@_Kalyan_K@Flipkart@flipkartsupport the order (OD436549132452474100) is already delayed by 2 days.
Today as well, it’s still not out for delivery and there’s no clear confirmation. No proper response from customer care agents. Update me d status of order. attaching screenshots
@flipkartsupport Where is the actual resolution mentioned in the earlier conversations? I’m only receiving predefined, generic responses with no concrete update or solution.
@porterit_ new partner assigned is also not cooperating and is saying dat his vehicle has broken down. it hard to believe this explanation as it seems like both partners may be part of the same group. Overall dis entire exprnce has been very unprofessional &have lost trust in using Porter.
@porterit_ My scheduled pickup didn’t happen today because the partner's vehicle broke down, and no alternate option was arranged. I had to waste the full day and postpone my shifting. Requesting immediate attention. Order ID: PNM39205643
@porterit_ Earlier, 1st partner assigned to my booking demanded an extra ₹260 per carton box and claimed I would need around 15 boxes, totalling ₹3,900 . When I contacted customer support, they confirmed that no additional charges were required. Due to this, I requested a partner change.
We have been training deep learning models to predict various atmospheric variables. Although we lack the support to publish a paper on this project currently, sharing our findings can still greatly benefit the scientific community.
Background and Motivation
The sea surface temperature (SST) in the Arabian Sea has been rising over the years, which has been well-documented in several studies. This increase in SST has also been linked to a rise in cyclonic activity in the region (Arabian Sea emerging as a cyclone hotspot." Nature) . To address these changes, we focused on predicting SST in the Arabian Sea using deep learning models.
Methodology
We selected a spatial domain within the Arabian Sea and identified relevant physical atmospheric variables for SST prediction. Our models were trained using historical data spanning 20 years at six-hourly time steps. The SST predictions are validated through 12-hour ahead predictions, as illustrated in the accompanying images.
Results
The visualizations above demonstrate the actual and predicted SST for a 12-hour ahead validation timestep. Despite computational limitations, where all computations were run on a personal laptop, the data was bilinearly interpolated to optimize for efficient computational usage, serving as a proof of concept.
Conclusion
This deep learning model, even in its current state, shows promise for predicting SST 12 hours ahead. With further optimization and better computational resources, this model can become a robust tool for SST prediction. Additionally, such models can be extended to develop hybrid models for predicting various atmospheric processes using deep learning techniques.
Future Work
Future efforts will focus on optimizing the model for better performance and expanding the scope to include other atmospheric variables. By integrating these predictions, we can enhance our understanding and forecasting of atmospheric processes, contributing valuable insights to the scientific community.
@iitmpune@IMDWeather
Awful service by @amazonIN Ordered a Cello novelty on May 7 but received a damaged one. Raised 4 pickups,but no one showed up. Complained multiple times to customer care, still no response&refund. Faced similar issue with Amazon for the same product ordered on May 2
@cello_world
Awful service by @amazonIN Ordered a Cello novelty on May 7 but received a damaged one. Raised 4 pickups,but no one showed up. Complained multiple times to customer care, still no response&refund. Faced similar issue with Amazon for the same product ordered on May 2
@cello_world