2,859 publications from this institution
A promising sensitive phenolic chemical sensor was developed using a porous pentasil type framework of protonated H-ZSM-5 zeolite by the reliable <italic>I</italic>–<italic>V</italic> method.
Abstract Double cascade cyclization of arenyl tetraynes gives benzothiadiazole derivatives and related compounds.
Deep reinforcement learning (DRL) has been applied to the routing, modulation, and spectrum assignment (RMSA) in elastic optical networks (EON), enabling the learning of RMSA policies through interaction between a DRL agent and the EON environment. Existing approaches aim to make decisions that fulfill spectrum utilization requirements. For the quality of transmission (QoT) assurance, they rely on distance-dependent modulation selection. However, other factors such as physical impairments, spectrum fragmentation, and traffic dynamics can impact QoT. In this work, we introduce DeepRMSA-QoT (deep reinforcement learning for QoT-focused routing, modulation, and spectrum assignment), a technique using the DRL framework to explore efficient QoT-aware RMSA policies for EONs. We propose a state representation that includes QoT-level information. Additionally, two reward functions are developed alongside the basic one, incorporating the QoT information. Considering QoT during the development of state representation and reward functions can guide the agent to actions that meet the QoT requirements and minimize exploration blindness when performing RMSA. This ultimately leads to more efficient learning of improved policies. Furthermore, a QoT unit is designed to guarantee the QoT and to facilitate the implementation of proposed reward functions. Extensive simulation results demonstrate that our proposed approach outperforms the existing approaches. Our approach can identify lightpath feasibility based on QoT requirements, select feasible lightpaths to meet QoT of demands, and avoid infeasible ones. In addition, our approach achieves a lower blocking rate while provisioning high bit-rates with dynamic traffic demands in different topologies.
The title compound 2 is obtained as bright yellow metastable crystals, which on mechanical grinding change to the dark red stable form; unlike piezochromic compounds, the reverse colour change does not occur; X-ray crystallographic analyses shows that the yellow form has a folded structure while the red form has a twisted structure.
A new class of PANI/Sn(II)SiO3/FCNTs nanocomposite was synthesized by mixing polyaniline into the gel of Sn(II)SiO3 followed by FCNTs (Polyaniline/Sn(II)SiO3/Functionalized Carbon nanotubes). The physico-chemical characterization was carried out by scanning electron microscope, XRD (X-ray Diffraction), FTIR (Fourier Transform Infrared Spectroscopy), ultraviolet–visible spectroscopy, and simultaneous thermogravimetric analysis studies. The ion-exchange capacity (1.2 meq/g) and distribution studies were also determined to understand the ion-exchange capabilities. The DC electrical conductivity studies revile it in the range of 3–5 × 10−3 S/cm. On the basis of distribution studies, ion-selective membrane electrode was designed for Hg(II). The analytical utility of this membrane was established by using it as an indicator electrode in electrometric titrations.
The nine-membered fused-ring system of the title pyridazine derivative, C(13)H(12)N(4)O(4)S, is approximately planar (r.m.s. deviation 0.027 Å), and the benzene ring of the phenyl-sulfamide substituent is aligned at 43.5 (1)° to the fused-ring system. The amine group of the sulfonamide substituent forms an N-H⋯O hydrogen bond to the ketonic O atom of two neigboring mol-ecules to generate a chain running along the c axis.
