Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance

Plastics are persistent synthetic polymers that accumulate as waste in the marine environment. Microplastic (MP) particles are derived from the breakdown of larger debris or can enter the environment as microscopic fragments. Because filter-feeder organisms ingest MP while feeding, they are likely to be impacted by MP pollution. To assess the impact of polystyrene microspheres (micro-PS) on the physiology of the Pacific oyster, adult oysters were experimentally exposed to virgin micro-PS (2 and 6 µm in diameter; 0.023 mg·L(-1)) for 2 mo during a reproductive cycle. Effects were investigated on ecophysiological parameters; cellular, transcriptomic, and proteomic responses; fecundity; and offspring development. Oysters preferentially ingested the 6-µm micro-PS over the 2-µm-diameter particles. Consumption of microalgae and absorption efficiency were significantly higher in exposed oysters, suggesting compensatory and physical effects on both digestive parameters. After 2 mo, exposed oysters had significant decreases in oocyte number (-38%), diameter (-5%), and sperm velocity (-23%). The D-larval yield and larval development of offspring derived from exposed parents decreased by 41% and 18%, respectively, compared with control offspring. Dynamic energy budget modeling, supported by transcriptomic profiles, suggested a significant shift of energy allocation from reproduction to structural growth, and elevated maintenance costs in exposed oysters, which is thought to be caused by interference with energy uptake. Molecular signatures of endocrine disruption were also revealed, but no endocrine disruptors were found in the biological samples. This study provides evidence that micro-PS cause feeding modifications and reproductive disruption in oysters, with significant impacts on offspring.

Despite the perception people may have regarding the agricultural process, the reality is that today's agriculture industry is data-centered, precise, and smarter than ever. The rapid emergence of the Internet-of-Things (IoT) based technologies redesigned almost every industry including “smart agriculture” which moved the industry from statistical to quantitative approaches. Such revolutionary changes are shaking the existing agriculture methods and creating new opportunities along a range of challenges. This article highlights the potential of wireless sensors and IoT in agriculture, as well as the challenges expected to be faced when integrating this technology with the traditional farming practices. IoT devices and communication techniques associated with wireless sensors encountered in agriculture applications are analyzed in detail. What sensors are available for specific agriculture application, like soil preparation, crop status, irrigation, insect and pest detection are listed. How this technology helping the growers throughout the crop stages, from sowing until harvesting, packing and transportation is explained. Furthermore, the use of unmanned aerial vehicles for crop surveillance and other favorable applications such as optimizing crop yield is considered in this article. State-of-the-art IoT-based architectures and platforms used in agriculture are also highlighted wherever suitable. Finally, based on this thorough review, we identify current and future trends of IoT in agriculture and highlight potential research challenges.

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BACKGROUND: Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. OBJECTIVE: The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice. METHODS: This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. RESULTS: A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. CONCLUSIONS: Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

PURPOSE: To evaluate the potential of intrastromal corneal ring technology (Intacs, KeraVision) to correct keratoconus without central corneal scarring. SETTING: Department of Ophthalmology, Brest University Hospital, Brest, France. METHODS: In this prospective, noncomparative, interventional case series, Intacs segments were implanted in 10 keratoconic eyes with clear central corneas and contact lens intolerance after corneal pachymetry was checked. Segment thicknesses varied based on corneal topography analysis. RESULTS: No intraoperative complications occurred. The mean follow-up was 10.6 months. Postoperative results revealed a reduction in astigmatism and spherical correction and an increase in topographical regularity and increased uncorrected visual acuity. CONCLUSION: Intacs technology can reduce the corneal steepening and astigmatism associated with keratoconus.

Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We describe a series of comparisons that support our views: For instance, we show that a supposedly "under-performing" method (i.e., giving baseline average results), if applied at the "appropriate" time in the data-processing pipeline (before or after peptide aggregation) on a data set with the "appropriate" nature of missing values, can outperform a blindly applied, supposedly "better-performing" method (i.e., the reference method from the state-of-the-art). This leads us to formulate few practical guidelines regarding the choice and the application of an imputation method in a proteomics context.

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High-level synthesis raises the design abstraction level and allows rapid generation of optimized RTL hardware for performance, area, and power requirements. This article gives an overview of state-of-the-art HLS techniques and tools.

Information security and authentication are important challenges facing society. Recent attacks by hackers on the databases of large commercial and financial companies have demonstrated that more research and development of advanced approaches are necessary to deny unauthorized access to critical data. Free space optical technology has been investigated by many researchers in information security, encryption, and authentication. The main motivation for using optics and photonics for information security is that optical waveforms possess many complex degrees of freedom such as amplitude, phase, polarization, large bandwidth, nonlinear transformations, quantum properties of photons, and multiplexing that can be combined in many ways to make information encryption more secure and more difficult to attack. This roadmap article presents an overview of the potential, recent advances, and challenges of optical security and encryption using free space optics. The roadmap on optical security is comprised of six categories that together include 16 short sections written by authors who have made relevant contributions in this field. The first category of this roadmap describes novel encryption approaches, including secure optical sensing which summarizes double random phase encryption applications and flaws [Yamaguchi], the digital holographic encryption in free space optical technique which describes encryption using multidimensional digital holography [Nomura], simultaneous encryption of multiple signals [Prez-Cabr], asymmetric methods based on information truncation [Nishchal], and dynamic encryption of video sequences [Torroba]. Asymmetric and one-way cryptosystems are analyzed by Peng. The second category is on compression for encryption. In their respective contributions, Alfalou and Stern propose similar goals involving compressed data and compressive sensing encryption. The very important area of cryptanalysis is the topic of the third category with two sections: Sheridan reviews phase retrieval algorithms to perform different attacks, whereas Situ discusses nonlinear optical encryption techniques and the development of a rigorous optical information security theory. The fourth category with two contributions reports how encryption could be implemented at the nano-or micro-scale. Naruse discusses the use of nanostructures in security applications and Carnicer proposes encoding information in a tightly focused beam. In the fifth category, encryption based on ghost imaging using single-pixel detectors is also considered. In particular, the authors [Chen, Tajahuerce] emphasize the need for more specialized hardware and image processing algorithms. Finally, in the sixth category, Mosk and Javidi analyze in their corresponding papers how quantum imaging can benefit optical encryption systems. Sources that use few photons make encryption systems much more difficult to attack, providing a secure method for authentication. S Online supplementary data available from stacks.iop.org/JOPT/18/083001/mmedia (Some figures may appear in colour only in the online journal) Contents I. Encryption technologies 1. Secure optical sensing 4 2. Digital holographic encryption in free space optical technique 6 3. Simultaneous encryption and authentication of multiple signals 8 4. Amplitude-and phase-truncation based optical asymmetric cryptosystem 10 5. Optical security: dynamical processes and noise-free recovery 12

The Okinawa Trough, lying to the east of China, is a back arc basin formed by extension within continental lithosphere behind the Ryukyu trench‐arc system. Middle to late Miocene uplift, associated with normal faulting of the initially adjacent Ryukyu nonvolcanic arc and the Taiwan‐Sinzi folded belt, corresponds to the first rifting phase. The timing of rifting is supported by the presence of marine sediments of corresponding age drilled in the northern Okinawa Trough. The rifting occurred after a major early Miocene change in the motion of the Philippine plate with respect to Eurasia and ceased during the Pliocene. A second rifting phase started about 2 m.y. ago, at the Plio‐Pleistocene boundary and has continued until the present time. It has proceeded to a more advanced stage in the middle and southern Okinawa Trough than it has farther north. Detailed bathymetric (Sea Beam), seismic reflection, and magnetics data collected during the POP 1 cruise of the R/V Jean Charcot reveal the principal features of the extensional processes. The back arc spreading phase started very recently in the southern and middle Okinawa Trough, as exemplified by several en échelon and, in some cases, overlapping active, central graben oriented N70°E–N80°E. Some of these depressions are intruded by volcanic ridges of fresh back arc basalt with associated large magnetic anomalies. Transform faults between these en échelon active rifts are not obvious. We suggest that the major part of the southern Okinawa Trough is underlain by a thinned continental crust and that except for the system of en échelon rifts of the southern Okinawa Trough, the back arc basin oceanic domain is limited to a width of a few tens of kilometers or less in the axial portion of the trough. The system of axial back arc volcanic ridges that occur in the rifts ends at the latitude of Okinawa Island whereas active volcanoes in the Ryukyu arc occur only north of Okinawa Island. We refer to this transition between active arc and back arc volcanism as the volcanic arc‐rift migration phenomenon (VAMP). Globally, back arc volcanism propagated from the southern Okinawa Trough to the Okinawa VAMP area. Rifting continues to occur in the northern Okinawa Trough but is not yet accompanied by associated volcanism. The Okinawa VAMP area is characterized by a series of parallel basaltic ridges oriented N75°E with associated linear magnetic anomalies characteristic of dyke intrusions. We suggest that the formation of the back arc oceanic domain took place along the axial back arc extensional zone trending N75°E and that this zone presently ends at the southern extremity of the active volcanic chain. The initial phase of formation of back arc basin oceanic crust is non‐steady state and is characterized by the lack of a developed fracture zone pattern. The termination of the VAMP area in the direction of the volcanic zone of the arc is consistent with the suggestion of Molnar and Atwater that the volcanic arc is a fundamental line of weakness which determines where initial back arc spreading occurs. Apparently, back arc extension initially occurred within the continental lithosphere located westward of the Ryukyu arc, along its whole length, but the subsequent back arc volcanism was initiated in the southernmost portion of the region and then moved northward. This migration was accompanied by the shutting down of volcanic activity along the abandoned portions of the arc. It is this transfer of volcanism that we call the VAMP process. Thus arc and back arc basin volcanism seem to be associated in such a manner that spreading tends to migrate simultaneously with a cessation in volcanic activity along the arc. This interplay of arc and back arc activity is probably linked to changes in the parameters of plate convergence. Since the plate motion in the Phillippine sea is oblique to the trench at least in the southern part of the Okinawa Trough, we suggest that the oblique resisting force applied to the edge of the overriding plate engenders the development of en échelon extensional features within and behind the arc. The motion of the Ryukyu platelet with respect to Eurasia is consequently an extensional strike‐slip motion trending roughly parallel to the Okinawa Trough.

In recent years, there has been a growing research interest in integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. This integration aims to lead meta-heuristics toward an efficient, effective, and robust search and improve their performance in terms of solution quality, convergence rate, and robustness. Since various integration methods with different purposes have been developed, there is a need to review the recent advances in using machine learning techniques to improve meta-heuristics. To the best of our knowledge, the literature is deprived of having a comprehensive yet technical review. To fill this gap, this paper provides such a review on the use of machine learning techniques in the design of different elements of meta-heuristics for different purposes including algorithm selection, fitness evaluation, initialization, evolution, parameter setting, and cooperation. First, we describe the key concepts and preliminaries of each of these ways of integration. Then, the recent advances in each way of integration are reviewed and classified based on a proposed unified taxonomy. Finally, we provide a technical discussion on the advantages, limitations, requirements, and challenges of implementing each of these integration ways, followed by promising future research directions.

This paper deals with the power generation control in variable-speed wind turbines. These systems have two operation regions which depend on wind turbine tip speed ratio. A high-order sliding-mode control strategy is then proposed to ensure stability in both operation regions and to impose the ideal feedback control solution in spite of model uncertainties. This control strategy presents attractive features such as robustness to parametric uncertainties of the turbine. The proposed sliding-mode control approach has been validated on a 1.5-MW three-blade wind turbine using the national renewable energy laboratory wind turbine simulator FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. Validation results show that the proposed control strategy is effective in terms of power regulation. Moreover, the sliding-mode approach is arranged so as to produce no chattering in the generated torque that could lead to increased mechanical stress because of strong torque variations.

The aim of this paper is to show the possibility to harvest RF energy to supply wireless sensor networks in an outdoor environment. In those conditions, the number of existing RF bands is unpredictable. The RF circuit has to harvest all the potential RF energy present and cannot be designed for a single RF tone. In this paper, the designed RF harvester adds powers coming from an unlimited number of sub-frequency bands. The harvester's output voltage ratios increase with the number of RF bands. As an application example, a 4-RF band rectenna is designed. The system harvests energy from GSM900 (Global System for Mobile Communications), GSM1800, UMTS (Universal Mobile Telecommunications System) and WiFi bands simultaneously. RF-to-dc conversion efficiency is measured at 62% for a cumulative -10-dBm input power homogeneously widespread over the four RF bands and reaches 84% at 5.8 dBm. The relative error between the measured dc output power with all four RF bands on and the ideal sum of each of the four RF bands power contribution is less than 3%. It is shown that the RF-to-dc conversion efficiency is more than doubled compared to that measured with a single RF source, thanks to the proposed rectifier architecture.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

Abstract. The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.

LoRa™ is low power wide area wireless network (LPWAN) protocol for Internet of Things (IoTs) applications. LPWAN has been enabling technology of large scale wireless sensor networks (WSNs). Effective cost, long range and energy efficiency of LPWANs make them most suitable candidates for smart city applications. These technologies offer novel communication paradigm to address discrete IoT's applications. LoRa is a recently proposed LPWAN technology based on spread spectrum technique with a wider band. LoRa uses the entire channel bandwidth to broadcast a signal which makes it resistant to channel noise, long term relative frequency, doppler effects and fading. This paper focuses on the emerging transmission technologies dedicated to IoT networks. Characteristics of LoRa are based on three basic parameters: Code Rate (CR), Spreading Factor (SF) and Bandwidth (BW). This paper provides in depth analysis of the impact of these three parameters on the data rate and time on air.

Abstract The present assessment of the West African monsoon in the models of the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) indicates little evolution since the third phase of CMIP (CMIP3) in terms of both biases in present-day climate and climate projections. The outlook for precipitation in twenty-first-century coupled simulations exhibits opposite responses between the westernmost and eastern Sahel. The spread in the trend amplitude, however, remains large in both regions. Besides, although all models predict a spring and summer warming of the Sahel that is 10%–50% larger than the global warming, their temperature response ranges from 0 to 7 K. CMIP5 coupled models underestimate the monsoon decadal variability, but SST-imposed simulations succeed in capturing the recent partial recovery of monsoon rainfall. Coupled models still display major SST biases in the equatorial Atlantic, inducing a systematic southward shift of the monsoon. Because of these strong biases, the monsoon is further evaluated in SST-imposed simulations along the 10°W–10°E African Monsoon Multidisciplinary Analysis (AMMA) transect, across a range of time scales ranging from seasonal to intraseasonal and diurnal fluctuations. The comprehensive set of observational data now available allows an in-depth evaluation of the monsoon across those scales, especially through the use of high-frequency outputs provided by some CMIP5 models at selected sites along the AMMA transect. Most models capture many features of the African monsoon with varying degrees of accuracy. In particular, the simulation of the top-of-atmosphere and surface energy balances, in relation with the cloud cover, and the intermittence and diurnal cycle of precipitation demand further work to achieve a reasonable realism.

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surro-gate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed.

Abstract This paper presents a study of the St Patrick's Day storm of 2015, with its ionospheric response at middle and low latitudes. The effects of the storm in each longitudinal sector (Asian, African, American, and Pacific) are characterized using global and regional electron content. At the beginning of the storm, one or two ionospheric positive storm effects are observed depending on the longitudinal zones. After the main phase of the storm, a strong decrease in ionization is observed at all longitudes, lasting several days. The American region exhibits the most remarkable increase in vertical total electron content (vTEC), while in the Asian sector, the largest decrease in vTEC is observed. At low latitudes, using spectral analysis, we were able to separate the effects of the prompt penetration of the magnetospheric convection electric field (PPEF) and of the disturbance dynamo electric field (DDEF) on the basis of ground magnetic data. Concerning the PPEF, Earth's magnetic field oscillations occur simultaneously in the Asian, African, and American sectors, during southward magnetization of the B z component of the interplanetary magnetic field. Concerning the DDEF, diurnal magnetic oscillations in the horizontal component H of the Earth's magnetic field exhibit a behavior that is opposed to the regular one. These diurnal oscillations are recognized to last several days in all longitudinal sectors. The observational data obtained by all sensors used in the present paper can be interpreted on the basis of existing theoretical models.

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