Lesser Himalayan sequences in Eastern Himalaya and their deformation:Implications for Paleoproterozoic tectonic activity along the northern margin of India

Substantial part of the northern margin of Indian plate is subducted beneath the Eurasian plate during the Caenozoic Himalayan orogeny, obscuring older tectonic events in the Lesser Himalaya known to host Proterozoic sedimentary successions and granitic bodies. Tectonostratigraphic units of the Proterozoic Lesser Himalayan sequence （LHS） of Eastern Himalaya, namely the Daling Group in Sikkim and the Bomdila Group in Arunachal Pradesh, provide clues to the nature and extent of Proterozoic passive margin sedimentation, their involvement in pre-Himalayan orogeny and implications for supercontinent reconstruction. The Daling Group, consisting of flaggy quartzite, meta-greywacke and metapelite with minor mafic dyke and sill, and the overlying Buxa Formation with stromatolitic carbonate-quartzite- slate, represent shallow marine, passive margin platformal association. Similar lithostratigraphy and broad depositional framework, and available geochronological data from intrusive granites in Eastern Himalaya indicate strikewise continuity of a shallow marine Paleoproterozoic platformal sequence up to Arunachal Pradesh through Bhutan. Multiple fold sets and tectonic foliations in LHS formed during partial or complete closure of the sea/ocean along the northern margin of Paleoproterozoic India. Such deformation fabrics are absent in the upper Palaeozoic-Mesozoic Gondwana formations in the Lesser Himalaya of Darjeeling-Sikkim indicating influence of older orogeny. Kinematic analysis based on microstructure, and garnet composition suggest Paleoproterozoic deformation and metamorphism of LHS to be distinct from those associated with the foreland propagating thrust systems of the Caenozoic Himalayan collisional belt. Two possibilities are argued here： （1） the low greenschist facies domain in the LHS enveloped the amphibolite to granulite facies domains, which were later tectonically severed; （2） the older deformation and metamorphism relate to a Pacific type accretionary orogen which affected the northern margin of greater India. Better understanding of geodynamic evolution of the northern margin of India in the Paleoproterozoic has additional bearing on more refined model of reconstruction of Colllrnhia.

Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone

In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach,this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a smartphone.First,three driving modes were developed for maize sowing scenarios:manual driving assisted driving and unmanned driving.While sowing,smartphone software and CAN(Controller Area Network)storage devices collected both positional data and engine operating conditions.Second,the tractor trajectory points were divided into kinematic sequences,with six driving cycle indicators built in each series based on the time window.Based on the semantic information of the kinematic sequences,the three operations of sowing,seeds filling,and turning round were well recognized.Last,a model for maize sowing fuel consumption forecast was advanced using the principal component analyses and random forest algorithm,regarding three factors:driving cycles,operating behaviors,and driving patterns.When compared to the traditional K-means algorithm,the results demonstrated that the harmonic mean of the precision and recall(F1 score)of sowing behavior recognition,seeds filling behavior recognition,and turning behavior recognition were enhanced by 2.06%,8.99%,and 21.79%,respectively.In terms of the impacts of driving modes and operating behaviors on fuel consumption,assisted driving mode had the lowest fuel usage for both sowing and turning behavior.Therefore,assisted driving is the most fuel-efficient mode for maize sowing.Combining the three driving modes,the relative error of the fuel consumption prediction model was 0.11 L/h,with the manual driving mode having the lowest relative error at 0.09 L/h.This research method lays the foundation for the optimization of tractor operation behavior,the selection of tractor driving mode,and the fine management of tractor fuel consumption.