Observation of inverse Compton emission from a long γ-ray burst.

Long-duration γ-ray bursts (GRBs) originate from ultra-relativistic jets launched from the collapsing cores of dying massive stars. They are characterized by an initial phase of bright and highly variable radiation in the kiloelectronvolt-to-megaelectronvolt band, which is probably produced within the jet and lasts from milliseconds to minutes, known as the prompt emission1,2. Subsequently, the interaction of the jet with the surrounding medium generates shock waves that are responsible for the afterglow emission, which lasts from days to months and occurs over a broad energy range from the radio to the gigaelectronvolt bands1-6. The afterglow emission is generally well explained as synchrotron radiation emitted by electrons accelerated by the external shock7-9. Recently, intense long-lasting emission between 0.2 and 1 teraelectronvolts was observed from GRB 190114C10,11. Here we report multi-frequency observations of GRB 190114C, and study the evolution in time of the GRB emission across 17 orders of magnitude in energy, from 5 × 10-6 to 1012 electronvolts. We find that the broadband spectral energy distribution is double-peaked, with the teraelectronvolt emission constituting a distinct spectral component with power comparable to the synchrotron component. This component is associated with the afterglow and is satisfactorily explained by inverse Compton up-scattering of synchrotron photons by high-energy electrons. We find that the conditions required to account for the observed teraelectronvolt component are typical for GRBs, supporting the possibility that inverse Compton emission is commonly produced in GRBs

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

Optimisation of design and operation of MSF desalination process using MINLP technique in gPROMS

NoOptimal design and operation of MSF desalination process is considered here using MINLP technique within gPROMS model builder 2.3.4. gPROMS provides an easy and flexible platform to build a process flowsheet graphically and the corresponding master model connecting automatically individual unit model equations during simulation and optimisation. For different freshwater demand throughout the year and with seasonal variation of seawater temperature, the total annualised cost of desalination is minimised. It is found that seasonal variation in seawater temperature results in significant variation in design and some of the operating parameters but with minimum variation in of process temperatures. The results also reveal the possibility of designing stand-alone flash stages which would offer flexible scheduling in terms of the connection of various units (to build up the process) and efficient maintenance of the units throughout the year as the weather condition changes. In addition, operation at low temperatures throughout the year will reduce design and operating costs in terms of low temperature materials of construction and reduced amount of anti-scaling and anti-corrosion agents

Neural network based correlations for estimating temperature elevation for seawater in MSF desalination process

NoModelling played an important role in simulation, optimisation, and control of multi-stage flash (MSF) desalination processes. Top brine temperature (TBT) is one of the many important parameters that affect optimal design and operation of MSF processes. Within the MSF process model, calculation of TBT is therefore important. For a given pressure, TBT is a function of boiling point temperature (BPT) at zero salinity and temperature elevation (TE) due to salinity. In this work, we develop several neural network (NN) based correlations for predicting TE. It is found that the NN based correlations can predict the experimental TE very closely. Also predictions by the NN based correlations were good when TE values, obtained using existing correlations from the literature are compared. Due to advancement of the microcomputer, plant automation becomes reliable means of plant maintenance. NN based correlations (models) can be updated in terms of new sets of weights and biases for the same architecture or for a new architecture reliably with new plant data

Automatic solar panel cleaning system based on Arduino for dust removal

Solar panel is vulnerable to accumulated dust on its surface. The efficiency of the solar panel gradually decreases because of dust accumulation. In this paper, an Arduino based solar panel cleaning system is designed and implemented for dust removal. The proposed solar panel cleaner is waterless, economical and automatic. Two-step mechanism used in this system consists of an exhaust fan which works as an air blower and a wiper to swipe the dust from the panel surface. a dc motor is used to power the wiper. Since, the system does not need water to clean solar panel, it avoids the wastage of water and effective in desert areas. Experimental results show that the proposed cleaning system can operate with an efficiency of 87-96% for different types of sand