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Study regions: The study area encompasses two distinct sub-basins within the High Atlas Mountains: Oukaimeden in the Rheraya and Tichki in the Mgoun Valley. Study focus: The research integrates remote sensing data, particularly the Normalized-Difference Snow Index (NDSI) from the MODIS Sensor, with machine learning (ML) and deep learning (DL) models to predict daily snow depth (DSD) at a local scale. The models evaluated include two ML approaches: Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost) and four DL models: 1-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), and Bi-directional Long Short-Term Memory Network (Bi-LSTM). The dataset was processed and normalized for optimal performance, and hyperparameters were fine-tuned using a randomized search method. New hydrological insights for the region: The Results highlight the efficacy of AI-based approaches for snow depth prediction, with SVR achieving the best performance (Root Mean Square Error of 2–5 cm and an average coefficient of determination of 0.97). This study reveals that incorporating lag times of snow depth data significantly enhances predictive accuracy. These findings underscore the potential of integrating remote sensing with AI techniques to improve hydrological modeling and water resource planning in data-scarce regions like the Atlas Mountains.

In semi-arid Mediterranean areas, a significant proportion of the population living downstream depends on water resources from snowmelt and precipitation as their main source of water. Consequently, snow-covered mountain regions can be considered as a vital water tower, providing a steady supply of water, and contributing significantly to streamflow and groundwater recharge. Given the scarcity of ground-based hydroclimatic measurements, remote sensing could be an effective technique for mapping and monitoring snow cover. This study evaluates the last version of MODIS (version 6, called V6) snow cover product, optimizing the NDSI threshold for accurate snow cover mapping and developing models for local fractional snow cover estimation in the southern Mediterranean region, particularly in the Moroccan Atlas Mountains. For this purpose, 448 Sentinel-2 (S2) scenes from six different regions across the Atlas Range were used to adjust the NDSI threshold and to develop FSC estimation models. In addition, a total of 8419 MOD10A1 images from March 2000 to June 2023, and 7561 MYD10A1 images from September 2002 to June 2023, were processed to improve cloud filtering and to develop a highly accurate daily snow cover product suitable for the Moroccan Atlas Mountains. The cloud correction approach significantly reduced the number of cloud-covered pixels, from 25.7% to 0.4% after filtering. Two schemes for selecting the MODIS NDSI threshold were tested: (1) the global reference of 0.4 and (2) the locally optimal threshold of 0.2. The average snow cover estimation errors using the optimal and global NDSI thresholds for Terra are an average overestimation of 0.34% and a significant underestimation of 6.13%, respectively. For Aqua, the corresponding errors are an overestimation of 1.4% and an underestimation of 6.8%. Thus, the optimal NDSI threshold of 0.2 could be more appropriate than the threshold of 0.4 for use in the southern Mediterranean region. The new FSC estimation models developed showed satisfactory performance with significant correlation coefficients (mean of 0.85 for Terra and 0.83 for Aqua), and with low RMSE and MAE values (mean of 0.17 and 0.12 for Terra and mean of 0.19 and 0.14 for Aqua) when comparing FSC derived from high-resolution S2 data with predicted FSC from MODIS NDSI. The daily snow cover product developed was compared with the high-resolution snow maps obtained from S2 satellite imagery in different regions of the Moroccan Atlas. On average, the product showed a mean correlation coefficient of 0.96, a mean absolute error of 0.22%, and a mean reasonable negative bias of −0.17%. This research concludes that the enhanced daily snow cover product could improve the understanding of spatiotemporal dynamics of snow extent and, therefore, contribute to quantifying the snowmelt contribution to the water budget through modeling approaches in the southern Mediterranean region.

Accurate and efficient streamflow simulations are necessary for sustainable water management and conservation in arid and semi-arid contexts. Conceptual hydrological models often underperform in these catchments due to the high climatic variability and data scarcity, leading to unstable parameters and biased results. This study evaluates the stability of the HBV model across seven sub-catchments of the Oum Er Rabia river basin (OERB), focusing on the HBV model regionalization process and the effectiveness of Earth Observation data in enhancing predictive capability. Therefore, we developed a nested cross-validation framework for spatiotemporal stability assessment, using optimal parameters from a donor-single-site calibration (DSSC) to inform target-multi-site calibration (TMSC). The results show that the HBV model remains spatially transferable from one basin to another with moderate to high performances (KGE (0.1~0.9 NSE (0.5~0.8)). Furthermore, calibration using KGE improves model stability over NSE. Some parameter sets exhibit spatial instability, but inter-annual parameter behavior remains stable, indicating potential climate change impacts. Model performance declines over time (18–124%) with increasing dryness. As a conclusion, this study presents a framework for analyzing parameter stability in hydrological models and highlights the need for more research on spatial and temporal factors affecting hydrological response variability.

Despite the tax reform in the 1980s, the Moroccan tax system still suffers from structural vulnerabilities that have led to inefficient tax policies and increasing sectoral disparities, which have led to an increase in the tax pressure rates between 1990 and 2020. To overcome these vulnerabilities, and according to the recommendations of the 2019 National Tax Conference, the Moroccan tax system has been recently restructured, with the aim of strengthening the principles of equity and tax efficiency. The main objective of this paper is to determine the impact of the tax pressure on long-term economic growth in Morocco from 1990 to 2020. To do so, the methodology adopted in this work consists of verifying the effect of the tax pressure on economic growth through a quantitative methodology based on two vector autoregression approaches: the vector autoregression model (VAR) and the vector error correction model (VECM). The results of this study confirm that the effect of fiscal pressure on economic growth in Morocco is not significant in the long run, and therefore, we can deduce that taxation is not yet a well-mastered instrument for the state to act on the economic sphere. The results show a complex relationship between tax pressures and economic growth in Morocco, underlining the importance of tax reform. However, the research is limited by the specific models and the need to explore other determinants. The contribution of this paper lies in its in-depth empirical analysis of fiscal pressure and its influence on long-term economic growth in Morocco, as based on VAR and VECM modeling. This econometric approach makes it possible to isolate the dynamic effects and responses of economic variables over time, offering an understanding of the interactions between tax policy and long-term economic growth. By focusing specifically on the Moroccan context, this study offers an original perspective and helps to fill the gap in the empirical research in this area while providing a valuable analytical framework for assessing fiscal policies and their impact on long-term economic growth.

Seasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco’s Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016–2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change.

Juvenile myelomonocytic leukemia (JMML) is a rare, generally aggressive myeloproliferative neoplasm affecting young children. It is characterized by granulomonocytic expansion, with monocytosis infiltrating peripheral tissues. JMML is initiated by mutations upregulating RAS signaling. Approximately 10% of cases remain without an identified driver event. Exome sequencing of 2 unrelated cases of familial JMML of unknown genetics and analysis of the French JMML cohort identified 11 patients with variants in SH2B3, encoding LNK, a negative regulator of the JAK-STAT pathway. All variants were absent from healthy population databases, and mutation spectrum was consistent with a loss of function of the LNK protein. A stoploss variant was shown to affect both protein synthesis and stability. The other variants were either truncating or missense, the latter affecting the SH2 domain that interacts with activated JAK. Of the 11 patients, 8 from 5 families inherited pathogenic bi-allelic SH2B3 germline variants from their unaffected heterozygous parents. These children represent half of the cases with no identified causal mutation in the French cohort. They displayed typical clinical and hematological JMML features with neonatal onset and marked thrombocytopenia. They were characterized by absence of additional genetic alterations and a hypomethylated DNA profile with fetal characteristics. All patients showed partial or complete spontaneous clinical resolution. However, progression to thrombocythemia and immunity-related pathologies may be of concern later in life. Bi-allelic SH2B3 germline mutations thus define a new condition predisposing to a JMML-like disorder, suggesting that the JAK pathway deregulation is capable of initiating JMML, and opening new therapeutic options.

Context and background Wheat is one of the oldest cultivated plants in the world and has always been one of the most important staples for millions of people around the world and especially in North Africa, where wheat is the most used crop for typical food industry. Thus, an operational crop production system is needed to help decision makers make early estimates of potential food availability Yield estimation using remote sensing data has been widely studied, but such information is generally scarce in arid and semi-arid regions such as North Africa, where interannual variations in climatic factors, and spatial variability in particular, are major risks to food security. Goal and Objectives: The aim of this study is to develop a model to estimate wheat yield based on phenological metrics derived from SENTINEL-2 NDVI images in order to generalize a spatial model to estimate wheat yields in Morocco's semi-arid conditions Methodology: The 10 m NDVI time series was integrated into TIMESAT software to extract wheat phenology-related metrics during the 2018-2019 agricultural season, the period in which ground truth data was collected. Through the multiple stepwise regression method, all phenological metrics were used to predict wheat yield. Moreover, the accuracy and stability of produced models were evaluated using a K-fold cross-validation (K-fold CV) method. Results: The results of the obtained models indicated a good linear correlation between predicted yield and field observations (R2 = 0.75 and RMSE of 7.08q/ha). The obtained method could be a good tool for decision makers to orient their actions under different climatic conditions

The planning and management of groundwater in the absence of in situ climate data is a delicate task, particularly in arid regions where this resource is crucial for drinking water supplies and irrigation. Here the motivation is to evaluate the role of remote sensing data and Input feature selection method in the Long Short Term Memory (LSTM) neural network for predicting groundwater levels of five wells located in different hydrogeological contexts across the Oum Er-Rbia Basin (OER) in Morocco: irrigated plain, floodplain and low plateau area. As input descriptive variable, four remote sensing variables were used: the Integrated Multi-satellite Retrievals (IMERGE) Global Precipitation Measurement (GPM) precipitation, Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), MODIS land surface temperature (LST), and MODIS evapotranspiration. Three LSTM models were developed, rigorously analyzed and compared. The LSTM-XGB-GS model, was optimized using the GridsearchCV method, and uses a single remote sensing variable identified by the input feature selection method XGBoost. Another optimized LSTM model was also constructed, but uses the four remote sensing variables as input (LSTM-GS). Additionally, a standalone LSTM model was established and also incorporating the four variables as inputs. Scatter plots, violin plots, Taylor diagram and three evaluation indices were used to verify the performance of the three models. The overall result showed that the LSTM-XGB-GS model was the most successful, consistently outperforming both the LSTM-GS model and the standalone LSTM model. Its remarkable accuracy is reflected in high R2 values (0.95 to 0.99 during training, 0.72 to 0.99 during testing) and the lowest RMSE values (0.03 to 0.68 m during training, 0.02 to 0.58 m during testing) and MAE values (0.02 to 0.66 m during training, 0.02 to 0.58 m during testing). The LSTM-XGB-GS model reveals how hydrodynamics, climate, and land-use influence groundwater predictions, emphasizing correlations like irrigated land-temperature link and floodplain-NDVI-evapotranspiration interaction for improved predictions. Finally, this study demonstrates the great support that remote sensing data can provide for groundwater prediction using ANN models in conditions where in situ data are lacking.

Context and background: Wheat is one of the oldest crops in the world and has always been one of the most important staple foods for millions of people around the world, especially in North Africa, where wheat is the most dominant crop. The importance of wheat yield estimation is well known in agricultural management and policy making at regional and national levels. In semi-arid areas such as the case of Morocco, an operational cereal yield estimating system that could assist decision makers in planning annual imports is needed. In some developed countries, several effective tools are now available to monitor crops and optimize farm-level decisions by combining crop simulation models with seasonal forecasts. However, few tools are used to effectively manage crops at the farm level to cope with climate variability and risk. Goal and objectives: The following article presents an overview of current methods used for wheat yield estimation in the world and in Morocco Methodology: Various sections describing traditional methods, simulation models, and remote sensing. Then a section is devoted to the estimation methods used in Morocco and their efficiencies. Results: This article is very useful for researchers working on this subject because it brings together all the methods of estimating wheat yields worldwide and classifies them into categories and then situates Morocco, which is a relevant example of a North African country that is a leader in the use of spatial techniques and in the monitoring of crops, and wheat in particular.

In this paper, we address the problem of scheduling jobs on identical machines for minimising the maximum completion time (makespan). Each job requires a sequence-independent setup time, which represents the time needed to prepare the machines for job execution. Then, we introduce a dynamic programme to solve the case with two machines, and show that this problem admits a fully polynomial time approximation scheme. For the case of m machines, we propose heuristics and an adapted genetic algorithm. Some numerical experiments are done to evaluate the proposed algorithms. [ABSTRACT FROM AUTHOR]

Anti-TNF-alpha treatments are increasingly widely prescribed. Multiple side events have been reported in recent years, in particular peripheral neuropathy. We report a case of sensory neuropathy occurring six months after the start of treatment with Infliximab. This is a 43-year-old female patient, followed for APS since 2016, who required treatment with Infliximab. Six months later, the patient presented a picture of sensitive neuropathy. The etiological workup remained negative and the discontinuation of Infliximab was accompanied by an improvement in symptomatology. The delay between the initiation of Infliximab-based treatment and the onset of clinical manifestations, as well as the improvement after dose reduction, argue for the responsibility of Infliximab in the occurrence of the sensory neuropathy. [ABSTRACT FROM AUTHOR]

As a semi-arid/arid country located in the northwest of Africa, Morocco is facing serious water scarcity driven by the dual stresses of decreasing availability of water resources and increasing water demands. Virtual water trade could be an effective tool to alleviate water scarcity. The paper presents an analysis of the relationships between agrarian productions, foreign trade, and the water sector in Morocco by deriving a comprehensive estimate of virtual water export and import in Morocco’s foreign trade of 40 crop products during the period from 2000 to 2017. Our objectives include determining the intensity of water consumption of exported and imported crop products and quantifying the water consumed and saved, respectively, by locally producing and importing these products. To this end, FAO’s Penman-Monteith climate model was used to estimate crop water requirements based on data on meteorological factors. The results show that Morocco was a net virtual water importer during the study period. The deficit was 595.74 Gm3. The tendency of total virtual water export was on a rising trend, while the total virtual water import was on a downward trend. The main exported virtual water was from vegetables (68.87 Gm3, 72.47%) and the main imported virtual water was from cereals (679.68 Gm3, 98.4%). Regarding crop product’s water intensity, we found that the exported crop products were excessively concentrated on water-intensive products such as mandarins and clementines, figs, oranges, apricots, plums, citrus fruits, olives, tomatoes, asparagus, peas, and artichokes. On the other hand, the agricultural policy of 2009–2020 increased the production of water-intensive products. This finding seems to be going against the virtual water trade theory, which states that water-poor countries should import water-intensive products and produce local products with lower water requirements.

Accurate and efficient streamflow simulations are necessary for sustainable water management and conservation in arid and semi-arid contexts. Conceptual hydrological models often underperform in these catchments due to the high climatic variability and data scarcity, leading to unstable parameters and biased results. This study evaluates the stability of the HBV model across seven sub-catchments of the Oum Er Rabia river basin (OERB), focusing on the HBV model regionalization process and the effectiveness of Earth Observation data in enhancing predictive capability. Therefore, we developed a nested cross-validation framework for spatiotemporal stability assessment, using optimal parameters from a donor-single-site calibration (DSSC) to inform target-multi-site calibration (TMSC). The results show that the HBV model remains spatially transferable from one basin to another with moderate to high performances (KGE (0.1~0.9 NSE (0.5~0.8)). Furthermore, calibration using KGE improves model stability over NSE. Some parameter sets exhibit spatial instability, but inter-annual parameter behavior remains stable, indicating potential climate change impacts. Model performance declines over time (18–124%) with increasing dryness. As a conclusion, this study presents a framework for analyzing parameter stability in hydrological models and highlights the need for more research on spatial and temporal factors affecting hydrological response variability. [ABSTRACT FROM AUTHOR]

Despite the tax reform in the 1980s, the Moroccan tax system still suffers from structural vulnerabilities that have led to inefficient tax policies and increasing sectoral disparities, which have led to an increase in the tax pressure rates between 1990 and 2020. To overcome these vulnerabilities, and according to the recommendations of the 2019 National Tax Conference, the Moroccan tax system has been recently restructured, with the aim of strengthening the principles of equity and tax efficiency. The main objective of this paper is to determine the impact of the tax pressure on long-term economic growth in Morocco from 1990 to 2020. To do so, the methodology adopted in this work consists of verifying the effect of the tax pressure on economic growth through a quantitative methodology based on two vector autoregression approaches: the vector autoregression model (VAR) and the vector error correction model (VECM). The results of this study confirm that the effect of fiscal pressure on economic growth in Morocco is not significant in the long run, and therefore, we can deduce that taxation is not yet a well-mastered instrument for the state to act on the economic sphere. The results show a complex relationship between tax pressures and economic growth in Morocco, underlining the importance of tax reform. However, the research is limited by the specific models and the need to explore other determinants. The contribution of this paper lies in its in-depth empirical analysis of fiscal pressure and its influence on long-term economic growth in Morocco, as based on VAR and VECM modeling. This econometric approach makes it possible to isolate the dynamic effects and responses of economic variables over time, offering an understanding of the interactions between tax policy and long-term economic growth. By focusing specifically on the Moroccan context, this study offers an original perspective and helps to fill the gap in the empirical research in this area while providing a valuable analytical framework for assessing fiscal policies and their impact on long-term economic growth. [ABSTRACT FROM AUTHOR]

The availability of concurrently high spatiotemporal resolution remote sensing data is highly desirable as they represent a key element for effective monitoring in various environmental applications. However, due to the tradeoff between the spatial resolution and acquisition frequency of current satellites, such data are still lacking. Many studies have been undertaken trying to overcome these problems; however, a couple of long-standing limitations remain, including accommodating abrupt temporal changes, dealing with complex and heterogeneous landscapes, and integrating other satellite datasets as well. Accordingly, this paper proposes a deep learning spatiotemporal data fusion approach based on Very Deep Super-Resolution (VDSR) to fuse the NDVI retrievals from Sentinel-2 and Landsat 8 images. The performances of VDSR are analyzed in comparison with those of two other classical methods, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method. The results obtained indicate that VDSR outperforms other data fusion algorithms as it generated the least blurred images and the most accurate predictions of synthetic NDVI values, particularly in areas with heterogeneous landscapes and abrupt land-cover changes. The proposed algorithm has broad prospects to improve near-real-time agricultural monitoring purposes and derivation of crop status conditions in the field-scale.