Herein, a facile one-pot multicomponent protocol for the synthesis of biologically important pyrano[2,3-c]pyrazole and benzylpyrazolyl coumarin derivatives has been demonstrated using ZrO2 nanoparticles as reusable catalyst at room temperature.

Statistical analyses on π–π interactions among benzenoid dimers and pyridinoid dimers have been performed. The population distributions in these analyses are not in accordance with the theoretical study on benzene dimers and pyridine dimers. A geometrical correction has been proposed and incorporated into the statistical analysis, and then the population distributions have been compared with the theoretical models. The preferred interplanar angles, interplanar distances, offset distances, offset directions, and relative orientation of the pyridyl ring in the T-shaped geometry and relative orientation of the dipoles of two pyridyl rings have been evaluated from the geometry-corrected population distribution. Interestingly, all these parameters are in good agreement with the geometries predicted in a theoretical study. In addition, this analysis also reveals the presence of a small offset in the T-shaped geometry of pyridinoid dimers, which was overlooked in the theoretical study.

Geometrical correction, used in statistical analysis on interhalogen interactions, provides a new insight into the nature of halogens. Statistical analysis without geometrical correction shows preference for Type 1 interactions over Type 2 interactions, but the trend is reversed after geometrical correction. It is known that polarizability in F is very small and hence should not show much preference for any particular angle in C−F•••F−C interactions. Statistical analysis without geometrical correction could not prove it, but it is demonstrated here after geometrical correction. It is shown here that population after geometrical corrections vs θ1 and θ2 plots provides more meaningful information than the usually practiced population vs Δθ plot. The geometrical corrections proposed here are more general and can be used in several other interactions to eliminate geometrical bias from the statistical data in the process of extracting true chemical information from statistical analysis.

With the growing needs of data across the world, it almost hard to live without data a day. This is almost a blessing in disguise for the researchers working on different domains and dealing with huge amounts of data. The fog computing concept is aiming to change the scenarios created by cloud computing environments and also to make the data-centric clouds decentralized and localized. Fog devices contain only specific information that is frequently used in it and generally the size of the data in fog device is very less in comparison with cloud server. Fog stores only those data, which are frequently used by the users, rest it takes from the cloud itself. The motivation toward this field to make the data flow in various domain seamless and hassle free. Fog computing can be thought as an intermediate between the users and the cloud server, which connects end users easily and thus reduces the latency of the services. This paper discusses the three layered data flow architecture for fog computing and it proposes several novel architectures such as energy lattices, MediFog, UXFog, connected parking system, and FoAgro to utilize the concept of fog computing. K E Y W O R D Scloud computing, fog computing, security challenges

Three different approaches of statistical analyses, after surface area corrections, on the C–X···X–C (X = halogen) and C–H···X–C interactions have been performed. The analyses suggest that with respect to the accessible surface area on H and X, the C–H···X–C interactions are preferable over C–X···X–C interactions for all the halogens in the organic crystals. This is in contrast to the previous statistical analysis which stated that lighter halogens prefer C–H···X–C interactions whereas heavier halogens prefer C–X···X–C interactions. We have shown here the origin of the difference from the previous analysis. We also have shown here why it gives the impression that lighter halogens generally do not go for C–X···X–C interactions, but when they are replaced with the heavier halogens in the same molecule, the probability of formation of the C–X···X–C contacts increases. The preferred geometry of the C–X···X–C interactions with respect to the two ∠CXX angles and X···X distance have been shown. This study suggests that the high frequency of the C–X···X–C interactions for the heavier halogens is mainly due to their large surface area rather than their interaction strength.