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The agricultural spreading of dehydrated sewage sludge from urban sewage treatment plants is economically profitable provided that the soil agronomic quality and the absence of contamination, in particular of heavy metals, are maintained. We evaluated the variability of sludge between five treatment plants in northern Algeria. We determined parameters that account for their agronomic quality and total content of Ag, Cd, Co, Cr, Cu, Ni, Pb, Ti and Zn. The speciation of metals, which determines their bioavailability, was characterized by sequential extraction into five fractions easily exchangeable, acid-soluble, bound to carbonates and Fe-sulphides, bound to Fe-Mn oxides, bound to organic matter or sulphides, residual. All the sludges analysed showed satisfactory properties for plant growth. High total Ni contents for three of the sludges indicated that they were not landfillable under French or Chinese regulations. Ni, however, was contained in poorly bioavailable fractions and therefore presented a low risk to soils. In contrast, the total Cu was lower than the regulatory limit values, but mainly contained in very bioavailable fractions whose accumulation over time could reach toxic levels for plants over a period of 3 to 11 years depending on the sludges. These results showed that regulations are not adapted and must take into account the bioavailability with regard to the characteristics of the soils on which to spread. The speciation of metals in the sludge has also, on the one hand, made it possible to identify the zone of the sewerage network in which the sources of contamination must be sought and, on the other hand, has given indications on the possible nature of these sources.Metals (trace elements and rare earth elements, REEs) were analysed by inductively coupled plasma-mass spectrometry in blood, the liver, the kidney and muscle of ex situ spotted dogfish (Scyliorhinus stellaris). The controlled environment in which these elasmobranchs were hosted allowed to assess a baseline level of metals in the different organs since exposure via water and food can be easily monitored. The highest arsenic, chromium, copper, and iron values were found in the liver, cobalt in the kidney, and cadmium and rubidium in muscle. The highest total trace elements content was found in the trend liver (75 mg kg-1) > blood (33 mg kg-1) > muscle (31 mg kg-1) > kidney (10 mg kg-1), while the ΣREEs was the liver (30 μg kg-1) > muscle (15 μg kg-1) > kidney (13 μg kg-1) > blood (4.1 μg kg-1). Between REEs, the most represented element was scandium. Significant differences in the concentration of metals among organs were observed for almost all elements. Nonessential elements were generally lower and essential elements higher in the examined specimens compared to wild elasmobranchs, suggesting a close relationship between a balanced diet and animal welfare.This study applied multivariate statistical analysis (MSA) to synthetic data simulated by a river water quality model to verify whether the MSA can correctly infer the pollution scenario assigned in the river water quality model. The results showed that when assessing the number and possible locations of pollution sources based on the results of cluster analysis (CA), two instead of three pollution point source were identified when considering the hydraulic variations of surface water. When discussing the principal component analysis (PCA) result, the second principal component (PC2) and the Pearson correlation coefficients among the pollutants should also be considered, which can infer that Cu, Pb, Cr, and Ni are contributed by the same pollutant point source, and Cu is also influenced by another pollutant point source. This result also implies that the solid and liquid partition coefficients (Kd) of pollutants can affect the interpretation of the PCA results, so the Kd values should be determined before tracing the pollution sources to facilitate the evaluation of the source characteristics and potential targets. This study established a working framework for surface water pollution traceability to enhance the effectiveness of pollution traceability.Dinotefuran is a chiral insecticide widely used to control Nilaparvata lugens in agriculture. However, little is known about the toxic effects of dinotefuran enantiomers on aquatic organisms. In this study, zebrafish were exposed to 1.00 and 10.00 mg/L dinotefuran enantiomers for 96 h, after which multivariate pattern recognition, metabolite identification, and pathway analysis were performed. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were then conducted to reveal the metabolic perturbations caused by dinotefuran enantiomers. Metabolic pathway analysis revealed the perturbation of five main pathways, including phenylalanine, tyrosine and tryptophan biosynthesis; phenylalanine metabolism; retinol metabolism; arginine and proline metabolism; and glycerophospholipid metabolism. LY2584702 These disturbed metabolic pathways were strongly correlated with energy, amino acid metabolism, and lipid metabolism. Pathway analysis also indicated that the metabolic pathway changes induced by the same level of R and S-dinotefuran were enantioselective. Our research may provide better insight into the risk of chiral dinotefuran in aquatic organisms in the environment.Tunisia is among the developing countries that have taken initiatives to develop renewable energy and strengthen energy efficiency. Moreover, it has considerable potential, especially in the field of wind and solar energy. However, the country is still dependent on fossil fuel energy. In this context, the transition to renewable energy is considered one of the possible solutions to reduce energy dependence and strengthen the economy in general. Therefore, the aim of this research study is to evaluate the role of renewable energy in shaping the energy transition in Tunisia in order to qualify the possibilities of energy transition. Accordingly, we investigate the potential for substitution between the following factors and fuels Capital, labor, renewable, and non-renewable energy in Tunisia using a translog production function approach. Due to the multicollinearity of the model, the ridge regression method is used to estimate the parameters of the function. The obtained results showed that the possibility of substitution between inputs, especially between renewable and non-renewable energy, can replace fossil fuels with clean energy consumption. Moreover, to maximize the potential of renewable energy in Tunisia, this study recommends that policy makers should take more reliable measures to reflect the exact price of energy through price regulation measures, encourage investment in research and development, and introduce carbon taxes that could accelerate this transition.Demand for high forage production and quality has been increased markedly by development of animal husbandry in China. The lack of efficient planting regimes and key technologies greatly limits production of high-quality forage. Oat has become an important forage in animal husbandry in China due to its high nutritional value and forage yield as well as its great adaptation to harsh environment. To maximize oat forage production in an alpine region, we developed a new model of oat forage production known as two-sown regime, i.e., the first spring-sown and the second summer-sown, during a single growing season in an alpine region of Hulun Buir, Inner Mongolia Autonomous Region, China, using two early-matured oat species, Avena sativa (cv. Qinghai444, winner oat cultivar) and A. nuda (cv. Huazao2, spring oat cultivar). The key technologies and the underlying agronomic mechanisms were investigated across three experimental years of 2017-2019. The main results were as follows (1) dry weight yield, crude protein yield, and relative feed value of forage in the two-sown regime were significantly increased by 53.6%, 48.9%, and 70.6% relative to traditional one-sown regime across the 3 years, respectively; (2) forage production was mainly achieved by an increase in plant height at the first spring-sown; and (3) forage yield resulted mainly from an increase in tiller density by increasing seeding rate under no-tillage treatment in the second summer-sown. The key technologies of the two-sown regime were the first spring-sown at the soil thawing depth 10-13 cm, followed by the second summer-sown with increasing seeding rate under no-tillage treatment. These findings highlight that the two-sown regime of oat forage can be widely used as an effective planting regime to maximize forage production in large alpine regions of northern China as well as in regions with similar climates.Microplastics (MPs) have been defined as particles of size  less then  5 mm and are characterized by hydrophobicity and large surface areas. MPs interact with co-occurring hydrophobic organic contaminants (HOCs) via sorption-desorption processes in aquatic and terrestrial environments. Ingestion of MPs by living organisms may increase exposure to HOC levels. The key mechanisms for the sorption of HOCs onto MPs are hydrophobic interaction, electrostatic interaction, π-π interactions, hydrogen bonding, and Van der Waals forces (vdW). Polymer type, UV-light-induced surface modifications, and the formation of oxygen-containing functional groups have a greater influence on electrostatic and hydrogen bonding interactions. In contrast, the formation of oxygen-containing functional groups negatively influences hydrophobic interaction. MP characteristics such as crystallinity, weathering, and surface morphology affect sorption capacity. Matrix properties such as pH, ionic strength, and dissolved organic matter (DOM) also influence sorption capacity by exerting synergistic/antagonistic effects. We reviewed the mechanisms of HOC sorption onto MPs and the polymer and matrix properties that influence the HOC sorption. Knowledge gaps and future research directions are outlined.Growing international trade requires more flexible warehouse management to match it. In order to achieve more effective warehouse management efficiency, a shelf status-detection method based on deep learning is proposed. Firstly, the image acquisition of a multi-level shelf containing multiple bays is performed under different time and lighting conditions. Due to the difference in image characteristics between the bottom shelf on the ground and the upper shelf on the non-ground level, the collected images were divided into two groups floor images and shelf images; and the warehouse status recognition was performed on the two groups separately. The two sets of images are cropped and center projection transformed separately to obtain the region of interest. On this basis, the improved residual network model is used to construct different depot detection models for the two sets of images, respectively, and the above algorithm is verified by actual measurements. In this paper, 102,614 images of 3246 depots with different states of non-ground layer, and 27,903 images of ground layer are collected. They are divided into training set and test set according to the ratio of 41, and the accuracy of training set is 99.6%, and the accuracy of test set is 99.3%. The experimental outcomes provide a theoretical method and technical support for the intelligent warehouse system management.