement and noise removal in two dimensional gray scale images. In the grid smoothing approach, the image is represented by a graph in which the nodes represent the pixels and the edges reflect the connectivity. A cost function is defined using the spatial coordinates of the nodes and the gray levels present in the image. The minimisation of the cost function leads to new spatial coordinates for each node. Using an adequate cost function, the grid is compressed in the regions with large gradient values and relaxed in the other regions. The result is a grid which fits accurately the objects in the image. In the presented framework, the noise in the initial image is removed using a mesh smoothing approach. The edges are then enhanced using the grid smoothing. If the level of noise is low, the grid smoothing is applied directly to the image. The mathematical framework of the method is introduced in the paper. The processing chain is tested on natural images.

The effects of lactic acid fermentation using Lactiplantibacillus plantarum 75 (L75), Leuconostoc pseudomesenteroides 56 (L56) and its combination (L56 + 75) on the quality, bioactive and volatile compounds of mango juices (MJ) from three cultivars (‘Peach’, ‘Sabre’ and ‘Tommy Atkins’) were investigated. Fermented and unfermented MJ were evaluated for LAB growth, physicochemical parameters, volatile compounds, antioxidants activities (DPPH, ABTS, FRAP methods), total phenolic content (TPC) and sensory properties. The unfermented juices served as a control. Twenty-four-hour fermentation was ideal for MJ based on LAB growth profiles. Generally, titratable acidity, TPC, FRAP, DPPH and ABTS scavenging activities significantly increased with fermentation by the L75 strain and were highest in the L75-fermented ‘Sabre’ MJ, while L75-fermented ‘Peach’ MJ had higher ABTS activity (p < 0.05). In contrast, the L56 strain enhanced β-carotene retention, with improved colour properties in L56-fermented ‘Peach’ MJ. Fermentation with L75 in ‘Sabre’ and ‘Peach’ MJ aided the synthesis of new volatile compounds (alcohols, esters, ketones and aldehydes). A PLS-DA scatter plot showed two clusters separating the ‘Peach’ and ‘Sabre’ mango juice fermented with L75 from the rest. Based on the variable importance of the projection value (VIP) scores, pentadecane, 8-hexyl and butyl isobutyrate were shown as marker candidates to distinguish ‘Peach’ and ‘Sabre’ MJ fermented with L75 from the other treatments, whereas ethyl octanoate and isobutyl acetate differentiated the ‘Sabre’ MJ fermented with L75 from the other treatments. ‘Sabre’ and ‘Peach’ MJ fermented with L75 and L56 could provide antioxidants, meeting the recommended daily requirements for ascorbic acid and carotenoids in adults and teenagers. Hence, lactic acid fermentation of these local cultivars is a way to benefit consumers.

International audience; Simple Summary Bacteria can produce diverse homopolysaccharides (HoPSs), which are polymers of glucose, fructose or galactose. The synthesis of these compounds is catalyzed by glycosyltransferases. The range of HoPS sizes is very large and mostly depends on the carbon source in the medium and the catalyzing enzyme; however, factors such as nitrogen nutrients, pH, water activity, temperature and duration of bacterial culture also impact size and yield. The sequence of the polymerization enzyme influences the structure of the HoPS, by modulating the type of linkage between monomers, both for the linear chain and for the ramifications. HoPSs' size and structure influence its rheological properties. As a consequence, the choice of catalyzing enzyme and the modulation of environmental factors open ways to guide the production of specific HoPSs in foods by bacteria. This approach presents many advantages to propose applications of bacterial HoPS to texture foods, either by in situ or ex situ production, but requires a better knowledge of HoPS production conditions. Various homopolysaccharides (HoPSs) can be produced by bacteria: alpha- and beta-glucans, beta-fructans and alpha-galactans, which are polymers of glucose, fructose and galactose, respectively. The synthesis of these compounds is catalyzed by glycosyltransferases (glycansucrases), which are able to transfer the monosaccharides in a specific substrate to the medium, which results in the growth of polysaccharide chains. The range of HoPS sizes is very large, from 10(4) to 10(9) Da, and mostly depends on the carbon source in the medium and the catalyzing enzyme. However, factors such as nitrogen nutrients, pH, water activity, temperature and duration of bacterial culture also impact the size and yield of production. The sequence of the enzyme influences the structure of the HoPS, by modulating the type of linkage between monomers, both for the linear chain and for the ramifications. HoPSs' size and structure have an effect on rheological properties of some foods by their influence on viscosity index. As a consequence, the control of structural and environmental factors opens ways to guide the production of specific HoPS in foods by bacteria, either by in situ or ex situ production, but requires a better knowledge of HoPS production conditions.

Management of water distribution networks (WDNs) is critical for the security of water resources. Climate variability increases the need to properly manage the available resources. A significant amount of water is lost in water supply systems in South Africa, and the quantity of these losses depends mostly on the pressure in the system. Therefore, pressure control in WDNs is one of the interventions commonly employed to improve the reliability and sustainability of the water supply. Excessive pressure in water supply systems (WSSs) increases water loss and pipe breakages, and the need for frequent repairs. On the other hand, inadequate pressure could lead to inward flows (infections) into the system and/or non-delivery of water to the end-user. This could lead to ill-health for the end-user and to socio-economic issues in the society.In general, the pressure control problem is addressed by installing pressure-reducing valves (PRVs) in WDNs and determining their appropriate settings. Various approaches have been proposed to determine the appropriate settings. Classical, optimal and advanced control schemes have been used. These methods rely on the accuracy of a model in order to precisely control a real WDN. Therefore, any variation between the parameters of a model and a real WDN could render a control scheme useless.This research project now proposes the utilisation of the reinforcement learning (RL) technique to control node pressures in WDNs. An emulator-based quadratic approximation of the hydraulic simulation is used as an environment interacting with the RL agent. From these interactions, the RL agent receives the data on current pressures and then proposes appropriate control settings of the PRVs. The performance of the proposed scheme is compared with the conventional optimisation scheme that is commonly used for simulation cases. The results show that the proposed scheme can achieve the desired results, when compared to the benchmark optimisation procedure. However, unlike the optimisation procedure, the proposed scheme achieved the results without the numerical solution of the WDNs. Therefore, this scheme could be applicable in situations where the model of a network is not well defined.; La gestion des réseaux de distribution d'eau (WDN) est essentielle pour la sécurité des ressources en eau. La variabilité climatique augmente la nécessité de gérer correctement les ressources disponibles. Une quantité importante d'eau est perdue dans les systèmes d'approvisionnement en eau en Afrique du Sud, et la quantité de ces pertes dépend principalement de la pression dans le système. Par conséquent, le contrôle de la pression dans les WDN est l'une des interventions couramment utilisées pour améliorer la fiabilité et la durabilité de l'approvisionnement en eau. Une pression excessive dans les systèmes d'approvisionnement en eau (WSS) augmente les pertes d'eau et les ruptures de canalisations, ainsi que le besoin de réparations fréquentes. D'un autre côté, une pression inadéquate pourrait entraîner des flux entrants (infections) dans le système et/ou une non-livraison d'eau à l'utilisateur final. Cela pourrait entraîner une mauvaise santé pour l'utilisateur final et des problèmes socio-économiques dans la société.En général, le problème de contrôle de la pression est résolu en installant des vannes de réduction de pression (PRV) dans les WDN et en déterminant leurs réglages appropriés. Diverses approches ont été proposées pour déterminer les paramètres appropriés. Des schémas de contrôle classiques, optimaux et avancés ont été utilisés. Ces méthodes reposent sur la précision d'un modèle afin de contrôler précisément un WDN réel. Par conséquent, toute variation entre les paramètres d'un modèle et d'un WDN réel pourrait rendre un schéma de contrôle inutile.Ce projet de recherche propose maintenant l'utilisation de la technique d'apprentissage par renforcement (RL) pour contrôler les pressions sur les nœuds dans les WDN. Une approximation quadratique basée sur un émulateur de la simulation hydraulique est utilisée comme environnement interagissant avec l'agent RL. A partir de ces interactions, l'agent RL reçoit les données sur les pressions actuelles et propose ensuite les réglages de contrôle appropriés des PRV. Les performances du schéma proposé sont comparées au schéma d'optimisation conventionnel qui est couramment utilisé pour les cas de simulation. Les résultats montrent que le schéma proposé peut atteindre les résultats souhaités, par rapport à la procédure d'optimisation de référence. Cependant, contrairement à la procédure d'optimisation, le schéma proposé a atteint les résultats sans la solution numérique des WDN. Par conséquent, ce schéma pourrait être applicable dans des situations où le modèle d'un réseau n'est pas bien défini.

α-unsaturated esters are fruity-aromatic compounds which are largely spread in the volatilome of many different fruits, but they are rarely found in the volatilome of yeasts. The yeast S. suaveolens has been recently shown to produce relatively high amounts of α-unsaturated esters and it appears to be an interesting model for the production of these compounds. This study aimed to isolate new α-unsaturated ester-producing yeasts by focusing on strains displaying a similar metabolism to S. suaveolens. While the production of α-unsaturated esters by S. suaveolens is believed to be closely related to its ability to grow on media containing branched-chain amino acids (isoleucine, leucine and valine) as the sole carbon source (ILV+ phenotype), in this study, an original screening method was developed that selects for yeast strains displaying ILV+ phenotypes and is able to produce α-unsaturated esters. Among the 119 yeast strains isolated from the feces of 42 different South African wild animal species, 43 isolates showed the ILV+ phenotype, among which 12 strains were able to produce α-unsaturated esters. Two interesting α-unsaturated esters were detected in two freshly isolated strains, both identified as Galactomyces candidus. These new esters were detected neither in the volatilome of the reference strain S. suaveolens, nor in any other yeast species previously studied for their aroma production. This work demonstrated the efficiency of an original method to rapidly screen for α-unsaturated ester-producing yeasts. In addition, it demonstrated that wild animal feces are interesting resources to isolate novel strains producing compounds with original aromas.

In 2020, the ISO 56000:2020 was published to bring globally harmonized definitions,a common language, and frameworks, to support companies, organizations and policy-makers to structure their innovation efforts. How has it been possible to write global definitions in innovation fields? In what way does this terminology standard differ from the other standards? The purpose of this paper is to highlight the standardization strategy to increase the relevance of terminology standard by integrating the different stakeholders at an early stage, how to disseminate the document and prepare its revision, once it is published., En 2020, l'ISO 56000:2020 a été publiée pour apporter des définitions harmonisées au niveau mondial, un langage commun et des cadres, afin d'aider les entreprises, les organisations et les décideurs à structurer leurs efforts d'innovation. Comment a-t-il été possible de rédiger des définitions mondiales dans les domaines de l'innovation ? En quoi cette norme terminologique diffère-t-elle des autres normes ? L'objectif de ce document est de mettre en évidence la stratégie de normalisation visant à accroître la pertinence de la norme terminologique en intégrant les différentes parties prenantes à un stade précoce, comment diffuser le document et préparer sa révision, une fois qu'il sera publié.

Biopolymeric systems that co-encapsulate probiotics and bioactive compounds ensure timely delivery in the gastrointestinal tract. Cyanidin 3-sambubioside is the dominant anthocyanin in Natal plum (Carissa macrocarpa). This study aims at the co-encapsulation of Natal plum (Carissa macrocarpa) juice inoculated with Lactiplantibacillus plantarum 75 (Ltp. plantarum 75) by freeze-drying using pea protein isolate, maltodextrin, and psyllium mucilage and evaluating their release in vitro. An encapsulation efficiency of >85% was noted in lactic acid bacteria (LAB) survival and anthocyanin content. Freeze-drying produced pinkish-red powder, rich in polyphenols and LAB (>6 Log CFU mL−1) after 14 days of storage. Natal plum juice + maltodextrin + pea protein isolate + psyllium mucilage + Ltp. plantarum 75 (NMPeaPsyB) showed the highest LAB population (6.74 Log CFU mL−1) with a survival rate of 81.9%. After digestion, NMPeaPsyB and NMPeaPsy had the highest LAB survival (>50%) at 67.5% and 67.5 ± 0.75%, respectively, and the highest bioaccessibility of cyanidin 3-sambubioside in Natal plum juice than the other co-encapsulation with other biopolymers. NMPeaPsy and NMPeaPsyB showed phenolic stability in the gastric phase and controlled release in the intestinal simulated phase. The antioxidant activities had strong correlations with cyanidin 3-sambubioside. The results confirmed that microencapsulation is important for improving stability and allowing for the development of functional foods.

This study describes the impact of utilising different strains of lactic acid bacteria (LAB) for the fermentation of papaya puree and their effect on the quality parameters and bioaccessibility of phenolic compounds during simulated in vitro gastrointestinal digestion. Papaya was processed into puree, pasteurised and fermented at 37 °C for 2 days, and stored for 7 days at 4 °C using LAB strains Lactiplantibacillus plantarum 75 (L75*D2, L75*D7), Weissella cibaria&nbsp, 64 (W64*D2, W64*D7) and Leuconostoc pseudomesenteroides 56 (L56*D2, L56*D7), respectively. Non-fermented samples at 0 (PPD0), 2 (PPD2) and 7 days (PPD7) served as controls. pH was reduced with fermentation and was lowest in L56*D2 (3.03) and L75*D2 (3.16) after storage. The colour change (ΔE) increased with the fermentation and storage of purees, L75*D7 showed the highest ΔE (13.8), and its sourness reduced with storage. The fermentation by W64*D7 and L75*D7 increased the % recovery of chlorogenic, vanillic, syringic, ellagic, ferulic acids, catechin, epicatechin and quercetin in the intestinal fraction compared to the L56*D7 and PPD7. Fermentation by W64*D7 and L75*D7 significantly improved the antioxidant capacity of the dialysed fraction compared to the L56*D7 or PPD7. L56*D7-fermented papaya puree showed the highest inhibitory effect of α-glucosidase activity followed by L75*D7. L75*D7 had a significantly higher survival rate. LAB fermentation affected the bioacessibilities of phenolics and was strain dependent. This study recommends the use of Lpb. plantarum 75 for fermenting papaya puree.

In the course of evolution, pecorans (i.e., higher ruminants) developed a remarkable diversity of osseous cranial appendages, collectively referred to as “headgear,” which likely share the same origin and genetic basis. However, the nature and function of the genetic determinants underlying their number and position remain elusive. Jacob and other rare populations of sheep and goats are characterized by polyceraty, the presence of more than two horns. Here, we characterize distinct POLYCERATE alleles in each species, both associated with defective HOXD1 function. We show that haploinsufficiency at this locus results in the splitting of horn bud primordia, likely following the abnormal extension of an initial morphogenetic field. These results highlight the key role played by this gene in headgear patterning and illustrate the evolutionary co-option of a gene involved in the early development of bilateria to properly fix the position and number of these distinctive organs of Bovidae.

The Narrowband-Internet of Things (NB-IoT) is one of the leading licensed Low Power Wide Area Networks (LPWANs) that is expected to play a very important role in connecting the exponentially increasing number of connected devices that make up the Internet of Things (IoT). To enable its network scalability, NB-IoT requires more efficient utilization of its limited energy, channel capacity, and spectrum network resources. These characteristics place various constraints in terms of the design of the physical (PHY) channel coding and spectrum access as well as the network layer protocol for the NB-IoT. A few existing channel coding approaches have been proposed to enhance the NB-IoT’s network reliability, and therefore, enable long-distance transmissions. However, most of the existing NB-IoT channel coding approaches are based on repeating transmission data and control signals and do not consider the channel conditions. Although these repetitions ensure network reliability, they are energy expensive and compromise the performance of the data rate of the network. At the same time, the increasing number of devices to be handled by a single Base Station (BS) within a cell increases the probability for interlink interference which compromises the uplink communication reliability, further increases the number of required data transmission repetitions, and therefore, reduces the overall energy efficiency of the network. To mitigate these identified problems, a multi-layer approach is proposed in this study. Firstly, a channel-aware two-dimensional Energy-Efficient Adaptive Channel Coding (EEACC) approach is considered at the PHY layer. The EEACC not only selects an appropriate modulation coding scheme (MCS) based on the estimated channel conditions (dynamically classified as bad, medium or good based on a periodically assessed BLER performance outcome) but also minimizes the transmission repetition number under a pre-assessed probability of successful transmission. Secondly, at the PHY and network layers, the study considers a two-layers NB-IoT mixed spread spectrum and clustering approach with network coding (N-MANC). At one layer, the approach uses an Adaptive Frequency Hopping Spread Spectrum (AFHSS) technique that uses a lightweight and secure pseudo-random sequence with a varying period T depending on the number of NB-IoT nodes in the considered cell to exploit the channel diversity as a way to mitigate inter-link and cross-technology interference. At the second layer, the mixed approach consists of a cooperative clustering approach based on a defined energy-signal strength gradient used for Cluster Head (CHs) selection, and a network coding (data aggregation) to further boost the overall energy efficiency of the network. A systematic mathematical model of the NB-IoT PHY layer is first developed and modelled. Then, the proposed algorithms are implemented and validated in MATLAB. The obtained simulation results demonstrate that the important QoS metrics such as energy efficiency, network reliability, data rate, transmission latency, network scalability, and lifetime are improved by the proposed algorithms when compared to existing approaches., L'Internet des objets à bande étroite (NB-IoT) est l'un des principaux réseaux étendus à basse consommation énergétique (LPWAN) dans la bande des fréquences sous licence d’utilisation, qui devrait jouer un rôle très important dans la connexion du nombre exponentiellement croissant d'objets connectés qui composent l'Internet des objets (IoT). Pour permettre l'évolutivité de son réseau, la NB-IoT nécessite une utilisation plus efficace de son énergie limitée, de la capacité de ses canaux et des ressources du spectre du réseau. Ces caractéristiques créent ainsi diverses contraintes en termes de conception du codage physique des canaux (PHY) et de l'accès au spectre ainsi que du protocole de la couche réseau pour l'Internet des objets. Quelques approches de codage de canal existantes ont été proposées pour améliorer la fiabilité du réseau de la NB-IoT et permettre ainsi les transmissions à longue distance. Cependant, la plupart des approches de codage de canal existantes de la NB-IoT sont basées sur la répétition des données de transmission et des signaux de contrôle et ne tiennent pas compte des conditions du canal. Bien que ces répétitions assurent la fiabilité du réseau, elles sont coûteuses en énergie et compromettent les performances du réseau en matière de débit de données. En même temps, le nombre croissant de dispositifs devant être traités par une seule station de base (BS) au sein d'une cellule augmente la probabilité d'interférences entre les liaisons, ce qui compromet la fiabilité de la communication sur la liaison montante, augmente encore le nombre requis de répétitions de transmission de données et, par conséquent, réduit l'efficacité énergétique globale du réseau. Pour pallier ces divers problèmes identifiés, une approche multicouche est proposée dans cette thèse. Tout d'abord, une approche bidimensionnelle de codage de canal adaptatif économe en énergie (EEACC) est proposée au niveau de la couche PHY. L'EEACC ne se contente pas de sélectionner un schéma de codage de modulation (MCS) approprié en fonction des conditions estimées du canal (classé dynamiquement comme mauvais, moyen ou bon sur la base d'un résultat de performance BLER évalué périodiquement), mais il minimise également le nombre de répétitions de transmission en fonction d'une probabilité préévaluée de réussite de la transmission. Deuxièmement, en ce qui concerne les couches PHY et réseau, l'étude développe une approche à deux couches NB-IoT `a étalement mixte du spectre et à regroupement avec codage du réseau (N-MANC). Sur une couche, l'approche utilise une technique d'étalement du spectre par saut de fréquence adaptative (AFHSS) qui utilise une séquence pseudo-aléatoire légère et sûre avec une période variable T selon le nombre de nœuds NB-IoT dans la cellule considérée pour exploiter la diversité des canaux comme moyen d'atténuer les interférences entre les liaisons et les technologies croisées. A la deuxième couche, l'approche mixte consiste en une approche de regroupement coopérative basée sur un gradient de force du signal énergétique défini utilisé pour la sélection des têtes de grappe (CHs), et un codage du réseau (agrégation de données) pour stimuler davantage l'efficacité énergétique globale du réseau. Un modèle mathématique systématique de la couche PHY du NB-IoT est d'abord développé et modélisé. Ensuite, les algorithmes proposés sont mis en œuvre et validés utilisant les simulations MATLAB. Les résultats de simulation obtenus montrent que les algorithmes proposés améliorent les paramètres importants de la qualité de service (QoS), tels que l'efficacité énergétique, la fiabilité du réseau, le débit de données, la latence de transmission, l'extensibilité du réseau et la durée de vie, par rapport aux approches existantes.

International audience; Not much information is available on the changes of phenolic metabolites or anti-nutritive compounds in African nightshade leaves during moist cooking. Blanching methods (boiling, microwave, and steaming) using 5-20% lemon juice was compared with the plain water as a control. Lemon juice at 20% significantly increased the total colour differences (ΔE). Chlorophyll content and the overall acceptance were highest for samples steamed in 5% lemon juice. Steam blanching in plain water reduced the tannin, oxalate and phytate contents but showed higher comparative peak responses for steroidal saponins. Neochlorogenic, chlorogenic, and caffeoylmalic acid, kaempferol O-rhamnosyl hexoside, and rutin were identified blanched leaves. Principle component analysis (PCA) discriminated between blanching treatments while Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) showed clear group distinctions between the blanching treatments. Chlorogenic acid, neochlorogenic acid and cryptochlorogenic acid were the responsible biomarkers for the separation of the steam blanching treatments. Thus, steam blanching preserves the functional compounds in nightshade leaves.

The present paper introduces an extension of the quantile periodogram developed in Li (2012) since the latter suffers from instabilities and spectral leakage. To overcome these drawbacks, a L 1 regularised version is proposed. The asymptotic properties of the new spectral estimator are discussed and simulations are performed in order to show its efficiency in the presence of hidden periodicities and under different types of noise.

In the 21st century, energy demand and the attendant environmental degradation, are among the most challenging issues. The concern is due to the high dependence, globally on fossil fuels as a form of energy generation. Over 6.5 billion people worldwide require approximately 13 Terawatts of energy for their day-to-day needs. In order to achieve the required energy demand, there is a need to diversify into other forms of energy; in this case, renewable energy. In so doing, there is the need to study, extensively, alternative materials and sources needed for energy generation, storage, distribution and application. There has been a significant advancement in energy generation, conversion and storage, such as fuel cells and solar cells, photovoltaic cells, supercapacitors, batteries, etc. The emergence of nanostructured and composite materials has resulted in some significant contributions towards the improvement in the energy industry development. Renewable energy, such as wind and solar energies, depend considerably, on the environmental conditions, which are not always stable. Hence, in order to harness the energy from these sources and to adequately store such energy, there is a need for a high-performance energy conversion and storage system for the energy generation process. In this regard, carbon nanomaterials, metallic sulphides, titanium oxide and many other nanostructured materials have been studied, to a large extent, for energy conversions and storage devices. The importance of nanostructured and composite materials has shown, from researches, to resolve the issues surrounding energy from generation to storage. Keywords: Nanostructured, Composites, Renewable energy, Energy application

Batteries typically used in electric vehicles have up to one hundred times lower energy-to-weight ratios (net calorific value, Wh/kg) than most fossil fuels used in internal combustion engines. Furthermore, solar-powered electric vehicles rely mostly on harvesting energy from solar irradiation (emitted by the sun) which is weather dependant. The need to optimise the energy usage of battery assisted solar-powered electric vehicles is crucial to maximise the range of such a vehicle. If the distance is fixed, weather conditions constant, and the topography flat, one can approximately optimise the energy usage of a solar car by finding the speed, which on average optimises energy usage over an entire journey. Such an approach provides a good guestimate for example for the Bridgestone World Solar Challenge (BWSC) in Australia (taking place primarily in the desolate outback regions) where the topography is predominantly flat, and the weather conditions are relatively constant and therefore very predictable. Contrastingly, the Sasol Solar Challenge (SSC) route in South Africa contains complex topography, which includes various mountainous regions with steep slopes and frequent changes in the gradient of the road. Also, events such as the BWSC, are fixed distance routes, where the SSC is a variable distance route.Various methods to optimise the energy usage of a solar car in a BWSC setting have been proposed. However, none of these methods are not suitable for the SSC context. This work is devoted to both theoretical research and novel applications of bi-level optimisation techniques to minimise energy usage and maximise distance travelled by a solar car participating in an SSC event in South Africa.The early chapters of the work create a foundation in terms of the current state-of-the-art when considering vehicle modelling and interpretation of weather forecasts. A detailed solar car energy model is devised, and Model Output Statistics (MOS) are employed to improve the accuracy and confidence interval of local weather forecasts required by the energy model. Extensive real-world experiments validated the robustness and accuracy of the energy model. The optimisation problem is formulated as a bi-level optimisation problem which makes use of Sequential Quadratic Programming (SQP) and Dynamic Programming (DP) solver techniques. The bi-level optimisation problem is implemented in the form of a User Interface (UI) for ease of use by the energy manager.The novel variable distance bi-level optimisation technique was implemented during the eight days of the Sasol Solar Challenge 2018. It provided the solar car team from TUT with the technological advantage required to obtain a local 1st place as well as a 4th place overall (internationally). The implementation highlighted the superiority of the bi-level technique when compared to conventional energy management techniques on various occasions, especially during extreme weather conditions.The accuracy and performance of the bi-level optimisation technique was thoroughly assessed and analysed. It was found that on average, 94 % of the variation in the energy simulation (State of Charge) error can be explained by the variables contained within the derived energy model. The remaining 6 % variation in the energy simulation error may be as a result of small un-modelled vehicle dynamics (including Coriolis forces), non-linearity of the charge and discharge cycles of the battery and temperature effects., Les batteries généralement utilisées dans les véhicules électriques ont un rapport énergie-poids jusqu'à cent fois plus faible (pouvoir calorifique net, Wh / kg) que la plupart des combustibles fossiles utilisés dans les moteurs à combustion interne.De plus, les véhicules électriques à énergie solaire dépendent principalement de la récupération de l'énergie provenant de l'irradiation solaire (émise par le soleil) qui dépend des conditions météorologiques. La nécessité d'optimiser la consommation d'énergie des véhicules électriques à batterie fonctionnant à l'énergie solaire est cruciale pour maximiser la portée d'un tel véhicule.Si la distance est fixe, les conditions météorologiques constantes et la topographie plate, on peut approximativement optimiser la consommation d'énergie d'une voiture solaire en trouvant la vitesse, qui optimise en moyenne la consommation d'énergie sur tout un trajet. Une telle approche fournit une bonne estimation par exemple pour le Bridgestone World Solar Challenge (BWSC) en Australie. En revanche, la route Sasol Solar Challenge (SSC) en Afrique du Sud contient une topographie complexe, qui comprend diverses régions montagneuses avec des pentes abruptes et des changements fréquents dans le gradient de la route. Ce travail est consacré à la fois à la recherche théorique et aux nouvelles applications des techniques d'optimisation à deux niveaux, pour minimiser la consommation d'énergie et maximiser la distance parcourue par une voiture solaire participant à un événement SSC en Afrique du Sud. Les premiers chapitres de l'ouvrage jettent les bases sur l'état actuel de la technique lors de l'examen de la modélisation des véhicules et de l'interprétation des prévisions météorologiques. Un modèle détaillé d'énergie solaire de voiture est conçu et des statistiques de sortie de modèle (MOS) sont utilisées pour améliorer la précision et l'intervalle de confiance des prévisions météorologiques locales requises par le modèle énergétique. Le problème d'optimisation est formulé comme un problème d'optimisation à deux niveaux qui utilise des techniques de solveur de programmation quadratique séquentielle (SQP) et de programmation dynamique (DP). Le problème d'optimisation à deux niveaux est mis en œuvre sous la forme d'une interface utilisateur (UI) pour une facilité d'utilisation par le gestionnaire d'énergie.

A series of cellulose triacetate/Ludox-silica nancomposite pervaporation membranes was successfully prepared via solution casting, aiming to improve the performance of cellulose triacetate membranes for desalination. The fabricated nanocomposite membranes were characterized to study the membrane morphology, chemical composition, mechanical properties, and surface hydrophilicity. Furthermore, the desalination performance was investigated as a function of silica (SiO2) loading (ranging from 1 to 4 wt.%) and feed concentration at 30 g/L and 60 g/L of sodium chloride (NaCl). Pervaporation experiments showed that incorporating 4 wt.% SiO2 into a cellulose triacetate (CTA) membrane increased the water flux by a factor 2.5 compared to pristine CTA (from 2.2 kg m-2 h-1 to 6.1 kg m-2 h-1) for a 30 g/L NaCl feed solution at 70°C, while the salt rejection remained above 99 %. The CTA/4 wt.% SiO2 membrane was found to have only 21 % flux reduction when tested with a 60 g/L NaCl feed solution, without changes in membrane selectivity. This suggests that the developed CTA/Ludox-SiO2 nanocomposite pervaporation membrane is suitable for desalination.

In recent years, various energy sources and methods have been used to heat water in domestic and commercial buildings. The known sources for water heating include electrical energy and solar radiation energy in the urban regions or burning of firewood in the rural areas. Several water heating methods may be used such as electrical heating elements, solar concentrators, flat plate collectors and evacuated tube collectors. This thesis focuses on ways to further improve the system’s performance for water heating through the combined use of solar energy and solar concentrator technique. Furthermore, the study proposed an alternative design method for the hot water storage tank.The solar collector-supporting frame was designed and analysed using Solidworks®. The forces acting on the structural members were simulated to determine the capacity of the frame to sustain the load, and the possible regions on the supporting frame, which could potentially fail while in operation.Energy performance was simulated for five years of operation using Matlab Simulink® software. This simulation was based on the use of three different data. The first is a five-year weather database of the City of Tshwane in South Africa. The second is a hot water consumption profile for a typical household. The third is the cost of additional heating with electricity depending on the time of use. This simulation allowed the validation of the choices of the different elements of the heating system.This study allowed the development of an approach for the design of a solar heating system by optimising the dimensions of the different elements for a typical household and a specific region.In addition, the use of polymeric materials and other materials like polyurethane, salt and aluminium is possible for the development of a hot water storage tank based on their inherent properties.Extending the findings in this thesis will further improve the designs for solar concentrator technologies and solar water heating systems. Therefore, some recommendations and suggestions are highlighted in order to improve the overall system design, analysis and performance, Les sources d’énergies utilisées pour le chauffage de l’eau dans les bâtiments commerciaux et résidentielles sont multiples. Ces ressources sont essentiellement électriques dans les milieux urbains et utilisent le bois dans les milieux ruraux. Le pourcentage de l’énergie solaire utilisé reste assez faible. Les méthodes les utilisées pour produire l’eau chaude sont pour basés pour l’essentielle sur l’utilisation des résistances électrique ou des capteurs solaire plat. Le travail présenté dans cette thèse est basé sur l’utilisation des concentrateurs solaires pour chauffer des collecteurs d’énergie. Le rendement est augmenté par le développement de nouveau matériaux pour le stockage.La structure pour le support du collecteur a été conçue et analysée utilisant le logiciel Solidworks®. Les forces agissant sur les éléments de la structure sont simulées pour assurer la fiabilité du support lors des différentes conditions de fonctionnement. L’analyse par la méthode des éléments finis a permis la vérification de la structure utilisée pour le réflecteur et son support.Les performances énergétiques ont été simulées pour cinq ans d’opération utilisant le logiciel Matlab Simulink®. Cette simulation a été basée sur l’utilisation de trois données différentes. La première est une base de données météorologique de cinq ans en Afrique du Sud dans la Ville de Tshwane. La deuxième est un profil d’utilisation pour un foyer type. La troisième est le coût de complément de chauffage en électricité dépendant de l’heure de l’utilisation. Cette simulation a permis la validation des choix de dimensions de différents éléments du système de chauffage.Cette étude a permis le développement d’une approche pour la conception d’un système de chauffage solaire en optimisant les dimensions des différents éléments pour un foyer type et une région spécifique.De plus, nous avons conçu un autre réservoir d’eau chaude. Nous avons démontré que l'utilisation de matériaux polymères et d'autres matériaux comme le polyuréthane, le sel et l'aluminium est possible pour le développement d'un réservoir de stockage d'eau chaude en fonction de leurs propriétés inhérentes.L'extension des résultats de cette thèse améliorera encore les conceptions des technologies de concentrateurs solaires et des systèmes de chauffage solaire de l'eau. Par conséquent, certaines recommandations et suggestions sont mises en évidence afin d'améliorer la conception, l'analyse et les performances globales du système

International audience

A two-junction micro p+np+ silicon avalanche-mode light-emitting device (Si AMLED) is analyzed for its dispersion characteristics, which generally resulted in different wavelengths of light (colors) being emitted at different angles from the surface of the device. The SiAMLED is integrated into on-chip bipolar radio frequency-integrated circuitry at micron dimensions. LEDs have high-frequency modulation frequencies reaching into the GHz range. Such devices, which are of micron dimension, operate at 8 to 10 V, 1 μA to 2 mA. The emission wavelength is in the 450- to 850-nm range, emission spot sizes are about 1 μm2, and emission intensities are up to 200 nW . μm − 2. The observed geometrical-chromatic dispersion characteristics range from 0.01 deg / nm wavelength for green radiation at a 5 deg exit angle to the normal of the device to 0.16 deg / nm wavelength for blue radiation at a 60 deg exit angle to the normal of the surface of the device. The high dispersion characteristics of the emitted radiation are attributed to the positioning of the optical source ∼1 μm subsurface to the silicon–silicon oxide interface, as well as to the high-refractive index differences between silicon and the surrounding lower refractive index silicon oxide layers. It is believed that the identified dispersion characteristics will have interesting and futuristic on-chip electro-optic applications for on-chip micro-optical wavelength dispersers, futuristic optical communication demultiplexers, along with on-chip microgas and biosensor applications.

International audience; Modern water distribution networks (WDNs) are complex and difficult to manage due to increased level of urbanization, varying consumer demands, ageing infrastructure, operational costs, and inadequate water resources. The management problems in such complex networks may be classified into short-term, medium-term, and long-term, depending on the duration at which the problems are solved or considered. To address the management problems associated with WDNs, mathematical models facilitate analysis and improvement of the performance of water infrastructure at minimum operational cost, and have been used by researchers, water utility managers, and operators. This paper presents a detailed review of the management problems and essential mathematical models that are used to address these problems at various phases of WDNs. In addition, it also discusses the main approaches to address these management problems to meet customer demands at the required pressure in terms of adequate water quantity and quality. Key challenges that are associated with the management of WDNs are discussed. Also, new directions for future research studies are suggested to enable water utility managers and researchers to improve the performance of water distribution networks.

D’Tech Thesis SummaryThe importance of medical imaging as a core component of several medical application and healthcare diagnosis cannot be over emphasised. Integration of useful data acquired from different images is vital for proper analysis of information contained in the images under observation. For the integration process to be successful, a procedure referred to as image registration is necessary.The purpose of image registration is to align two images in order to find a geometric transformation that brings one image into the best possible spatial correspondence with another image by optimising a registration criterion. The two images are known as the target image and the source image. Image registration methods consist of having the two images referenced with control points. This is followed by a registration transformation that relates the two images and a similarity metric function that aims to measure the qualitative value of closeness or degree of fitness between the target image and the source image. Finally, an optimiser which seeks an optimal transformation inside the defined solution search space is performed.This research presents an automated image registration algorithm for solving multimodal image registration on lung Computer Tomography (CT) scan pairs, where a comparison between regular step gradient descent optimisation technique and evolutionary optimisation was investigated. The aim of this research is to carry out optimisation and performance evaluation of image registration techniques in order to provide medical specialists with estimation on how accurate and robust the registration process is. Lung CT scan pairs are registered using mutual information as a similarity measure, affine transformation and linear interpolation. In order to minimise the cost function, an optimiser, which seeks the optimal transformation inside the defined search space is applied.Determination of a transformation model that depends on transformation parameters and identification of similarity metric based on voxel intensity were carried out. By fitting transformation to control points, three transformation models were compared. Affine transformation produced the best recovered image when compared to non-reflective similarity and projective transformations. The results of this research compares well with documented results from EMPIRE 10 Challenge research and conforms to both theoretical principles as well as practical applications.The contribution of this research is its potential to increase the scientific understanding of image registration of anatomical body organs. It lays a basis for further research in performance evaluation of registration techniques and validation of procedures to other types of algorithms and image registration application areas, such as remote sensing, satellite communication, biomedical engineering, robotics, geographical information systems and mapping, among others, Résumé : Thèse de DoctoratL’importance de l’imagerie médicale comme élément principal dans plusieurs application médicales et diagnostiques de soin de santé est indispensable. L’intégration des données utiles obtenues des diverses images est vitale pour une analyse appropriée de l’information contenues dans ces images sous observation. Pour que ce processus d’intégration réussisse, une procédure appelée recalage d’image est nécessaire.Le but du recalage d’image consiste à aligner deux images afin de trouver une transformation géométrique qui place une des deux images dans la meilleure correspondance spatiale possible avec l’autre image en optimisant un critère de recalage. Les deux images sont dites image cible et image source. Les méthodes de recalage d’image consistent à avoir référencées par des points de contrôle. Ceci est suivi d’une transformation de recalage qui associe les deux images et d’une fonction définie sur base de la mesure de similarité qui a pour but de mesurer la valeur qualitative de proximité ou encore de degré de concordance entre l’image cible et l’image source. Enfin, un optimisateur qui cherche à trouver la transformation optimale au sein du champ de solution de la recherche, est appliqué.Cette recherche présente un algorithme automatique de recalage d’image dont le but est de résoudre le problème du recalage d’image à multiple modes sur une paire des clichés de tomographie par ordinateur (CT) faite sur les poumons. Dans cette méthode, une étude comparative entre la méthode classique d’optimisation par algorithme du gradient à pas fixe et celle de l’algorithme évolutionniste est menée. L’objectif de cette recherche est d’effectuer l’optimisation par des techniques de recalage d’image ainsi qu’évaluer la performance de ces mêmes techniques afin de doter les spécialistes du domaine médical d’une estimation de combien précis et robuste le processus de recalage serait. Les paires des clichés obtenues de la tomographie par ordinateur faite sur les poumons sont recalées en utilisant l’information mutuelle comme mesure de similarité, la transformation affine ainsi que l’interpolation linéaire. Un optimisateur qui recherche la transformation optimale au sein de l’espace de recherche est appliqué afin de minimiser la fonction économique (fonction objectif).Les études de détermination d’un modèle de transformation qui dépend des paramètres de transformation et de l’identification des mesures de similarité basée sur l’intensité du voxel ont été menées. En alignant la transformation avec les points de control, trois modèles de transformation sont comparés. La transformation affine produit la meilleure reconstitution d’image en comparaison aux transformations non réfléchissantes et projectives. Les résultats de cette recherche sont assez comparables à celles rapportées dans le challenge de recherche EMPIRE 10 et sont conformes à la fois aux principes théoriques aussi bien qu’aux applications pratiques.La contribution de cette recherche réside dans son potentiel à améliorer la compréhension scientifique du recalage d’image des organes anatomiques du corps humain. Cette recherche établie ainsi une base pour une recherche avancée sur l’évaluation de la performance des techniques de recalage et la validation des procédures sur d’autres types d’algorithmes et domaines d’application du recalage d’images comme la détection, la communication par satellite, l’ingénierie biomédicale, la robotique, les systèmes d'information géographique (SIG) et de localisation parmi tant d’autres

Part 2: ICT4D and Improvement of ICTs; International audience; Cybersecurity capability and innovation cannot be attained by a single party; researchers, government, private industry and academia should join hands and create public-private partnerships to share their knowledge and create solutions. If South Africa wants to be sufficiently equipped to respond to cyber-threats and to ensure growth in the cybersecurity sector, the country needs to strengthen the pipeline of cyber talent and support the development of a cybersecurity workforce. These requirements provide an opportunity for industry, in collaboration with government and academia, to initiate innovative and exciting approaches to establish cybersecurity and a cybersecurity workforce in South Africa. This paper considers measures for governments, business and academia to alleviate the cyber skills shortage, and pay particular attention to the South African case.

In the last two decades, interest in Brain-Computer Interfaces (BCI) has tremendously grown, with a number of research laboratories working on the topic. Since the Brain-Computer Interface Project of Vidal in 1973, where BCI was introduced for rehabilitative and assistive purposes, the use of BCI has been extended to more applications such as neurofeedback and entertainment. The credit of this progress should be granted to an improved understanding of electroencephalography (EEG), an improvement in its measurement techniques, and increased computational power.Despite the opportunities and potential of Brain-Computer Interface, the technology has yet to reach maturity and be used out of laboratories. There are several challenges that need to be addresses before BCI systems can be used to their full potential. This work examines in depth some of these challenges, namely the specificity of BCI systems to users physical abilities, the robustness of EEG representation and machine learning, and the adequacy of training data. The aim is to provide a BCI system that can adapt to individual users in terms of their physical abilities/disabilities, and variability in recorded brain signals.To this end, two main avenues are explored: the first, which can be regarded as a high-level adjustment, is a change in BCI paradigms. It is about creating new paradigms that increase their performance, ease the discomfort of using BCI systems, and adapt to the user’s needs. The second avenue, regarded as a low-level solution, is the refinement of signal processing and machine learning techniques to enhance the EEG signal quality, pattern recognition and classification.On the one hand, a new methodology in the context of assistive robotics is defined: it is a hybrid approach where a physical interface is complemented by a Brain-Computer Interface (BCI) for human machine interaction. This hybrid system makes use of users residual motor abilities and offers BCI as an optional choice: the user can choose when to rely on BCI and could alternate between the muscular- and brain-mediated interface at the appropriate time.On the other hand, for the refinement of signal processing and machine learning techniques, this work uses a Riemannian framework. A major limitation in this filed is the EEG poor spatial resolution. This limitation is due to the volume conductance effect, as the skull bones act as a non-linear low pass filter, mixing the brain source signals and thus reducing the signal-to-noise ratio. Consequently, spatial filtering methods have been developed or adapted. Most of them (i.e. Common Spatial Pattern, xDAWN, and Canonical Correlation Analysis) are based on covariance matrix estimations. The covariance matrices are key in the representation of information contained in the EEG signal and constitute an important feature in their classification. In most of the existing machine learning algorithms, covariance matrices are treated as elements of the Euclidean space. However, being Symmetric and Positive-Definite (SPD), covariance matrices lie on a curved space that is identified as a Riemannian manifold. Using covariance matrices as features for classification of EEG signals and handling them with the tools provided by Riemannian geometry provide a robust framework for EEG representation and learning.; Au cours des deux dernières décennies, l'intérêt porté aux interfaces cérébrales ou Brain Computer Interfaces (BCI) s’est considérablement accru, avec un nombre croissant de laboratoires de recherche travaillant sur le sujet. Depuis le projet Brain Computer Interface, où la BCI a été présentée à des fins de réadaptation et d'assistance, l'utilisation de la BCI a été étendue à d'autres applications telles que le neurofeedback et l’industrie du jeux vidéo. Ce progrès a été réalisé grâce à une meilleure compréhension de l'électroencéphalographie (EEG), une amélioration des systèmes d’enregistrement du EEG, et une augmentation de puissance de calcul.Malgré son potentiel, la technologie de la BCI n’est pas encore mature et ne peut être utilisé en dehors des laboratoires. Il y a un tas de défis qui doivent être surmontés avant que les systèmes BCI puissent être utilisés à leur plein potentiel. Ce travail porte sur des aspects importants de ces défis, à savoir la spécificité des systèmes BCI aux capacités physiques des utilisateurs, la robustesse de la représentation et de l'apprentissage du EEG, ainsi que la suffisance des données d’entrainement. L'objectif est de fournir un système BCI qui peut s’adapter aux utilisateurs en fonction de leurs capacités physiques et des variabilités dans les signaux du cerveau enregistrés.À ces fins, deux voies principales sont explorées : la première, qui peut être considérée comme un ajustement de haut niveau, est un changement de paradigmes BCI. Elle porte sur la création de nouveaux paradigmes qui peuvent augmenter les performances de la BCI, alléger l'inconfort de l'utilisation de ces systèmes, et s’adapter aux besoins des utilisateurs. La deuxième voie, considérée comme une solution de bas niveau, porte sur l’amélioration des techniques de traitement du signal et d’apprentissage statistique pour améliorer la qualité du signal EEG, la reconnaissance des formes, ainsi que la tache de classification.D'une part, une nouvelle méthodologie dans le contexte de la robotique d'assistance est définie : il s’agit d’une approche hybride où une interface physique est complémentée par une interface cérébrale pour une interaction homme-machine plus fluide. Ce système hybride utilise les capacités motrices résiduelles des utilisateurs et offre la BCI comme un choix optionnel : l'utilisateur choisit quand utiliser la BCI et peut alterner entre les interfaces cérébrales et musculaire selon le besoin.D'autre part, pour l’amélioration des techniques de traitement du signal et d'apprentissage statistique, ce travail utilise un cadre Riemannien. Un frein majeur dans le domaine de la BCI est la faible résolution spatiale du EEG. Ce problème est dû à l'effet de conductance des os du crâne qui agissent comme un filtre passe-bas non linéaire, en mélangeant les signaux de différentes sources du cerveau et réduisant ainsi le rapport signal-à-bruit. Par conséquent, les méthodes de filtrage spatial ont été développées ou adaptées. La plupart d'entre elles – à savoir la Common Spatial Pattern (CSP), la xDAWN et la Canonical Correlation Analysis (CCA) – sont basées sur des estimations de matrice de covariance. Les matrices de covariance sont essentielles dans la représentation d’information contenue dans le signal EEG et constituent un élément important dans leur classification. Dans la plupart des algorithmes d'apprentissage statistique existants, les matrices de covariance sont traitées comme des éléments de l'espace euclidien. Cependant, étant symétrique et défini positive (SDP), les matrices de covariance sont situées dans un espace courbe qui est identifié comme une variété riemannienne. Utiliser les matrices de covariance comme caractéristique pour la classification des signaux EEG, et les manipuler avec les outils fournis par la géométrie de Riemann, fournit un cadre solide pour la représentation et l'apprentissage du EEG.

In the last two decades, interest in Brain-Computer Interfaces (BCI) has tremendously grown, with a number of research laboratories working on the topic. Since the Brain-Computer Interface Project of Vidal in 1973, where BCI was introduced for rehabilitative and assistive purposes, the use of BCI has been extended to more applications such as neurofeedback and entertainment. The credit of this progress should be granted to an improved understanding of electroencephalography (EEG), an improvement in its measurement techniques, and increased computational power.Despite the opportunities and potential of Brain-Computer Interface, the technology has yet to reach maturity and be used out of laboratories. There are several challenges that need to be addresses before BCI systems can be used to their full potential. This work examines in depth some of these challenges, namely the specificity of BCI systems to users physical abilities, the robustness of EEG representation and machine learning, and the adequacy of training data. The aim is to provide a BCI system that can adapt to individual users in terms of their physical abilities/disabilities, and variability in recorded brain signals.To this end, two main avenues are explored: the first, which can be regarded as a high-level adjustment, is a change in BCI paradigms. It is about creating new paradigms that increase their performance, ease the discomfort of using BCI systems, and adapt to the user’s needs. The second avenue, regarded as a low-level solution, is the refinement of signal processing and machine learning techniques to enhance the EEG signal quality, pattern recognition and classification.On the one hand, a new methodology in the context of assistive robotics is defined: it is a hybrid approach where a physical interface is complemented by a Brain-Computer Interface (BCI) for human machine interaction. This hybrid system makes use of users residual motor abilities and offers BCI as an optional choice: the user can choose when to rely on BCI and could alternate between the muscular- and brain-mediated interface at the appropriate time.On the other hand, for the refinement of signal processing and machine learning techniques, this work uses a Riemannian framework. A major limitation in this filed is the EEG poor spatial resolution. This limitation is due to the volume conductance effect, as the skull bones act as a non-linear low pass filter, mixing the brain source signals and thus reducing the signal-to-noise ratio. Consequently, spatial filtering methods have been developed or adapted. Most of them (i.e. Common Spatial Pattern, xDAWN, and Canonical Correlation Analysis) are based on covariance matrix estimations. The covariance matrices are key in the representation of information contained in the EEG signal and constitute an important feature in their classification. In most of the existing machine learning algorithms, covariance matrices are treated as elements of the Euclidean space. However, being Symmetric and Positive-Definite (SPD), covariance matrices lie on a curved space that is identified as a Riemannian manifold. Using covariance matrices as features for classification of EEG signals and handling them with the tools provided by Riemannian geometry provide a robust framework for EEG representation and learning., Au cours des deux dernières décennies, l'intérêt porté aux interfaces cérébrales ou Brain Computer Interfaces (BCI) s’est considérablement accru, avec un nombre croissant de laboratoires de recherche travaillant sur le sujet. Depuis le projet Brain Computer Interface, où la BCI a été présentée à des fins de réadaptation et d'assistance, l'utilisation de la BCI a été étendue à d'autres applications telles que le neurofeedback et l’industrie du jeux vidéo. Ce progrès a été réalisé grâce à une meilleure compréhension de l'électroencéphalographie (EEG), une amélioration des systèmes d’enregistrement du EEG, et une augmentation de puissance de calcul.Malgré son potentiel, la technologie de la BCI n’est pas encore mature et ne peut être utilisé en dehors des laboratoires. Il y a un tas de défis qui doivent être surmontés avant que les systèmes BCI puissent être utilisés à leur plein potentiel. Ce travail porte sur des aspects importants de ces défis, à savoir la spécificité des systèmes BCI aux capacités physiques des utilisateurs, la robustesse de la représentation et de l'apprentissage du EEG, ainsi que la suffisance des données d’entrainement. L'objectif est de fournir un système BCI qui peut s’adapter aux utilisateurs en fonction de leurs capacités physiques et des variabilités dans les signaux du cerveau enregistrés.À ces fins, deux voies principales sont explorées : la première, qui peut être considérée comme un ajustement de haut niveau, est un changement de paradigmes BCI. Elle porte sur la création de nouveaux paradigmes qui peuvent augmenter les performances de la BCI, alléger l'inconfort de l'utilisation de ces systèmes, et s’adapter aux besoins des utilisateurs. La deuxième voie, considérée comme une solution de bas niveau, porte sur l’amélioration des techniques de traitement du signal et d’apprentissage statistique pour améliorer la qualité du signal EEG, la reconnaissance des formes, ainsi que la tache de classification.D'une part, une nouvelle méthodologie dans le contexte de la robotique d'assistance est définie : il s’agit d’une approche hybride où une interface physique est complémentée par une interface cérébrale pour une interaction homme-machine plus fluide. Ce système hybride utilise les capacités motrices résiduelles des utilisateurs et offre la BCI comme un choix optionnel : l'utilisateur choisit quand utiliser la BCI et peut alterner entre les interfaces cérébrales et musculaire selon le besoin.D'autre part, pour l’amélioration des techniques de traitement du signal et d'apprentissage statistique, ce travail utilise un cadre Riemannien. Un frein majeur dans le domaine de la BCI est la faible résolution spatiale du EEG. Ce problème est dû à l'effet de conductance des os du crâne qui agissent comme un filtre passe-bas non linéaire, en mélangeant les signaux de différentes sources du cerveau et réduisant ainsi le rapport signal-à-bruit. Par conséquent, les méthodes de filtrage spatial ont été développées ou adaptées. La plupart d'entre elles – à savoir la Common Spatial Pattern (CSP), la xDAWN et la Canonical Correlation Analysis (CCA) – sont basées sur des estimations de matrice de covariance. Les matrices de covariance sont essentielles dans la représentation d’information contenue dans le signal EEG et constituent un élément important dans leur classification. Dans la plupart des algorithmes d'apprentissage statistique existants, les matrices de covariance sont traitées comme des éléments de l'espace euclidien. Cependant, étant symétrique et défini positive (SDP), les matrices de covariance sont situées dans un espace courbe qui est identifié comme une variété riemannienne. Utiliser les matrices de covariance comme caractéristique pour la classification des signaux EEG, et les manipuler avec les outils fournis par la géométrie de Riemann, fournit un cadre solide pour la représentation et l'apprentissage du EEG.

International audience; Modern water distribution networks (WDNs) are complex and difficult to manage due to increased level of urbanization, varying consumer demands, ageing infrastructure, operational costs, and inadequate water resources. The management problems in such complex networks may be classified into short-term, medium-term, and long-term, depending on the duration at which the problems are solved or considered. To address the management problems associated with WDNs, mathematical models facilitate analysis and improvement of the performance of water infrastructure at minimum operational cost, and have been used by researchers, water utility managers, and operators. This paper presents a detailed review of the management problems and essential mathematical models that are used to address these problems at various phases of WDNs. In addition, it also discusses the main approaches to address these management problems to meet customer demands at the required pressure in terms of adequate water quantity and quality. Key challenges that are associated with the management of WDNs are discussed. Also, new directions for future research studies are suggested to enable water utility managers and researchers to improve the performance of water distribution networks.

Taking into account high oil prices and environmental awareness, the development of electric vehicles (EVs) is considered as a healthier mode of transportation. Amongst other eco-friendly vehicles, EVs are considered as a favourable solution for a greener energy because the electricity they consume can be generated from a wide range of sources which include fossil fuel, nuclear power and renewable energy. However, users and owners of EVs feel uncomfortable because EVs require sufficient electrical energy battery storage on-board to provide sufficient driving autonomy.Lumped inductive power transfer (LIPT) system is a new technology that allows the transfer of electric power between its air-cored primary and secondary coils via high frequency magnetic fields to a consuming device. Unlike the conventional plug-in system, LIPT system is capable of providing a safe, efficient and convenient overnight recharging of EVs. However, its main limiting factor is the poor performance of its magnetic coupling structure (MCS), which is intended to transfer power efficiently. Thus the problem statement of this thesis is to improve the performance of MCS models for LIPT systems.Firstly, based on a more efficient and relevant mathematical model available in the literature, MATLAB code is implemented to compute the mutual inductance between air-cored filamentary circular (FC) coils. Also, the computation and experimental validation of the magnetic fields between two FC coils are presented.Furthermore, computational models of an IPT system for EV battery charge are presented in this thesis. Based on the technical specifications of Renault ZOE, the results obtained show that by supplying a higher frequency AC voltage to the primary coil of the MCS and compensating the primary and secondary sides of the air-cored coils with series-series capacitors, the 3 kW single-phase and 22 kW three-phase IPT systems modelled using MATLAB/Simulink are capable of delivering the electricity needed to power the Renault ZOE.Finally, in order to recommend a suitable and cost-efficient MCS model that can help transfer electric power more efficiently for the battery charging of EVs and E-bikes, a 3-D finite element analysis (FEA) package called COMSOL multiphysics is used to design, compute and investigate a more complex and realistic MCS model of LIPT systems. The designed MCS models incorporate air-cored coils with proper configuration of magnetic cores (e.g. ferrite), structural steel covering for the bottom part of the primary coil and top part of the secondary coil and lastly, iron plate which serves as a covering for the primary coil installed underground and the chassis or underbody structure of EVs. The performance of the designed models are determined by the values of the mutual inductance and induced voltage obtained from COMSOL., Compte tenu du contexte économique du marché des hydrocarbures et les problématiques environnementales, le développement des véhicules électriques (VE) prend de l’ampleur car ils sont considérés comme plus écologiques. Aujourd’hui, les véhicules électriques sont considérés comme une solution favorable pour une énergie plus verte. L'électricité qu'ils consomment peut être générée à partir d'un large éventail de sources qui comprennent les combustibles fossiles, l'énergie nucléaire et les énergies renouvelables. Toutefois, les utilisateurs et les propriétaires de véhicules électriques ont encore des réticences car cela nécessite un stockage d'énergie électrique à bord pour assurer une bonne autonomie.Le système de transfert de puissance par effet inductif (LIPT en anglais) est une nouvelle technologie qui permet le transfert d'énergie électrique par champ magnétique et un système de bobines primaires et secondaires. Le champ magnétique est un champ haute-fréquence à plusieurs dizaines de kilohertz. Par rapport au système de câble conventionnel, le système LIPT est capable de fournir une recharge qui est pratique mais également efficace des véhicules électriques. Cependant, actuellement son principal facteur limitant est la mauvaise performance de sa structure de couplage magnétique (MCS). L’objectif de cette thèse est d'améliorer la performance des systèmes MCS pour les systèmes de LIPT afin de concevoir des systèmes à meilleur rendement.Dans un premier temps, sur la base de modèles mathématiques issus de la littérature, un code Matlab a été mis en œuvre pour calculer l'inductance mutuelle des systèmes de bobines mise en jeu dans le MCS. Puis, le calcul et la validation expérimentale des champs magnétiques entre le primaire et le secondaire a été effectué.Dans un second temps, un modèle d'un système LIPT pour la charge d’une batterie de véhicule électrique est présenté. Sur la base des spécifications techniques d’une Renault ZOE, les résultats obtenus montrent que, en adaptant la fréquence de la bobine primaire et en compensant avec un système série-série de condensateurs, un système à 3 kW et un système à 22 kW peuvent atteindre des performances permettant la recharge d’une Renault Zoe dans de bonnes conditions.Enfin, une analyse par éléments finis (FEA) sous COMSOL est développée pour la conception, le calcul et l’optimisation de systèmes MCS plus complexes de nouveaux LIPT. Les modèles de MCS conçus intègrent des bobines d'air évidées avec des configurations appropriées de noyaux magnétiques (par exemple en ferrite), avec des études également sur des parties couvrantes des bobines primaires et secondaires en acier. Les performances des modèles conçus sont déterminées par les valeurs de l'inductance mutuelle et la tension induite qui sont deux critères d’évaluations.

Multipath is among the major sources of errors in precise positioning using GPS and continues to be extensively studied. Two Fast Fourier Transform (FFT)-based detectors are presented in this paper as GPS multipath detection techniques. The detectors are formulated as binary hypothesis tests under the assumption that the multipath exists for a sufficient time frame that allows its detection based on the quadrature arm of the coherent Early-minus-Late discriminator (Q EmL) for a scalar tracking loop (STL) or on the quadrature (Q EmL) and/or in-phase arm (I EmL) for a vector tracking loop (VTL), using an observation window of N samples. Performance analysis of the suggested detectors is done on multipath signal data acquired from the multipath environment simulator developed by the German Aerospace Centre (DLR) as well as on multipath data from real GPS signals. Application of the detection tests to correlator outputs of scalar and vector tracking loops shows that they may be used to exclude multipath contaminated satellites from the navigation solution. These detection techniques can be extended to other Global Navigation Satellite Systems (GNSS) such as GLONASS, Galileo and Beidou., Comment: 2016 European Navigation Conference (ENC 2016), May 2016, Helsinki, Finland. Proceedings of the 2016 European Navigation Conference (ENC 2016)