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Computer Vision has played a major role in Intelligent Transportation Systems (ITS) and traffic surveillance. Along with the rapidly growing automated vehicles and crowded cities, the automated and advanced traffic management systems (ATMS) using video surveillance infrastructures have been evolved by the implementation of Deep Neural Networks. In this research, we provide a practical platform for real-time traffic monitoring, including 3D vehicle/pedestrian detection, speed detection, trajectory estimation, congestion detection, as well as monitoring of the interaction of vehicles and pedestrians, all using a single CCTV traffic camera. We adapt a custom YOLOv5 deep neural network model for vehicle/pedestrian detection and an enhanced SORT tracking algorithm. For the first time, a hybrid satellite-ground based inverse perspective mapping (SG-IPM) method for camera auto-calibration is also developed which leads to an accurate 3D object detection and visualisation. We also develop a hierarchical traffic modelling solution based on short- and long-term temporal video data streams to understand the traffic flow, bottlenecks, and risky spots for vulnerable road users. Several experiments on real-world scenarios and comparisons with state-of-the-art are conducted using various traffic monitoring datasets, including MIO-TCD, UA-DETRAC and GRAM-RTM collected from highways, intersections, and urban areas under different lighting and weather conditions.Computer Vision has played a major role in Intelligent Transportation Systems (ITS) and traffic surveillance. Along with the rapidly growing automated vehicles and crowded cities, the automated and advanced traffic management systems (ATMS) using video surveillance infrastructures have been evolved by the implementation of Deep Neural Networks. In this research, we provide a practical platform for real-time traffic monitoring, including 3D vehicle/pedestrian detection, speed detection, trajectory estimation, congestion detection, as well as monitoring the interaction of vehicles and pedestrians, all using a single CCTV traffic camera. We adapt a custom YOLOv5 deep neural network model for vehicle/pedestrian detection and an enhanced SORT tracking algorithm. For the first time, a hybrid satellite-ground based inverse perspective mapping (SG-IPM) method for camera auto-calibration is also developed which leads to an accurate 3D object detection and visualisation. We also develop a hierarchical traffic modelling solution based on short- and long-term temporal video data streams to understand the traffic flow, bottlenecks, and risky spots for vulnerable road users. Several experiments on real-world scenarios and comparisons with state-of-the-art are conducted using various traffic monitoring datasets, including MIO-TCD, UA-DETRAC and GRAM-RTM collected from highways, intersections, and urban areas under different lighting and weather conditions. Video: https://youtu.be/FdiQ_EGbZe0

Wastewater management is one of the main hurdles encountered by the shale gas industry for boosting overall process cost-effectiveness while reducing environmental impacts. In this light, this paper introduces a new multi-objective model for the thermo-economic and environmental optimization of solar-based zero-liquid discharge (ZLD) desalination systems. The solar-driven ZLD system is especially developed for desalinating high-salinity wastewaters from shale gas process. A decentralized system is proposed, encompassing a solar thermal system, a Rankine power cycle, and a multiple-effect evaporator combined with mechanical vapor recompression. The environment-friendly ZLD operation is ensured by specifying the salt concentration of brine discharges close to saturation conditions. The mathematical modelling approach is centered on a multi-objective non-linear programming (MoNLP) formulation, which is aimed at simultaneously minimizing thermo-economic and environmental objective functions. The latter objective function is quantified by the ReCiPe methodology based on life cycle assessment. The MoNLP model is implemented in GAMS software, and solved through the epsilon-constraint method. A set of trade-off Pareto-optimal solutions is presented to support decision-makers towards implementing more sustainable and cost-efficient solar-driven ZLD desalination systems. The comprehensive energy, economic and environmental analysis reveals that the innovative system significantly decreases costs and environmental impacts in shale gas wastewater operations. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 640979.

Ensemble-based simulations of future shoreline evolution to 2100, including sea-level rise driven erosion, are performed and analysed  Future shoreline projections uncertainties are initially controlled by modelling assumptions and after 2060 by sea-level rise uncertainties  The choice of wave-driven equilibrium modelling approach and incident wave chronology are critical to future shoreline projections 1 Abstract Most sandy coasts worldwide are under chronic erosion, which increasingly put at risk coastal communities. Sandy shorelines are highly dynamic and respond to a myriad of processes interacting at different spatial and temporal scales, making shoreline predictions challenging, especially on long time scales (i.e. decades and centuries). Shoreline modelling inherits uncertainties from the primary driver boundary conditions (e.g. sea-level rise and wave forcing) as well as uncertainties related to model assumptions and/or misspecifications of the physics. This study presents an analysis of the uncertainties associated with future shoreline evolution at the high-energy, cross-shore transport dominated, sandy beach of Truc Vert (France) over the 21 st century. We explicitly resolve wave-driven shoreline change using two different equilibrium modelling approaches to provide new insight into the contributions of sea-level rise, and free model parameters uncertainties on future shoreline change in the frame of climate change. Based on a Global Sensitivity Analysis, shoreline response during the first half of the century is found to be mainly sensitive to the equilibrium model parameters, with the influence of sea-level rise emerging in the second half of the century (~2050 or later), in both Representative Concentration Pathways 4.5 and 8.5 scenarios. The results reveal that the seasonal and interannual variability of the predicted shoreline position is sensitive to the choice of the wave-driven equilibrium based model. Finally, we discuss the importance of the chronology of wave events in future shoreline change, calling for more continuous wave projection time series to further address uncertainties in future wave conditions.

Multisource approaches in taxonomy gather different lines of evidence in order to draw strongly supported taxonomic conclusions and constitute the basis of integrative taxonomy. In the case of overlooked taxa with disjunct distributions for which sampling is more challenging, integrative approaches help to propose stable hypotheses at the species and subspecies levels. Here, based on genetic and semio-chemical traits, we performed an integrative taxonomic analysis to evaluate species delimitation hypotheses within a monophyletic group of bumblebees (Hymenoptera, Apidae, Bombus) including the formerly recognised subgenera Eversmannibombus, Laesobombus and Mucidobombus which are now included in the subgenus Thoracobombus. Our results demonstrate the conspecificity of several polytypic taxa, and we formally recognise the subspecies Bombus laesus aliceae comb. nov. Cockerell, 1931, endemic to North Africa, based on its allopatry, unique mitochondrial haplotype and divergent cephalic labial gland secretions. This highlights the need to maintain studying polytypic complexes of bumblebee taxa for which phylogenetic relationships could be still entangled and eventually implement conservation strategies for taxonomically differentiated lineages. International audience

The Ab-Bagh clastic- and carbonate-hosted stratabound and stratiform Zn-Pb deposit in the Sanandaj-Sirjan zone, in the southeastern corner of the Malayer-Esfahan metallogenic belt of Iran, is typical of several sedimentary–exhalative (SEDEX) deposits in this metallogenic belt. The deposit is hosted in a late Jurassic-early Cretaceous sedimentary sequence. Based on position, there are two Zn-Pb-bearing stratigraphic ore horizons. Ore horizon 1 is hosted by late Jurassic-early Cretaceous black shale and siltstone. The wedge-shaped ore body is located close to a synsedimentary normal fault. Petrographic studies indicates that mineralization comprises three sulfide ore facies: stockwork, bedded and massive ore facies. Ore horizon 2 occurs in early Cretaceous carbonates and comprises a massive-replacement ore facies that is concordant with host rock layering; it is also underlain by a stockwork facies. Textures include framboidal, laminated, breccia, replacement, massive and vein-veinlet. Hypogene sulfide minerals are predominantly sphalerite, galena, pyrite and chalcopyrite. Calcite and quartz are major gangue minerals. Silicification and carbonatization are the main wallrock alteration styles; alteration intensity increases towards the feeder zone. Based on relationships between ore minerals and rock-forming minerals, sulfide mineralization in the Ab-Bagh deposit formed during two main stages: fine-grained sulfide bands (stage I) are intricately interlayered with organic matter-rich beds of black shale and siltstone. Sulfide bands exhibit classic sedimentary textures, such as laminations and bedding, indicative of a synsedimentary origin. Coarser-grained stage II base metal sulfides show breccia and vein-veinlet textures, and are considered to have formed by replacement during sub-seafloor fluid flow. Fluid inclusion microthermometry was carried out on calcite associated with sulfides of the stockwork ore facies. Homogenization temperatures are in the range of 180–301¿°C, and the average salinity is 17.93¿wt% NaCl eq. The size distribution of pyrite framboids of the bedded ore, geochemistry of host rock and presence of organic matter, collectively suggest anoxic to locally suboxic ambient conditions for the host basin. The d34S composition of pyrite, sphalerite, and galena ranges from -5.08 to +4.51‰. The highest d34S values are from massive and stockwork ore facies (+2.66 to¿+¿4.51‰) and the lowest from bedded ore (-5.08 to -4.72‰). In the bedded ore facies, the main sulfur source was bacterially reduced seawater sulfate. Whereas in massive and stockwork ore facies, thermochemically reduced seawater sulfate was the main source of sulfur. The 87Sr/86Sr ratios of calcite indicate that origin of ore forming fluids is Paleozoic radiogenic basement. Collectively, the isotopic, sedimentological, textural, mineralogical, fluid inclusion, and geochemical data for the Ab-Bagh deposit support a vent-proximal sub-seafloor replacement SEDEX ore deposit. This research was made possible by the help of the office of vice-chancellor for Research and Technology, Tarbiat Modares University (TMU) of Tehran. Strontium isotope analyses were sponsored by the National Natural Science Foundation of China (91855214, 41773043, 41320104004, 41773042), and conducted by Yu-cai Song at the Institute of Geology, Chinese Academy of Geological Sciences, Beijing, China. Sulfur isotope analyses were funded by the Catalan Government project 2014-SGR-1661. Comments and suggestions from Prof. Hooshang Asadi Haroni and Prof. Jan Matthias Peter greatly improved the quality of the paper. Peer Reviewed

34% of all days) of which 55 showed a decreasing mode diameter after growth. The NPF event occurrence was higher in summer than in winter, and events were accompanied with air mass back trajectories crossing over the Eastern Mediterranean. The mean nucleation rate (J10) was 1.9 ± 8.8 nm/h). The formation rate did not have a seasonal pattern, but the growth rate had a seasonal variation (maximum around August and minimum in winter). The mean condensable vapor source rate was 4.1 ± 105 molecules/cm3 s) with a seasonal pattern (maximum around August). The mean condensation sink was 8.9 ± We characterized new particle formation (NPF) events in the urban background of Amman during August 2016&ndash July 2017. The monthly mean of submicron particle number concentration was 1.2 × 3 (exhibited seasonal, weekly, and diurnal variation). Nucleation mode (10&ndash 1) with a seasonal pattern (minimum around June and maximum in winter). 3) around noon. We identified 110 NPF events (&asymp 3 during daytime with a sharp peak (1.1 × 1) and the mean growth rate was 6.8 ± 15 nm) concentration was 0.7 × 2.2 105 molecules/cm3 s (2.6&ndash 1 (monthly mean 1.6&ndash 3.1 nm/h (4.1&ndash 2.7 cm&minus 104 cm&minus 1.1 cm&minus 103 cm&minus 1 (6.4&ndash 14.8 × 1.1 × 3.7 × 6.9 × 1.8 × 3.3 × 104&ndash 3 s&minus 103&ndash 10&minus

This paper proposes an optimization framework to deal with the uncertainty in a day-ahead scheduling of smart active distribution networks (ADNs). The optimal scheduling for a power grid is obtained such that the operation costs of distributed generations (DGs) and the main grid are minimized. Unpredictable demand and photovoltaics (PVs) impose some challenges such as uncertainty. So, the uncertainty of demand and PVs forecasting errors are modeled using a hybrid stochastic/robust (HSR) optimization method. The proposed model is used for the optimal day-ahead scheduling of ADNs in a way to benefit from the advantages of both methods. Also, in this paper, the ac load flow constraints are linearized to moderate the complexity of the formulation. Accordingly, a mixed-integer linear programming (MILP) formulation is presented to solve the proposed day-ahead scheduling problem of ADNs. To evaluate the performance of the proposed linearized HSR (LHSR) method, the IEEE 33-bus distribution test system is used as a case study.