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Amidst the rising prevalence of electric vehicles (EV) and the corresponding need for adequate charging infrastructures, this study presents an innovative application of deep learning models to analyze urban traffic. Utilizing the YOLO v3 model, our research focuses on developing a prototype for counting vehicles at an urban roundabout connecting three main roads, providing essential data for planning EV charging infrastructures. This work involves adapting and optimizing the YOLO v3 model to accurately identify and count vehicles passing through the roundabout. The aim is to measure traffic volume and identify circulation trends, which are crucial for understanding the spatial and temporal distribution of vehicles. The collected data not only estimate the number of EVs likely to traverse this area but also predict the needs for charging infrastructure in and around this specific roundabout. Our prototype demonstrated high accuracy in vehicle counting, allowing detailed analysis of traffic patterns at different times of the day. This analysis reveals significant variations in traffic flows, providing valuable insights into the most suitable times and locations for installing EV charging stations. While this study focuses on a single roundabout, it establishes a precedent for using advanced deep learning techniques in assessing EV charging infrastructure needs in broader urban contexts. The results suggest that such methods can be extended to other urban areas for more comprehensive and effective planning of EV charging infrastructures, thus contributing to more sustainable and intelligent urban mobility management. [ABSTRACT FROM AUTHOR]

A large portion of Morocco's forest ecosystem is being damaged by human activity and climate change, which makes it crucial for the country to monitor forest dynamics and develop measures to counter these effects. The objective of this study was to determine how the forest cover has changed over the past four decades in Bouskoura forest, Morocco. Based on Remote Sensing (RS), Geographic Information System (GIS) and machine learning algorithms. Throughout the process, Spatial data such as Landsat 5 TM, Sentinel-2B, and spectral indices, including NDVI, NDWI, NDBI, and MSAVI2 were used to train/validate Random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers. By comparing the performance of the three classifiers for all four periods, the RF method was the most effective with an overall accuracy of 0.99 and kappa coefficient of 0.99 for 1991, 2001 and 2011, and an overall accuracy of 0.99 and kappa coefficient of 0.98 for 2021. Therefore, the RF was selected as a method of examining time variations. The results indicated that forests covered an area of 20,41 km2 in 1991 which has decreased to 18,96 km2 in 2021, a loss of 1,45 km2 (7.10%) in four decades. The highest forest loss was 2,69 km2 during 1991–2001, 2,12 km2 during 2001–2011, 1,40 km2 during 2011–2021. And the highest forest gain was found to be 3,75 km2 during 2011–2021, 0,61 km2 during 2001–2011, 0,43 km2 during 1991-2001. Recent declines in forest degradation attest to the benefits of initiatives to conserve the environment taken by the country. [ABSTRACT FROM AUTHOR]

The Oualidia lagoon on the Atlantic coast of Morocco provides important ecosystem services such as aquaculture, fisheries, tourism and high ecological and biological productivity. This is the first study to describe the spatio-temporal distribution of the crustacean community and potential controlling factors in the coastal waters of the Oualidia lagoon. Crustaceans were sampled with a Van Veen grab during two surveys in winter and summer 2013, and taxonomic composition and diversity were determined at 43 sampling sites. Of the eighteen crustacean taxa recorded, fifteen were new to Oualidia Lagoon. Sphaeroma serratum was the most abundant species in both seasons. Organic matter and chlorophyll a content were higher, temperature and salinity were lower in winter than in summer. The structure of the crustacean assemblages was characterised by the formation of two main clusters, organised according to a downstream gradient. Canonical Correspondence Analysis (CCA) showed that granulometry, organic matter and salinity strongly influenced the distribution pattern of crustaceans in the lagoon. [ABSTRACT FROM AUTHOR]

Agriculture is a very important economic sector in Morocco, which requires a set of tools to improve agricultural production. Among these tools, the use of geomembrane basins. The latter is of great importance in smart farming planning, management practices or even in livestock use. In this context, this study evaluates a recognition and classification of the geomembrane basin using remote sensing satellite images; based on the Yolov3 deep learning neural network. This paper first adjusts the network model to make it suitable for detecting small targets on remote sensing images, then uses the k-means algorithm to calculate the grid size of the Yolo network model suitable for geomembrane basins, then uses yolov3 to train the data that makes up the satellite remote sensing imagery. The network model for the detection of the geomembrane basins is obtained by the test phase. Finally, the geomembrane basin detection model adapted to the remote sensing image is obtained in the validation phase. Through the research and analysis of the experimental results, it can be seen that this method effectively detects the geomembrane basins in the remote sensing images and ensures the high detection accuracy of the experimental results, which gave us an accuracy of 75%. [ABSTRACT FROM AUTHOR]

Currently there is a public awareness to protect the environment especially forest ecosystems and the forest fire dilemma has become a topic of intense research around the world. In this setting, this study evaluates forest fire susceptibility (FFS) in northern Morocco using three geographic information system (GIS) based on machine learning algorithms: XGBoost (XGB), Random Forest (RF), and Support Vector Machine (SVM). To this effect, a Geographic Information System (GIS) database was developed involving ten independent causal factors (elevation, aspect, slope, distance to roads, distance to residential areas, land cover, normalized difference vegetation index (NDVI), rainfall, temperature and wind speed) and 345 fire pixels. The 345 pixels were split into two sets for training (70%) and validation (30%) and the spatial relationships between factors affecting FFs and fire pixels was analyzed using XGB, RF, and SVM models to generate the FFS maps. The effectiveness of the models was evaluated using the receiver operating characteristic curve, the area under the curve (AUC), and several statistical measures. The results of the three models hinted that XGBoost had the highest performance (AUC = 0.856), followed by RF (AUC) = 0.827), and SVM (AUC = 0.803) in the forecasting of the forest fire. The resulting FFS maps areas can provide crucial support for the management of Mediterranean forest ecosystems and can enhance the effectiveness of planning and management of forest resources and ecological balances in these areas. [ABSTRACT FROM AUTHOR]

Globally, Increasing greenhouse production is causing producing environmental issues. Monitoring the number and determining the locations of greenhouses are essential for the modern agricultural land management. The aim of this study was to detect plastic greenhouses using remote sensing data and the YOLOv5 CNN-based object detection algorithm. In the process, 426 images of plastic greenhouses were manually downloaded from Google and Mohammed 6 satellites and were divided into three datasets, training (63%), validation (15%), and testing (22%). Plastic greenhouse images in the training and validation sets were manually labeled, with the training set being used to train the YOLO-V5 Model. Throughout the training, the validation dataset was used to evaluate how well the network performed. According to the performance results the system reached its highest level of accuracy at epoch 71, with a relatively brief training time of 40 minutes, The model's average precision was 84.1 percent, Using satellite images. In detecting plastic greenhouses, YOLOv5 can deliver exceptional accuracy and computational performance. [ABSTRACT FROM AUTHOR]

Accurate and efficient extraction of rural settlements from high-resolution remote sensing imagery is of paramount importance for rural government management. Unplanned rural settlements are quite common. Understanding the spatial characteristic of these rural settlements is of great importance as it offers indispensable information for land management and decision-making. In this setting, the U-net architecture is proposed in this study for rural settlements differentiation by image segmentation on high-resolution satellite images of rural settlements in Zagora province, Draa-Tafilalet region, Morocco. To predict pixels in remote sensing images representing rural settlements in this province. Image segmentation is conducted using different encoders in the U-net architecture, and the results are compared. Experimental results demonstrate that the proposed method effectively mapped and discriminated rural settlements areas with an overall accuracy of 98%, achieving comparable and improved performance over other traditional rural extraction methods. [ABSTRACT FROM AUTHOR]

The molluscan assemblages inhabiting the coastal waters of Oualidia Lagoon, Morocco, were studied during winter and summer of 2013. The taxonomic composition and diversity were determined at 43 sample sites. Thirty-four mollusc taxa were recorded in total, including 12 species of bivalves, 20 species of gastropods, 1 species of polyplacophoran and 1 species of cephalopod. Twenty-six of these species are reported here for the first time in Oualidia Lagoon. The gastropod Peringia ulvae and the bivalve Abra alba were the most abundant taxa in both seasons. The deposit-feeder trophic group was highly dominant in both seasons. A classification analysis revealed the existence of three main clusters from downstream to upstream in both seasons: a marine assemblage, a transition assemblage and a lagoon assemblage. Canonical correspondence analyses indicated that the main environmental variables governing the spatial distribution of the molluscs in winter are salinity, temperature and granulometry, whereas in summer they are salinity and granulometry. This study provides a good baseline for future ecological research. [ABSTRACT FROM AUTHOR]

PbTiO3 (PT) sol gel processed powders doped with lanthanum (PLT14) and cerium (PLCexT14, x = 1; 3; 6; 10) were characterized by XRD and Raman spectroscopy. The samples crystallize in a pure perovskite phase at temperatures smaller than those reported for the same compounds prepared by other conventional methods. The effect of cerium or/and lanthanum on the crystal structure, microstructural and dielectric properties of PbTiO3 were investigated using FTIR, XRD, SEM, Raman spectroscopy and impedance spectroscopy. Incorporation of Ce reduces the temperature of crystallization of the samples, the temperature of the maximum of the permittivity and the diffuseness character of the ferroelectric to paraelectric transition, and enhances the permittivity.

By 2050, Most of the world's population will live in cities, this demographic explosion will lead to significant urban development at the expanse of natural land which may harm the environmental quality. Consequently, assessing and modeling the urban environmental quality (UEQ) is requisite for efficient urban sprawl control and better city planning and management. The present study proposes a methodology to model and assess the environment of the urban system by developing the urban environmental quality index (UEQI) based on remote sensing data. Five environmental indicators were derived from the Landsat OLI image namely, Modified Normalized Difference Impervious Surface Index (MNDISI), Modified Normalized Difference, Water Index (MNDWI), Normalized difference vegetation Index (NDVI), Normalized difference built-up Index (NDBI) and Soil adjusted vegetation index (SAVI). Using the Principal Component Analysis (PCA) the urban environmental quality index was computed for the 17 communes of Casablanca city. The UEQI values were spatially mapped under three classes (good, moderate, and poor). The results obtained from the analysis showed a significant difference in the term of UEQI values among the communes. In addition, the environmental quality is inadequate in communes with fewer green spaces and more impervious surfaces. The outcomes of this work can serve as an efficient tool to determine the most critical interventions to be made by the authority for current and future urban planning and land/resource management. [ABSTRACT FROM AUTHOR]

Coastal lagoons are highly dynamic and physically complicated systems. They are environmentally productive and socio-economically valuable. Contemporary global development and management pressures require a better understanding of their dynamics and sustainability. The present study focuses on the problem of water confinement in the Oualidia lagoon (Atlantic coast of Morocco). This lagoon is characterized by an asymmetric tidal propagation, with a shorter duration of the flood (rising tide) than the ebb (falling tide). In the long term, this contributes to the reduction of depths and the confinement of water upstream. After extensive studies, a sediment trap was created in 2011 to trap the finest sediment in the upstream part of the lagoon. This study aims to analyze the morphodynamical and sedimentological changes in the lagoon of Oualidia, after the sediment trap dredging. For this purpose, bathymetric surveys covering 6 years between 2006 and 2012 were analyzed, providing sufficient data to identify the morphological changes that the lagoon has undergone during this period. The data analysis was followed by a study of the lagoon bed dynamics using profile lines extracted from the bathymetric data in a GIS environment. As a result, the findings partly show that over 6 years, an average height of +0.65 m was gained by the lagoon, while the average change in the eroded areas was estimated to be −0.42 m. In addition, the eroded area in the lagoon was estimated to be about 1,513,800 m2 with an erosion volume of 633,383 m3, while the accumulated area found was about 2,699,396 m2 with an accumulation volume of 1,765,866 m3. These changes can be related to the large input of marine sediment, mainly caused by tidal currents and waves, but also to the creation of a sediment trap in the upstream area of the lagoon. [ABSTRACT FROM AUTHOR]

During the last years, Morocco's Environmental, Social and Touristic perspectives has improved significantly, but it remains one of the biggest challenges for Mohammedia city. The purpose of this study is to locate an optimal site for the construction of a new Waste Water Treatment Plant (WWTP) in order to help the environment, to select an appropriate site for the construction of a new School that will benefit the social needs of the city and to create a suitable tour plan of touristic sites for tourism. It was carried out by using Geographic Information System (GIS), Remote Sensing (RS), Global Positioning System (GPS) devices, Digital cameras and Multi- criteria Decision Analysis (MCDA).Throughout the process, the used data for both WWTP and School selection include the remote sensing data of multi spectral satellite imageries and the digital elevation model combined with vector data of land use (LU) layers, and for Tour creation the used data include collected photographs and GPS data from the site. According to MCDA, the results presents an optimal site with an area of 48283,02 m2 for a new WWTP, a suitable area with an area of 3200 m² for a new School and a city tour for tourists with a distance of 27 km. [ABSTRACT FROM AUTHOR]

Since the industrial revolution, the world is experiencing a huge change in its climate, which causes many imbalances such as flash floods (FF). The aim of this study is to propose a new approach for detection and forecasting of flash flood susceptibility in the city of Tetouan, Morocco. For this regard, support vector machine (SVM), logistic regression (LR), random forest (RF), Naïve Bayes (NB) and Artificial neural network (ANN) are used based on 1101 points (680 flood points and 421 non-flood points) and 9 flash-flood predictors (Elevation , Slope , Aspect , LU/LC , Stream Power Index , Plan curvature , Profile Curvature , Topographic Position Index and Topographic Wetness Index) that were extracted from the DEM (10m resolution) and satellite imagery (Sentinel 2B) of the study area. Models were trained on 70% and tested on 30% of this dataset also they were evaluated using several metrics such as the Receiver Operating Characteristic (ROC) Curve, precision, recall, score and kappa index. The result demonstrated that RF (AUC = 0.99, Accuracy = 96%, Kappa statistics = 0.92) has the highest performance, followed by ANN (AUC = 0.98, Accuracy = 95%, Kappa statistics = 0.89) and SVM (AUC = 0.96, Accuracy = 92%, Kappa statistics = 0.80). The proposed approach is an effective tool for forecasting and predicting FF that can help reduce the severity of this disaster. [ABSTRACT FROM AUTHOR]

Accelerated urban growth has affected many of the planet's natural processes. In cities, most of the surface is covered with asphalt and cement, which has changed the water and air cycles. To restore the balance of urban ecosystems, cities must find the means to create green spaces in an increasingly gray world. Green spaces provide the city and its inhabitants a better living environment. This article uses Nador city as a case study area, this project consists in studying the possibility for the roofs to receive vegetation. The first axis of this project is the quantification of the current vegetation cover at ground level by calculating the Normalized Difference Vegetation Index (NDVI) based on Satellite images Landsat 8, then the classification of the LiDAR point cloud, and the generation of a digital surface model (DSM) of the urban area. This type of derived data was used as the basis for the various stages of estimating the potential plant cover at the roof level. In order to study the different possible scenarios, a set of criteria was applied, such as the minimum roof area, the inclination and the duration of the sunshine on the roof, which is calculated using the linear model of angstrom Prescott based on solar radiation. The study shows that in the most conservative scenario, 21771 suitable buildings that had to be redeveloped into green roofs, with an appropriate surface area of 369.26Ha allowing a 63,40% increase in the city's green space by compared to the current state contributing to the improvement of the quality of life and urban comfort. The average budget for the installation of green roofs in a building with a surface area of 100 m2 varies between 60000dh and 170000dh depending on the type of green roofs used, extensive or intensive. These results would enable planners and researchers in green architecture sciences to carry out more detailed planning analyzes. [ABSTRACT FROM AUTHOR]

In the last 2 decades, Morocco has known rapid growth of urban transformation followed by significant Population growth, which causes serious environmental problems related to water pollution and scarcity, and social with the deficiencies of infrastructures. And this has been witnessed in the city of Biougra which requires taking serious steps and adopting new projects to solve these issues as soon as possible. And as a reflection of that, this paper takes advantage of Geographic information system (GIS) coupled tools in the first place to locate future sites for building new schools using weighted overlay analysis approach, to improve the education system. And in the second place to choose potential sites for implementing new wastewater treatment plant (WWTP) by adopting multicriteria analysis (MCA). Finally, as a part of saving cultural heritage, and improving the social and economic situation for local citizens, we aim to value cooperative of this region as a replacement of poor touristic quality in the city, by making a track of the existing cooperative and profit from 3d modeling as a part of providing the traveler the best possible guide to reach these points of interest and also develop a desktop application for editing and manipulating different types of file related to cooperatives. This study showed successful results by localizing a new site to build a school in the northeastern of the city, and a match between the found site for WWTP and the existing station but with giving a possibility for expansion. [ABSTRACT FROM AUTHOR]

Cheating in exams is a worldwide phenomenon that hinders efforts to assess the skills and growth of students. With scientific and technological progress, it has become possible to develop detection systems in particular a system to monitor the movements and gestures of the candidates during the exam. Individually or collectively. Deep learning (DL) concepts are widely used to investigate image processing and machine learning applications. Our system is based on the advances in artificial intelligence, particularly 3D Convolutional Neural Network (3D CNN), object detector methods, OpenCV and especially Google Tensor Flow, to provides a real-time optimized Computer Vision. The proposal approach, we provide a detection system able to predict fraud during exams. Using the 3D CNN to generate a model from 7,638 selected images and objects detector to identify prohibited things. These experimental studies provide a detection performance with 95% accuracy of correlation between the training and validation data set. [ABSTRACT FROM AUTHOR]

Storms represent an increased source of risk that affects human life, property, and the environment. Prediction of these events, however, is challenging due to their low frequency of occurrence. This paper proposed an artificial intelligence approach to address this challenge and predict storm characteristics and occurrence using a gated recurrent unit (GRU) neural network and a support vector machine (SVM). Historical weather and marine measurements collected from buoy data, as well as a database of storms containing all the extreme events that occurred in Brittany and Pays de la Loire regions, Western France, since 1996, were used. Firstly, GRU was used to predict the characteristics of storms (wind speed, pressure, humidity, temperature, and wave height). Then, SVM was introduced to identify storm-specific patterns and predict storm occurrence. The approach adopted leads to the prediction of storms and their characteristics, which could be used widely to reduce the awful consequences of these natural disasters by taking preventive measures. [ABSTRACT FROM AUTHOR]

Energy plays a crucial role in the economy of any country. One of the most recent trends in global development is the transition to "green" growth. Morocco is keeping pace with its growth by promising to increase renewable energy capacity to 42% of total installed capacity by 2020 and to 52% by 2030. This study develops a framework that aims to determine the optimal areas for deploying photovoltaic (PV) installation in two stages. The first stage aims at excluding the undesirable areas such as forests, rivers and agricultural land. The second stage consists in defining the suitability of sites based on seven criteria; solar irradiation, land surface temperature, slopes, slope orientation, distance from power lines, distance from main roads, distance of urban area. In this study we are using fuzzy logic and fuzzy membership functions in order to create criteria layers in the environment of Geographic Information System (GIS) that allowing the integration of a Multi-Criterion Decision Analysis (MCDA) to identify the best sites to deploy PV solar power plants in north-western Morocco. Also, the Analytic Hierarchy Process (AHP) technique is used to determine weights for each one of the criteria. Results obtained from the spatial approach shows that the major portion of the studied area was judged inappropriate for solar farms installations. Also, it shows that only 5.11% (8500 Ha) of the territory demonstrate high suitability for PV solar installations located in the southern part of our studied area with a potential of electricity generation, from the areas with high suitability level, assuming a PV system efficiency of 6.48%; equivalent to 5.39 Twh/year with an installed capacity of 4.02 GW and an annual carbon emission reduction of 3 697.54 Kt-CO2/year. The resulting suitability map can be used as a decision support tool to help the public policy makers to integrate green energy into their policies. [ABSTRACT FROM AUTHOR]

In recent decades, many of the countries around the world as well as the south-western Morocco (Guelmim region, Assaka watershed), was subject to flood-storm causing huge human and material damages. The current study focuses on the Prediction of flash flood susceptibility using Fuzzy Analytical Hierarchy Process (FAHP) algorithms and Geographic Information System (GIS) technical. Flash floods areas were identified based on seven flash flood conditioning factors (Soil Moisture Index (SMI), Drainage Density, Rainfall, LULC, Altitude, Slope and Soil). Using AHP the weight derived for the factors were SMI 37% Rainfall 24.30%, Drainage Density 15.57%, LULC 9.98% Altitude 6.39% Slope of the river basin 4.06% and Soil type 2.70%. Then, applying a fuzzy inference system to create flash flood vulnerability maps. The resulting maps were classified into three categories: low, moderate and high flash flood susceptibility; indicated that the areas at the outlet of the watershed and which are close of the main affluent wadis (Seyyad and Oum Al-Achar) were very susceptible to flooding. This study will be helping these zones to be prioritized for the conservation and managing of flash floods. [ABSTRACT FROM AUTHOR]

Managing multiple ecosystem services (ES) across forest landscape, constitute a growing field of research. It represents a key challenge that attempts to optimize the trade-offs among provisioning, supporting, regulating and cultural ES. Here, we review approaches and shortcomings on some anterior FES studies. Three main components are presented, 1) an overview of the current state of research, 2) a summary of main methods adopted 3) an identification of principal approaches' restrictions. Several conclusions emerge: most of the studies focus on a limited number of FES which might undermine the long-term provision of other FES, or converge to using free software models which are practical and low-cost but require enormous data. This reveals how the lack of existing inventories and evaluations impacts the choice of methodologies and lead to use indirect methods of measurement. However, researches that aim to understand the relationships and conflicts among multiple FES and seeks to find out the best management regime will improve our ability to sustainably fulfil economic, ecologic and social goals. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems store more carbon (C) than the atmosphere and provide ecosystem services (ES) such as global climate regulation, by sequestering carbon within biomass and soil. Land use land cover (LULC) change is considered a key factor, playing an important role in the dynamic variations of carbon storage. The aim of this paper is to assess the effects that LULC has had on carbon stocks and consequently on climate change regulation in north-western Morocco over 21 years. To achieve this aim, the Integrated Valuation of ES and Trade-offs (InVEST) model is used to assess status and variation in the net amount of carbon stored by the different types of LULC, and the economic value of the carbon sequestered in the remaining stock. The results show that the total carbon stock increased from 4.81TgC in 1996 to 4.98TgC in 2017. Over the 21 years, the LULC changes had the greatest effect on carbon storage - an increase of 6.87% with 0.17TgC of carbon sequestered, since the majority of unused land was changed to forest and cultivated land. Based on the global costs of atmospheric carbon, we estimate the economic value of carbon storage services to be between US$1,800,000 and US$3,570,000 for the whole period, with an average yearly increment of between US$86,000 and US$170,000. The results show that the ecosystem management has had a substantial climate mitigation effect. Also, the possibility of paying for ES could inform policy on the adoption of LULC to support livelihood and management choices. [ABSTRACT FROM AUTHOR]

Storms can cause significant damage, severe social disturbance and loss of human life, but predicting them is challenging due to their infrequent occurrence. To overcome this problem, a novel deep learning and machine learning approach based on long short-term memory (LSTM) and Extreme Gradient Boosting (XGBoost) was applied to predict storm characteristics and occurrence in Western France. A combination of data from buoys and a storm database between 1996 and 2020 was processed for model training and testing. The models were trained and validated with the dataset from January 1996 to December 2015 and the trained models were then used to predict storm characteristics and occurrence from January 2016 to December 2020. The LSTM model used to predict storm characteristics showed great accuracy in forecasting temperature and pressure, with challenges observed in capturing extreme values for wave height and wind speed. The trained XGBoost model, on the other hand, performed extremely well in predicting storm occurrence. The methodology adopted can help reduce the impact of storms on humans and objects., (© 2024. The Author(s).)

Objective: The aim of this study was to present the results obtained in the Newborn Screening Program (NSP) for sickle cell disease (SCD) in western Andalusia and the autonomous city of Ceuta in the first 3 years of implementation, and to describe the discrepancies found in the diagnosis of hemoglobinopathies between the screening method and the confirmatory tests., Study Design: A descriptive and retrospective study was carried out, and the findings obtained in the newborns included in the NSP between November 2018 and December 2021 were analyzed., Results: A total of 111,205 samples were screened by high-performance liquid chromatography (HPLC). The birth prevalence of SCD, sickle cell trait, hemoglobin C carriers, and the compound heterozygosity Hb C/β-thalassemia was 1/12,356, 1/467, 1/1,278, and 1/55,602 newborns, respectively. Although there was a correlation between the first-line HPLC screening technique (VARIANTnbs HPLC analyzer, Bio-Rad) and the confirmatory tests in most cases, major discrepancies were found in detecting carriers of G-Philadelphia, D, E, and O-Arab hemoglobin variants, with the former having an incidence of 1/10,110 and the others 1/22,241. The carrier status of Hb G-Philadelphia produced an FAD pattern on the screening method that could be mistaken as Hb D, while Hb O-Arab was identified as an FA5 pattern. Hb D was initially recognized as Hb D in two cases., Conclusion: An NSP requires at least two different combined methods in order to identify the hemoglobin variant with sufficient certainty. Furthermore, even though software solutions for HPLC suggest a pattern, it must be confirmed with another technique to obtain a correct interpretation of the chromatograms., Key Points: · The NSPs are an essential activity in preventive medicine.. · At least two different combined methods are required to correctly identify hemoglobin variants.. · Different variants can produce a similar or identical pattern by a single method.., Competing Interests: None declared., (Thieme. All rights reserved.)

The Camargue or Rhône delta is a coastal wetland in southern France of which parts formerly devoted to salt production are undergoing a renaturation process. This study assessed a multisensor approach to investigate the link between sediment size distribution, habitat development mapped with Worldview 2, flooding durations estimated with time series of SENTINEL 2 images and elevation modelled with a LIDAR point cloud in former saltworks. A Random Forest classification algorithm was used to map the vegetation distributions of Sarcocornia fruticosa and Arthrocnemum macrostachyum, main representatives of the NATURA 2000 "Mediterranean and thermo-Atlantic halophilous scrubs (Sarcocornetea fruticosi)" habitat on the site. The best habitat map was obtained when considering the species separately. The random forest Out-of-bag errors were 1.43 % for S. fruticosa and 2.18 % for A. macrostachyum. Both species were generally distributed on different elevation and flooding duration zones considering mean values. Flooding duration was estimated with the Water In Wetland index (WIW) based on 15 Sentinel-2 scenes. Two models related to sediment grain size distribution were developed: one predicting the flooding duration and one predicting the halophilous scrub distribution. Maps of flooding duration, sediment grain size distribution and elevation highlighted two main zones in the study area: a western section with coarser sediments, shorter flooding durations and higher elevations under sea influence; an eastern section with finer sediments, longer hydroperiods and lower elevations under a historic river influence. This multidisciplinary approach offers perspectives for using space-based data over large scales to monitor changes in edaphic conditions of coastal areas facing natural and anthropogenic forcings. The results call for further investigations to predict the dynamic distribution of other coastal habitats following climate change impacts, such as sea level rise., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

This comparative study assessed hepatitis A virus (HAV) contamination in shellfish harvesting areas in Morocco, and the correlations between viral contamination and rainfall. To this aim, HAV contamination was evaluated in 156 shellfish samples collected at three Moroccan coastal areas (52 samples/area): Dakhla (class A), Oualidia (class B), and Moulay Bousselham (class C). Samples were collected monthly between March 2018 and March 2019, and included oysters from different farms at the Oualidia and Dakhla coastal areas, and wild mussels at the Moulay Bousselham lagoon. HAV was detected by RT-qPCR in 24/156 (15.38%) samples: 16/145 (11.03%) mussel (Mytillus galloprovincialis) and 8/156 (5.13%) oyster (Crassostea gigas) samples. The 16 mussel-positive samples (16/52) were collected at Moulay Bousselham, and the eight oyster-positive samples (8/52) were collected at Oualidia, and none at Dakhla (class A). The highest HAV contamination rate was recorded at Moulay Bousselham lagoon (class C) (30.77%), while it did not exceed 8% at Oualidia (class A). A nonsignificant, positive correlation between HAV-positive samples and mean rainfall was observed. The important HAV presence in bivalve mollusks from the Oualidia and Moulay Bousselham lagoons indicate the need of viral surveillance of bivalve mollusks and of improving wastewater quality to enhance shellfish safety., (© The Author(s) 2022. Published by Oxford University Press on behalf of Applied Microbiology International.)

Seagrass is a vital structural and functional element of the marine environment worldwide and is highly valued for its ecological benefits. Monitoring the evolution of the seagrass habitat is essential to understand how this coastal ecosystem changes, and to develop good environmental management practices. For the present study, two remote sensing methods were used to map and monitor Zostera noltei Hornemann, 1832 ( Z. noltei ), in the Merja Zerga lagoon from 2010 to 2020. These methods which are the random forest algorithm and the object-oriented classification, were convenient to provide significant results. The first approach employed Sentinel-2 images from 2018 to 2020, which were used to extract information on changes in Z. noltei (commonly called dwarf eelgrass) distribution and aboveground biomass estimation. The second involved three orthophotography (orthophoto) mosaics from the years 2010, 2016, and 2018, which were analyzed to map the distribution of the species. It was revealed that Z. noltei coverage has increased by 212 ha since 2010, with most of the growth occurring in the center and upstream part of the lagoon. The mean aboveground biomass of dwarf eelgrass in the lagoon was 78.5 DW/m² in 2018, 92.6 DW/m² in 2019, and 115.2 g DW/m² in 2020. The approach used in this study has provided important insights into the dynamic and mean biomass of Z. noltei in the Merja Zerga lagoon. It is therefore a valuable, non-destructive method that uses freely-available Sentinel-2 satellite data., Competing Interests: Competing InterestsThe authors have no relevant financial or non-financial interests to disclose., (© The Author(s), under exclusive licence to Society of Wetland Scientists 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Water browning or brownification refers to increasing water color, often related to increasing dissolved organic matter (DOM) and carbon (DOC) content in freshwaters. Browning has been recognized as a significant physicochemical phenomenon altering boreal lakes, but our understanding of its ecological consequences in different freshwater habitats and regions is limited. Here, we review the consequences of browning on different freshwater habitats, food webs and aquatic-terrestrial habitat coupling. We examine global trends of browning and DOM/DOC, and the use of remote sensing as a tool to investigate browning from local to global scales. Studies have focused on lakes and rivers while seldom addressing effects at the catchment scale. Other freshwater habitats such as small and temporary waterbodies have been overlooked, making the study of the entire network of the catchment incomplete. While past research investigated the response of primary producers, aquatic invertebrates and fishes, the effects of browning on macrophytes, invasive species, and food webs have been understudied. Research has focused on freshwater habitats without considering the fluxes between aquatic and terrestrial habitats. We highlight the importance of understanding how the changes in one habitat may cascade to another. Browning is a broader phenomenon than the heretofore concentration on the boreal region. Overall, we propose that future studies improve the ecological understanding of browning through the following research actions: 1) increasing our knowledge of ecological processes of browning in other wetland types than lakes and rivers, 2) assessing the impact of browning on aquatic food webs at multiple scales, 3) examining the effects of browning on aquatic-terrestrial habitat coupling, 4) expanding our knowledge of browning from the local to global scale, and 5) using remote sensing to examine browning and its ecological consequences., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Quantifying and mapping cultural ecosystem services are complex because of their intangibility. Data from social media, such as geo-tagged photographs, has been proposed for mapping cultural use or appreciation of ecosystems. However, manual content analysis and classification of large numbers of photographs is time-consuming. The potential of deep learning for automating the analysis of crowdsourced social media content is still being explored in CES research. Here, we use a new deep learning model for automating the classification of natural and human elements relevant to CES from Flickr images. This approach applies a convolutional neural network architecture to analyze over 29,000 photographs from the Lithuanian coast and uses hierarchical clustering to group these photographs. The accuracy of the classification was assessed by comparison with manual classification. Over 37% of the photographs were taken for the landscape appreciation class, and 28% of the photographs were taken of nature, of animals or plants, which represent the nature appreciation class. The main clusters were identified in urban areas, more precisely in the main coastal cities of Lithuania. The distribution of the nature photographs was concentrated around particular natural attractions, and they were more likely to occur in parks and natural reserves with high levels of vegetation and animal cover. This approach that was developed for clustering the photographs was accurate and saved approximately 100 km of manual work. The method demonstrates how analyzing large numbers of digital photographs expands the analytical toolbox available to researchers and allows the quantification and mapping of CES at large geographical scales. Automated assessment and mapping of cultural ecosystem services could be used to inform urban planning and improve nature reserve management., Competing Interests: Conflicts of Interest/Competing InterestsThe authors declare that they have no conflict of interest and no competing interests., (© The Author(s), under exclusive licence to Society of Wetland Scientists 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

The arid coasts of North Africa, extending over 4633 km from the Gulf of Tunis to the Nile Delta, are undergoing pronounced shoreline retreats and coastal floodings that are reported as a consequence of the ongoing sea level rise resulting from global warming. Of particular interest are the abnormal shoreline dynamics for deltaic and sandy beaches, which are severely impacted by abrupt decadal variabilities in both climatic and anthropogenic drivers resulting in their increased vulnerability to disturbances from coastal hazards. Unfortunately, the evolution, distribution and impacts of these drivers remain largely unquantified, let alone understood, for these extensive arid coasts that harbor the major portion of North Africa's population as well as unique and fragile marine ecosystems. To address this deficiency, we use GIS-based multi-criteria approaches combined with analytic hierarchy process to map the Coastal Vulnerability Index and the Socioeconomic Vulnerability Index along these coasts to investigate the amplitude and extent of shoreline deterioration resulting from sudden fluctuations in sediment transport to the coastline. We use the western bay of the Gulf of Tunis, the coasts of Tripoli and the Nile Delta as three validation sites for our vulnerability assessment. The statistical Integrated Coastal Vulnerability Index map reveals that 47% of arid North African coasts are characterized by high to very high vulnerability. In particular, we observe that the densely populated deltaic coasts in both Tunisia and Egypt are 70% more vulnerable than any others coast in the eastern Mediterranean Basin. These abnormally high-vulnerability extensive areas are also correlated with significant deterioration of coastal aquifers and hence in crop production, compromising local food security and resulting in increasing outflow migration trends. Both Tunisia and Egypt observed dramatic increases in the net population outflow migration by respectively 62% and 248% between 2000 and 2016, mostly from coastal areas. Our source analysis of the amplitude and extent of these high coastal vulnerabilities suggests that they result from the anthropogenic drivers of damming and rapid urban growth over the last few decades rather than the effects of global warming.

This paper reviews the climatological influences on major past storm events in the North-east Atlantic. Analyses are based on a millenary record of sedimentological and historical impacts affecting coastal societies. The effects of 20 past storms have been found from sedimentary deposits from the last 1,000 years. Historical archives confirmed these events. This paper highlights five major storms that have markedly impacted coastal populations. They date back to 1351-1352, 1469, 1645, 1711 and 1751 AD. The 1351-1352 AD event is defined as a millennium storm that was "likely apocalyptical", provoking serious damage and long lasting floods on much of the European coast. Major storm impacts have mostly been recorded during positive North Atlantic Oscillation phases. Four decreasing temperature phases are concomitant with 1300-1355, 1420-1470, 1560-1590 and 1690-1715 AD periods, during which much of the northern Atlantic coast of France underwent severe storm damages.

Heavy metal assessment in Dakhla Bay (Atlantic coast) was carried out using different environmental and ecological indices. Heavy metal concentrations were measured using ICP-AES and were compared with consensus-based sediment quality guidelines. The distribution of heavy metal concentrations varies for the three groups: (i) lead distribution is dominated by its associations with copper and chromium. These elements have the same source, most probably related to anthropogenic activities. (ii) Nickel, zinc and cobalt are associated with aluminium and iron indicating their terrigenous origin (natural content), and (iii) cadmium concentration is related to upwelling currents. This paper systematically studied the distributions and pollution levels of heavy metals in sediment in the coastal areas in Dakhla Bay, which is of scientific significance, to discuss the changing rules and the affecting factors of the harmful heavy metals and can be adopted for reference to other coastal areas.

Infertility has emerged as a significant public health concern, with assisted reproductive technology (ART) is a last-resort treatment option. However, ART's efficacy is limited by significant financial cost and physical discomfort. The aim of this study is to build Machine learning (ML) decision-support models to predict the optimal range of embryo numbers to transfer, using data from infertile couples identified through literature reviews. Binary classification models were developed to classify cases into two groups: those transferring two or fewer embryos and those transferring three or four. Four popular ML algorithms were used, including random forest (RF), logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN), considering seven criteria: the woman's age, sperm origin, the developmental qualities of four potential embryos, infertility duration, assessment of the woman, morphological qualities of the four best embryos on the day of transfer, and number of oocytes extracted. The stratified 3-fold cross-validation results show that the SVM model obtained the highest average accuracy (95.83%) and demonstrated the best overall performance, closely followed by the ANN and LR models with an average accuracy equal to 91.67%. The RF model achieved a slightly lower average accuracy (88.89%), which demonstrated the lowest variability. Testing on a new dataset revealed all models performed well, with ANN and SVM models classified all test set instances correctly, while the RF and LR models achieved 91.68% accuracy. These results highlight the superior generalization and effectiveness of the ANN and SVM models in guiding ART decisions.

The present research presents the first large-scale analysis of heavy metal assessment in the Moroccan Atlantic shelf. This work provides scientific basis for future studies on environmental research and fills the gap in knowledge on the worldwide continental platforms. Metal distributions identified three different zones, mainly influenced by industrial and urban sewer (northern areas), agriculture runoffs (central zone), and estuarine discharges (southern areas), respectively. In the north part of the shelf, metal enrichments are observed near industrial and urban sewer mouths (Casablanca and Mohammedia cities). In the south and central areas, the probable absence of human impact on sediments is attributed to effective trapping in the estuary (Oum Er Rbia) and coastal zones, as well as dilution with less contaminated sediments and shelf sediments and removal with fine fractions due to estuary discharges., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Superficial and cored sediment samples from the Moulay Bousselham lagoon and sub-watershed were analyzed for Al, Fe, Cu, Zn, Pb, Mn, Ni, Cr, As, Hg, and Cd. The temporal and spatial distributions of the main contamination sources of heavy metals were identified and described using chemometric and geographic information system (GIS) methods. Sediments from coastal lagoons near urban and agricultural areas are commonly contaminated with heavy metals, and the concentrations found in surface sediments are significantly higher than those from 50-100 years ago. The concentrations of these elements decrease sharply with depth in the sediment column, and the elements are preferentially enriched in the <2-μm-sized fraction of the sediment. The zones of enhanced risk of heavy metals were detected by means of GIS-based geostatistical modeling. According to sediment pollution indices and statistical analysis, heavy metals (Pb, Cu, Ni, Zn, Cr, and Hg) that pose a risk have become largely enriched in the lagoon sediments during the recent period of agricultural intensification.

In an effort to develop safe and efficacious compounds for the treatment of metabolic disorders, novel thiophene substituted oxazole containing α-alkoxy-phenylpropanoic acid derivatives are designed as highly potent PPARα/γ dual agonists. These compounds were found to be efficacious at picomolar concentrations. Lead compound 18d has emerged as very potent PPARα/γ dual agonist demonstrating potent antidiabetic and lipid lowering activity at a very low dose and did not exhibit any significant signs of toxicity in rodents., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

A novel series of oxime containing benzyl-1,3-dioxane-r-2-carboxylic acid derivatives (6a-k) were designed as selective PPARα agonists, through bioisosteric modification in the lipophilic tail region of PPARα/γ dual agonist. Some of the test compounds (6a, 6b, 6c and 6f) showed high selectivity towards PPARα over PPARγ in vitro. Further, highly potent and selective PPARα agonist 6c exhibited significant antihyperglycemic and antihyperlipidemic activity in vivo, along with its improved pharmacokinetic profile. Favorable in-silico interaction of 6c with PPARα binding pocket correlate its in vitro selectivity profile toward PPARα over PPARγ. Together, these results confirm discovery of novel series of oxime based selective PPARα agonists for the safe and effective treatment of various metabolic disorders., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

A novel series of thaizole and oxazole containing phenoxy acetic acid derivatives is reported as PPAR-pan agonists. Incorporation of structurally constrained oxime-ether based linker in the chemotype of a potent PPARδ selective agonist GW-501516 was adapted as designing strategy. In vitro, selected test compounds 12a, 12c, 17a and 18a showed PPAR-pan agonists activities and among these four compounds tested, 12a emerged as highly potent and efficacious compound, while 17a exhibited moderate and balanced PPAR-pan agonistic activity. In vivo, selected test compounds 12a and 17a exhibited significant anti-hyperglycemic and anti-hyperlipidemic activities in relevant animal models. These results support our hypothesis that the introduction of structurally constrained oxime-ether linker between lipophilic tail and acidic head plays an important role in modulating subtype selectivity and subsequently led to the discovery of potent PPAR-pan agonists., (Copyright © 2010 Elsevier Ltd. All rights reserved.)