Search Results (6)

This research introduces integrated spatio-temporal graph convolutional networks (ISTGCN), designed to capture complex spatiotemporal traffic data patterns. The proposed model integrates multi-layer graph convolutional networks (GCNs) to address dependencies in temporal and spatial traffic dynamics. Specifically, ISTGCN integrates graph convolutional layers and convolutional sequence learning layers within multiple spatiotemporal convolutional blocks. For capturing the temporal aspect, predictive graph modeling for road network traffic at particular time stamps is performed. To integrate the spatial information, graph convolution operations are applied. The proposed model was validated on real-life datasets, and the experimental results demonstrate that ISTGCN achieves significantly lower error values across key metrics—RMSE, MAE, and MAPE. Full article

As various regions around the world implement carbon taxes, we assert that the competitiveness of steel products in the marketplace will shift according to individual manufacturers’ ability to reduce CO₂ emissions as measured by cradle-to-gate Life Cycle Analysis (LCA). This study was performed by using LCA and cost estimate research to compare the CO₂ emissions and the additional cost applied to the production of various decarbonized materials used in sheet for automotive industry applications using the bending stiffness-based weight reduction factor. The pre-pandemic year 2019 was used as a baseline for cost estimates. This paper discusses the future cost scenarios based on carbon taxes and hydrogen cost. The pathways to decarbonize steel and alternative materials such as aluminum and reinforced polymer composites were evaluated. Normalized global warming potential (nGWP) estimates were calculated assuming inputs from the current USA electricity grid, and a hypothetical renewables-based grid. For a current electricity grid mix in the US (with 61% fossil fuels, 19% nuclear, 20% renewables), the lowest nGWP was found to be secondary aluminum and 100% recycled scrap melting of steel. This is followed by the natural gas Direct Reduced Iron–Electric Arc Furnace (DRI-EAF) route with carbon capture and the Blast Furnace-Basic Oxygen Furnace (BF-BOF) route with carbon capture. From the cost point of view, the current cheapest decarbonized production route is natural gas DRI-EAF with Carbon Capture and Storage (CCS). For a renewable electricity grid (50% solar photovoltaic and 50% wind), the lowest GWP was found to be 100% recycled scrap melting of steel and secondary aluminum. This is followed by the hydrogen-based DRI-EAF route and natural gas DRI-EAF with carbon capture. The results indicate that, when applying technologies available today, decarbonized steel will remain competitive, at least in the context of automotive sheet selection compared to aluminum and composites. Full article

The escalating demand for the cost-effective synthesis of valuable fine chemicals has fueled the search for sustainable heterogeneous catalysts. Among these catalytic reactions, Knoevenagel condensation has emerged as a very demanding reaction due to its involvement in the synthesis of new C–C bond formation. Porous metal phosphates have attracted significant attention in catalysis due to their unique surface properties. In this study, we report the synthesis of a novel porous magnesium aluminum phosphate (MALPO) material through a hydrothermal template-free approach. MALPO exhibited very promising specific surface area and hierarchical porosity. Moreover, the plate-like morphology of the material can enhance the exposure of the catalytic sites located at the surfaces, leading to enhanced catalytic activity. MALPO demonstrated excellent catalytic performance, yielding a series of Knoevenagel products with up to 99% yield. Notably, the catalyst displayed remarkable recyclability, retaining its structural integrity throughout multiple reaction cycles. The findings highlight the potential of porous mixed-metal phosphates, exemplified by MALPO, as sustainable and efficient base catalyst for the synthesis of value-added chemicals, contributing to the growing demand of the chemical industry. Further investigations are warranted to explore their catalytic potential in diverse chemical transformations and optimize their performance for large-scale operations. Full article

Selective oxidation of organics over an efficient heterogeneous catalyst under mild liquid phase conditions is a very demanding chemical reaction. Herein, we first report the modification of the surface of mesoporous silica MCM-41 material by phosphate for the efficient incorporation of Ti(IV) in the silica framework to obtain highly ordered 2D hexagonal mesoporous material STP-1. STP-1 has been synthesized by using tetraethyl orthosilicate, triethyl phosphate, and titanium isopropoxide as Si, P, and Ti precursors, respectively, in the presence of cationic surfactant cetyltrimethylammonium bromide (CTAB) under hydrothermal conditions. The observed specific surface area and pore volume of STP-1 were 878 m²g⁻¹ and 0.75 ccg⁻¹, respectively. Mesoporous STP-1 has been thoroughly characterized by XRD, FT-IR, Raman spectroscopy, SEM, and TEM analyses. Titanium incorporation (Ti/Si = 0.006) was confirmed from the EDX analysis. This mesoporous STP-1 was used as a heterogeneous catalyst for the selective oxidation of styrene into benzaldehye in the presence of dilute aqueous H₂O₂ as an oxidizing agent. Various reaction parameters such as the reaction time, the reaction temperature, and the styrene/H₂O₂ molar ratio were systematically studied in this article. Under optimized reaction conditions, the selectivity of benzaldehyde could reach up to 93.8% from styrene over STP-1. Further, the importance of both titanium and phosphate in the synthesis of STP-1 for selective styrene oxidation was examined by comparing the catalytic result with only a phosphate-modified mesoporous silica material, and it suggests that both titanium and phosphate synergistically play an important role in the high selectivity of benzaldehyde in the liquid phase oxidation of styrene. Full article

Nowadays, it is crucial to have effective road traffic signal timing, especially in an ideal traffic light cycle. This problem can be resolved with modern technologies such as artificial intelligence, cloud and crowd computing. We hereby present a functional model named Cloud–Crowd Computing-based Intelligent Transportation System (CCCITS). This model aims to organize traffic by changing the phase of traffic lights in real-time based on road conditions and incidental crowdsourcing sentiment. Crowd computing is responsible for fine-tuning the system with feedback. In contrast, the cloud is responsible for the computation, which can use AI to secure efficient and effective paths for users. As a result of its installation, traffic management becomes more efficient, and traffic lights change dynamically depending on the traffic volume at the junction. The cloud medium collects updates about mishaps through the crowd computing system and incorporates updates to refine the model. It is observed that nature-inspired algorithms are very useful in solving complex transportation problems and can deal with NP-hard situations efficiently. To establish the feasibility of CCCITS, the SUMO simulation environment was used with nature-inspired algorithms (NIA), namely, Particle Swarm Optimization (PSO), Ant Colony Optimization and Genetic Algorithm (GA), and found satisfactory results. Full article

Calcium sulphate nanocomposite materials (CB) have been synthesised from plant charcoal. Crushed charcoal powder was heated to red hot over a Bunsen burner flame and produced a white material which has been isolated. The surface morphology of the material has been studied by Scanning Electron Microscopy (SEM) and the elements were analyzed by Energy Dispersion Spectroscopy (EDS). To explore the structural features of the materials X-Ray Diffraction (XRD) patterns were recorded. The material showed pronounced inhibitory effects against Streptococcus faecaelis, Bacillus subtilis, Klebsilla pneumoni, E. coli, Proteus vulgaris and Pseudomonas aeruginosa. Full article