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The harmonized procedures in terms of the sampling, sample treatment and identification of microplastics in different environmental samples are missing, which poses challenges to researchers to compare the results or to adopt ‘the most effective’ monitoring approach. Furthermore, in the related literature, the used procedures are rarely tested with spiked microplastics to predetermine their recovery rates. Without this knowledge, results should only be discussed as rough estimations of the real environmental concentrations of microplastics. In this study, six different methods previously used in microplastic studies of different media were tested with municipal wastewater and digested sludge samples, spiked with seven different types of plastic particles and fibres. Recovery rates, time consumption, advantages and disadvantages were assessed and most suitable treatment procedures (i.e. high recovery rates in short amount of time) were chosen for both wastewater and sludge. Suitability of staining with Rose Bengal was examined together with most efficient methods, but it did not improve the recovery of microplastics. In addition, the possible impacts of the treatments for identification with micro-Raman and FTIR microscope were assessed. Filtration with size fractioning was found to be the best method for both wastewater and sludge samples, with recovery rates of spiked microplastics around 91.4% and 92.9%, respectively.

An attention-aware patch-based deep-learning model for a blind 360-degree image quality assessment (360-IQA) is introduced in this paper. It employs spatial attention mechanisms to focus on spatially significant features, in addition to short skip connections to align them. A long skip connection is adopted to allow features from the earliest layers to be used at the final level. Patches are properly sampled on the sphere to correspond to the viewports displayed to the user using head-mounted displays. The sampling incorporates the relevance of patches by considering (i) the exploration behavior and (ii) a latitude-based selection. An adaptive strategy is applied to improve the pooling of local patch qualities to global image quality. This includes an outlier score rejection step relying on the standard deviation of the obtained scores to consider the agreement, as well as a saliency to weigh them based on their visual significance. Experiments on available 360-IQA databases show that our model outperforms the state of the art in terms of accuracy and generalization ability. This is valid for general deep-learning-based models, multichannel models, and natural scene statistic-based models. Furthermore, when compared to multichannel models, the computational complexity is significantly reduced. Finally, an extensive ablation study gives insights into the efficacy of each component of the proposed model.

Image quality assessment of 360-degree images is still in its early stages, especially when it comes to solutions that rely on machine learning. There are many challenges to be addressed related to training strategies and model architecture. In this paper, we propose a perceptually weighted multichannel convolutional neural network (CNN) using a weight-sharing strategy for 360-degree IQA (PW-360IQA). Our approach involves extracting visually important viewports based on several visual scan-path predictions, which are then fed to a multichannel CNN using DenseNet-121 as the backbone. In addition, we account for users’ exploration behavior and human visual system (HVS) properties by using information regarding visual trajectory and distortion probability maps. The inter-observer variability is integrated by leveraging different visual scan-paths to enrich the training data. PW-360IQA is designed to learn the local quality of each viewport and its contribution to the overall quality. We validate our model on two publicly available datasets, CVIQ and OIQA, and demonstrate that it performs robustly. Furthermore, the adopted strategy considerably decreases the complexity when compared to the state-of-the-art, enabling the model to attain comparable, if not better, results while requiring less computational complexity.

Integrating plasmonic nanoparticles into the photoactive metal-organic matrix is highly desirable due to the plasmonic near field enhancement, complementary light absorption, and accelerated separation of photogenerated charge carriers at the junction interface. The construction of a well-defined, intimate interface is vital for efficient charge carrier separation, however, it remains a challenge in synthesis. Here we synthesize a junction bearing intimate interface, composed of plasmonic Ag nanoparticles and matrix with silver node via a facile one-step approach. The plasmonic effect of Ag nanoparticles on the matrix is visualized through electron energy loss mapping. Moreover, charge carrier transfer from the plasmonic nanoparticles to the matrix is verified through ultrafast transient absorption spectroscopy and in-situ photoelectron spectroscopy. The system delivers highly efficient visible-light photocatalytic H 2 generation, surpassing most reported metal-organic framework-based photocatalytic systems. This work sheds light on effective electronic and energy bridging between plasmonic nanoparticles and organic semiconductors. The integration of plasmonic structures with photoactive matrices offers a promising means to enhance solar-to-fuel conversion. Here, the authors bridge plasmonic nanoparticles and metal-organic matrix through interface-engineering to boost photocatalytic hydrogen evolution.

In this paper, we propose a novel approach for face spoofing detection using a combination of color texture descriptors with a new convolutional neural network (CNN) architecture. The proposed approach is based on a new convolutional neural network architecture composed of two CNN parallel branches. The first branch is fed with complementary shallow local phase quantization (LPQ) invariant descriptors that result from joint color texture information from the hue, saturation, and value (HSV) color space to accurately capture the reflection properties of the face. Combining the HSV color space with LPQ is known to significantly improve performance. The second branch of the CNN takes an RGB image directly as input, effectively separating chromatic (color-related) information from achromatic (brightness-related) information in order to extract crucial facial color features. Each branch of the CNN produces a vector of deep features that are extracted. To effectively concatenate the deep features from the two output branches, we employ an attention mechanism based combination method. This method captures the complementarity of the two branches, improving the accuracy and robustness of the model. The combined feature vectors form an input vector for the next Dense layer, where the model can distinguish between live and spoofed faces. Our method detects 2D facial spoofing attacks involving printed photos and replayed videos. We showcase the effectiveness and superior performance of our approach through a series of experiments conducted on both the CASIA-FASD and Replay-Attack datasets. Our results are promising and surpassing those of other state-of-the-art methods on both used datasets in terms of 9 performance metrics.

This study aims to optimize the removal of carbon and nitrogen pollutants from saline municipal wastewater using both membrane-based and biological treatment methods. It examines a pilot-scale sequential aerobic ceramic membrane bioreactor (AeCMBR) under various salinity levels (0–20 g NaCl/L) to assess biological processes and fouling behavior. While high COD removal rates of (≈90%) were consistently achieved, ammoniacal nitrogen removal dropped from 82 to 55% at 15 g NaCl/L, despite increased oxygenation flow rates. Notably, the biomass quickly adapted to salinity changes. Indicators such as mixed liquor suspended solids (MLSS), mixed liquor suspended volatiles (MLVSS), MLVSS/MLSS ratio, and sludge volume index (SVI) showed no significant correlation with increasing salt concentrations. Soluble microbial product (SMP) production was also unaffected by rising salinity levels. The transmembrane pressure (TMP) fluctuated, with the most pronounced trend at 15 g NaCl/L, even after reducing the flux from 20 to 15 L/m2/h. The primary fouling mechanism observed was reversible cake deposition. Overall, this research enhances our understanding of short-term operational impacts on AeCMBR performance as a function of different salinity levels.

Located in a semi-arid to arid region, Morocco is confronting increasing water scarcity challenges. In the circular economy paradigm, the reuse of treated wastewater in agriculture is currently considered a possible solution to mitigate water shortage and pollution problems. In recent years, Morocco has made significative progress in urban wastewater treatment under the National Wastewater Program (PNA). However, rural sanitation has undergone significant delays. Therefore, an alternative technology for wastewater treatment and reuse in rural areas is investigated in this review, considering the region’s economic, social, and regulatory characteristics. Constructed wetlands (CWs) are a simple, sustainable, and cost-effective technology that has yet to be fully explored in Morocco. CWs, indeed, appear to be suitable for the treatment and reuse of wastewater in remote rural areas if they can produce effluent that meets the standards of agricultural irrigation. In this review, 29 studies covering 16 countries and different types of wastewater were collected and studied to assess the treatment efficiency of different types of CWs under different design and operational parameters, as well as their potential application in agricultural reuse. The results demonstrated that the removal efficiency of conventional contamination such as organic matter and suspended solids is generally high. CWs also demonstrated a remarkable capacity to remove heavy metals and emerging contaminants such as pharmaceuticals, care products, etc. The removal of microbial contamination, on the other hand, is challenging, and does not satisfy the standards all the time. However, it can be improved using hybrid constructed wetlands or by adding polishing treatment. In addition, several studies reported that CWs managed to produce effluent that met the requirements of wastewater reuse in agriculture of different countries or organisations including Morocco.

We combine ultrafast electron diffraction and time-resolved terahertz spectroscopy measurements to link structure and electronic transport properties during the photoinduced insulator–metal transitions in vanadium dioxide. We determine the structure of the metastable monoclinic metal phase, which exhibits antiferroelectric charge order arising from a thermally activated, orbital-selective phase transition in the electron system. The relative contribution of the photoinduced monoclinic and rutile metals to the time-dependent and pump-fluence–dependent multiphase character of the film is established, as is the respective impact of these two distinct phase transitions on the observed changes in terahertz conductivity. Our results represent an important example of how light can control the properties of strongly correlated materials and demonstrate that multimodal experiments are essential when seeking a detailed connection between ultrafast changes in optical-electronic properties and lattice structure.

Conversion of clean solar energy to chemical fuels is one of the promising and up-and-coming applications of metal–organic frameworks. However, fast recombination of photogenerated charge carriers in these frameworks remains the most significant limitation for their photocatalytic application. Although the construction of homojunctions is a promising solution, it remains very challenging to synthesize them. Herein, we report a well-defined hierarchical homojunction based on metal–organic frameworks via a facile one-pot synthesis route directed by hollow transition metal nanoparticles. The homojunction is enabled by two concentric stacked nanoplates with slightly different crystal phases. The enhanced charge separation in the homojunction was visualized by in-situ surface photovoltage microscopy. Moreover, the as-prepared nanostacks displayed a visible-light-driven carbon dioxide reduction with very high carbon monooxide selectivity, and excellent stability. Our work provides a powerful platform to synthesize capable metal–organic framework complexes and sheds light on the hierarchical structure-function relationships of metal–organic frameworks. Homojunctions are very promising in photocatalysis, but challenging to achieve. Herein, authors report a well-defined hierarchical metal–organic framework-based homojunction, formed via a one-pot synthesis route directed by hollow transition metal nanoparticles, as photocatalysts for CO 2 reduction.

Description of redox reactions is critically important for understanding and rational design of materials for electrochemical technologies, including metal-ion batteries, catalytic surfaces, or redox-flow cells. Most of these technologies utilize redox-active transition metal compounds due to their rich chemistry and their beneficial physical and chemical properties for these types of applications. A century since its introduction, the concept of formal oxidation states (FOS) is still widely used for rationalization of the mechanisms of redox reactions, but there exists a well-documented discrepancy between FOS and the electron density-derived charge states of transition metal ions in their bulk and molecular compounds. We summarize our findings and those of others which suggest that density-driven descriptors are, in certain cases, better suited to characterize the mechanism of redox reactions, especially when anion redox is involved, which is the blind spot of the FOS ansatz.