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This study was carried to assess the radiological health risks associated with occupational exposure to natural radioactivity in some of ceramic raw materials and building materials. The concentrations of Ra-226, Th-232, and K-40 were measured using a gamma ray spectrometer equipped with a hyper pure germanium (HPGe) detector. In the present work, the mean values of Ra-226 for fly ash, bauxite, ceramic colors and marble are higher than the world average value of 35 (Bq/kg). The mean values of Th-232 for clay, fly ash, bauxite, ceramic colors, marble and granite are higher than the world average value of 30 (Bq/kg). For K-40, field spar, ceramic colors, marble and granite have mean values that higher than the world average value of 400 (Bq/kg). The radiological health hazard parameters such as radium equivalent activity, absorbed dose rate, annual effective dose rate, excess life time cancer risk, external hazard index and internal hazard index were calculated based on the mean values of these radionuclides. The maximum values of radium equivalent activity, absorbed dose rate, annual effective dose rate, excess life time cancer risk, external hazard index and internal hazard index were 3299.98 (Bq/kg), 4429.658 (nGy/h), 3.703 (mSv/y), 12.962, 8.92 and 15.97; respectively. Also, the equivalent and effective doses due to occupational exposure were calculated with Dose Conversion Factors for External Exposure (DFEXT code).

Abstract A novel chelating adsorbent based on (3-iminodiacetic acid) propyltriethoxysilane graphene oxide (IAT-GO) has been developed, showing exceptional promise for capturing lead. IAT-GO is made by combining a high-surface-area graphene oxide with a specially designed chelating ligand, which can selectively and efficiently remove lead. The synthesis of IAT-GO involves a two-step progression. In the first step, covalent bonds form between graphene oxide and (3-aminopropyl)-triethoxysilane (AT) through hydrolysis, condensation, and epoxide ring opening reactions. In the second step, nucleophilic substitution reactions occur between the primary amines and chloroacetic acid (CAA). A comprehensive suite of characterization techniques, including XPS, UV–Vis, XRD, Raman, FTIR, TEM, and SEM, provides detailed insights into the IAT-GO adsorbent's chemical composition and physical form, elucidating its intricate structure and morphology. Optimizing the experimental conditions for using the adsorbent material to remove Pb(II) ions from contaminated water revealed a maximum adsorption capacity of 124.0 mg/g at pH 5 and 30 min. The IAT-GO displays high selectivity for Pb(II) in a mixture of six metal ions containing 100 ppm of each one. Moreover, the IAT-GO shows 100% removal of Pb(II) for concentrations lower than 50 ppm. The excellent fit of the experimental data with the Langmuir adsorption isotherm and pseudo-second-order kinetic models (R2 > 99%) indicates that Pb(II) ion uptake onto the IAT-GO surface occurs via the monolayer formation of mercury ions. IAT-GO demonstrates exceptional potential as an innovative adsorbent for lead-contaminated water. Nitric acid (0.4 M) effectively regenerates the material, while its reusability remains impressive even after five cycles (> 97% removal efficiency). Therefore, this study highlights the development of a groundbreaking material, IAT-GO, with exceptional potential for remediating lead-contaminated water. Its high efficiency, selectivity, reusability, and cost-effectiveness make it a promising candidate for real-world applications.

The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive intercomparison due to the availability of quality checked in situ databases. The CoastColour Round Robin (CCRR) project, funded by the European Space Agency (ESA), was designed to bring together three reference data sets using these to test algorithms and to assess their accuracy for retrieving water quality parameters. This paper provides a detailed description of these reference data sets, which include the Medium Resolution Imaging Spectrometer (MERIS) level 2 match-ups, in situ reflectance measurements, and synthetic data generated by a radiative transfer model (HydroLight). These data sets, representing mainly coastal waters, are available from doi:10.1594/PANGAEA.841950. The data sets mainly consist of 6484 marine reflectance (either multispectral or hyperspectral) associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: total suspended matter (TSM) and chlorophyll a (CHL) concentrations, and the absorption of coloured dissolved organic matter (CDOM). Inherent optical properties are also provided in the simulated data sets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three data sets are compared. Match-up and in situ sites where deviations occur are identified. The distributions of the three reflectance data sets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

Remote sensing (RS) scene classification has received significant consideration because of its extensive use by the RS community. Scene classification in satellite images has widespread uses in remote surveillance, environmental observation, remote scene analysis, urban planning, and earth observations. Because of the immense benefits of the land scene classification task, various approaches have been presented recently for automatically classifying land scenes from remote sensing images (RSIs). Several approaches dependent upon convolutional neural networks (CNNs) are presented for classifying brutal RS scenes; however, they could only partially capture the context from RSIs due to the problematic texture, cluttered context, tiny size of objects, and considerable differences in object scale. This article designs a Remote Sensing Scene Classification using Dung Beetle Optimization with Enhanced Deep Learning (RSSC-DBOEDL) approach. The purpose of the RSSC-DBOEDL technique is to categorize different varieties of scenes that exist in the RSI. In the presented RSSC-DBOEDL technique, the enhanced MobileNet model is primarily deployed as a feature extractor. The DBO method could be implemented in this study for hyperparameter tuning of the enhanced MobileNet model. The RSSC-DBOEDL technique uses a multi-head attention-based long short-term memory (MHA-LSTM) technique to classify the scenes in the RSI. The simulation evaluation of the RSSC-DBOEDL approach has been examined under the benchmark RSI datasets. The simulation results of the RSSC-DBOEDL approach exhibited a more excellent accuracy outcome of 98.75 % and 95.07 % under UC Merced and EuroSAT datasets with other existing methods regarding distinct measures.

The exponential progress of image editing software has contributed to a rapid rise in the production of fake images. Consequently, various techniques and approaches have been developed to detect manipulated images. These methods aim to discern between genuine and altered images, effectively combating the proliferation of deceptive visual content. However, additional advancements are necessary to enhance their accuracy and precision. Therefore, this research proposes an image forgery algorithm that integrates error level analysis (ELA) and a convolutional neural network (CNN) to detect the manipulation. The system primarily focuses on detecting copy-move and splicing forgeries in images. The input image is fed to the ELA algorithm to identify regions within the image that have different compression levels. Afterward, the created ELA images are used as input to train the proposed CNN model. The CNN model is constructed from two consecutive convolution layers, followed by one max pooling layer and two dense layers. Two dropout layers are inserted between the layers to improve model generalization. The experiments are applied to the CASIA 2 dataset, and the simulation results show that the proposed algorithm demonstrates remarkable performance metrics, including a training accuracy of 99.05%, testing accuracy of 94.14%, precision of 94.1%, and recall of 94.07%. Notably, it outperforms state-of-the-art techniques in both accuracy and precision.

EAGLE2006 – an intensive field campaign for the advances in land surface hydrometeorological processes – was carried out in the Netherlands from 8th to 18th June 2006, involving 16 institutions with in total 67 people from 16 different countries. In addition to the acquisition of multi-angle and multi-sensor satellite data, several airborne instruments – an optical imaging sensor, an imaging microwave radiometer, and a flux airplane – were deployed and extensive ground measurements were conducted over one grassland site at Cabauw and two forest sites at Loobos and Speulderbos in the central part of the Netherlands. The generated data set is both unique and urgently needed for the development and validation of models and inversion algorithms for quantitative land surface parameter estimation and land surface hydrometeorological process studies. EAGLE2006 was led by the Department of Water Resources of the International Institute for Geo-Information Science and Earth Observation (ITC) and originated from the combination of a number of initiatives supported by different funding agencies. The objectives of the EAGLE2006 campaign were closely related to the objectives of other European Space Agency (ESA) campaign activities (SPARC2004, SEN2FLEX2005 and especially AGRISAR2006). However, one important objective of the EAGLE2006 campaign is to build up a data base for the investigation and validation of the retrieval of bio-geophysical parameters, obtained at different radar frequencies (X-, C- and L-Band) and at hyperspectral optical and thermal bands acquired simultaneously over contrasting vegetated fields (forest and grassland). As such, all activities were related to algorithm development for future satellite missions such as the Sentinels and for validation of retrievals of land surface parameters with optical and thermal and microwave sensors onboard current and future satellite missions. This contribution describes the campaign objectives and provides an overview of the airborne and field campaign dataset. This dataset is available for scientific investigations and can be accessed on the ESA Principal Investigator Portal http://eopi.esa.int/.

Securing user electronics devices has become a significant concern in the digital period, and a forward-thinking solution covers the fusion of blockchain (BC) technology and deep learning (DL) methods. Blockchain improves device safety by transforming access management, storing credentials on a tamper-resistant ledger, mitigating the risk of unauthorized access and giving a robust defence against malevolent actors. Integrating DL into this framework also raises safety measures, as it permits devices to inspect and regulate to develop attacks distinctly. DL models accurately recognize intricate designs and anomalies, allowing the technique to distinguish and threaten possible attacks in real time. The fusion of BC and DL not only improves the reliability of user electronics but also establishes a dynamic and adaptive safety system, enhancing consumer confidence in the safety of their devices. Therefore, this study presents a BC-Based Access Management with DL Threat Modeling (BCAM-DLTM) technique for securing consumer electronics devices in the IoT ecosystems. The BCAM-DLTM technique mainly follows a two-phase procedure: access management and threat detection. Moreover, BC technology can be applied to the access management of consumer electronics devices. Besides, the BCAM-DLTM technique applies a deep belief networks (DBNs) model for proficiently identifying threats. To enhance the recognition results of the DBN model, the hyperparameter tuning procedure uses the reptile search algorithm (RSA). The experimental outcome study of the BCAM-DLTM approach employs the NSLKDD dataset. The comprehensive results of the BCAM-DLTM approach portrayed a superior accuracy outcome of 99.63% over existing models in terms of distinct metrics.

Rough set has an important role to deal with uncertainty objects. The aim of this article is to introduce some kinds of generalization for rough sets through minimal neighborhoods using special kinds of binary relations. Moreover, four different types of dual approximation operators will be constructed in terms of minimal neighborhoods. The comparison between these types of approximation operators is discussed. Some new kinds of topological structures induced by minimal neighborhoods are established and some of their properties are studied. Finally, we give a comparison between these topologies that help for determining the major components of COVID-19 infections. In this application, the components of infections help the expert in decision making in medicine.

Integration of the pulsed power load (PPL) into the power system leads to high fluctuations in the frequency and voltage at the point of common coupling (PCC). This paper presents an effective solution to overcome these issues related to the PPL using the superconducting magnetic energy storage (SMES) system that has the features of high power capability and fast response. The fuzzy logic control technique (FLC) is employed to control the interfaced DC–DC chopper of the SMES to regulate the active power of the PCC and the state-of-charge (SOC) of the SMES coil. Two scenarios have been addressed with/without integrating the SMES conversion system. The on-time of the studied PPL is 0.1s with a total power of 5.7 MW, which is considered a high power demand in a short period. The simulation results based on MATLAB/Simulink show the high performance of the proposed system based on the FLC-SMES to minimize the voltage and frequency fluctuations during the operation of PPL.

Nowadays, human emotion recognition is a mandatory task for many human machine interaction fields. This paper proposes a novel multi-modal human emotion recognition framework. The proposed scheme utilizes first the 3D-Convolutional Neural Network (3D-CNN) deep learning architecture for extracting the spatio-temporal features from the electroencephalogram (EEG) signals, and the video data of human faces. Then, a combination of data augmentation, ensemble learning techniques is proposed to get the final fusion predictions. The fusion of the multi-modalities in the proposed scheme is carried out using data, and score fusion methods. Hence, three human recognition approaches are built to achieve the proposed goal. They are namely EEG-based emotion recognition approach, face-based emotion recognition approach, and fusion-based emotion recognition approach. For the EEG approach, the 3D-CNN is used to get the final predictions of the EEG signal. For the face approach, the Mask-RCNN object detection technique in combination with OpenCV libraries are first utilized to extract the exact face pixels with emotional content. Then, the Support Vector Machine (SVM) classifier is utilized to classify the 3D-CNN output features of the face chunks. For the fusion-based emotion recognition approach, two fusion techniques are experimented; bagging, and stacking. It is found that the stacking technique gives the best accuracy, and achieves recognition accuracies 96.13%, and 96.79% for valence, and arousal classes respectively using the grid search ensemble learning technique due to transferring the weights from the EEG, and the face approaches to the fusion-based emotion recognition approach. The proposed approach outperforms recent works in multi-modal emotion recognition field.

This paper presents an effective solution to overcome the problem caused by intermittent energy sources that are connected to a balanced/unbalanced distribution system using a superconducting magnetic energy storage (SMES) system by mitigating the voltage and frequency fluctuations during wind gusts. The fuzzy logic control technique (FLC) is used with SMES to improve the voltage and frequency. A squirrel cage induction generator (SCIG) is applied as the wind energy generator. The IEEE 33-bus distribution system is used to validate the proposed method. Buses 18 33 are the weakest points in this system; thus, the wind and SMES systems are connected to the system at these buses. We used MATLAB/Simulink to simulate the performance of the IEEE 33-bus system (balanced/unbalanced) considering the SMES, wind system, and fuzzy logic control (FLC). The simulation results show the high performance of the proposed control method to alleviate the voltage and frequency fluctuation and achieve the power leveling strategy of the studied system.

Abstract This paper describes the integration of a photovoltaic (PV) renewable energy source with a superconducting magnetic energy storage (SMES) system. The integrated system can improve the voltage stability of the utility grid and achieve power leveling. The control schemes employ model predictive control (MPC), which has gained significant attention in recent years because of its advantages such as fast response and simple implementation. The PV system provides maximum power at various irradiation levels using the incremental conductance technique (INC). The interfaced grid side converter of the SMES can control the grid voltage by regulating its injected reactive power to the grid, while the charge and discharge operation of the SMES coil can be managed by the system operator to inject/absorb active power to/from the grid to achieve the power leveling strategy. Simulation results based on MATLAB/Simulink® software prove the fast response of the system control objectives in tracking the setpoints at different loading scenarios and PV irradiance levels, while the SMES injects/absorbs active and reactive power to/from the grid during various events to improve the voltage response and achieve power leveling strategy.