Author: Neel Achary

Odisha Govt Pulls Up Colleges Over Low Usage of Free Wi-Fi, Orders Immediate Corrective Measures

Bhubaneswar, June 11 (UDN): Concerned over the underutilisation of free Wi-Fi services in higher educational institutions, the Odisha Government has directed colleges and universities across the state to take immediate measures to increase student engagement with the facility.

The Higher Education Department observed that despite the availability of free internet access in campuses, daily data consumption by students remains significantly below expectations in several institutions. The government has expressed concern that the low usage is preventing students from fully benefiting from digital learning resources and online academic opportunities.

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As part of its digital education initiative, the state government had established free Wi-Fi infrastructure in 130 higher educational institutions to bridge the digital divide and provide students with seamless access to e-learning platforms, research materials, online journals and educational content.

In a recent communication, the department instructed college authorities to identify the factors responsible for the poor utilisation of the service and implement corrective measures without delay. Institutions have been asked to create greater awareness among students regarding the availability and benefits of the facility.

The Directorate of Higher Education (DHE) has recommended organising orientation programmes for both new and existing students, along with campus-wide awareness campaigns. Colleges have also been directed to prominently display Wi-Fi login details, registration procedures and usage guidelines at strategic locations such as libraries, laboratories, classrooms and student common areas.

To encourage meaningful use of the internet facility, faculty members have been advised to integrate digital learning tools into classroom teaching. Students should be encouraged to access online journals, e-books, open educational resources and Massive Open Online Courses (MOOCs) to enhance their academic learning and research capabilities.

Officials said improving the utilisation of free Wi-Fi services is crucial to achieving the government’s vision of creating digitally empowered campuses and ensuring that public investments in educational technology deliver maximum benefits to students.

AI-Powered Railway Control System for Efficient Urban Train Operation

Researchers develop a new reinforcement learning framework for precise, real-time, and sustainable automatic railway control

Urban railways play a key role in sustainable transportation systems. However, achieving precise and energy-efficient train control under diverse operating conditions of urban railways remains a challenge. Researchers from Japan have now developed a recurrent reinforcement learning framework for urban railway speed control that combines expert-guided learning and safety filtering. The proposed method demonstrated stable, real-time operation and energy-saving performance, highlighting its potential for next-generation automatic train operation systems.

As cities continue to expand,railways are expected to become an important component of urban mobility systems. Compared to automobiles and air transport, railways are highly energy efficient and produce relatively low environmental impacts and therefore form an important component of sustainable transportation systems. Thus, researchers have been exploring advanced train control technologies for further improvement of operational efficiency, passenger comfort, and punctuality while reducing energy consumption.

In this context, Professor Masafumi Miyatake from the Department of Engineering and Applied Sciences in collaboration with doctoral studentMingyu Lyu from Graduate School of Science and Technology, Sophia University,investigated a new AI-based framework for urban railway speed control. The findings were published in Volume 14 of IEEE Access on March 25, 2026.

Reinforcement learning is a branch of artificial intelligence (AI) which has attracted attention as a promising approach for automatic train operations as it enables an AI agent to learn optimal control strategies by interacting repeatedly with an environment and improving its behavior through trial and error. However, implementing this in railway operation presents several practical challenges including incomplete information about operating conditions, train inertia, braking delays, and strict safety requirements.

To overcome this, researchers developed a recurrent reinforcement learning framework based on Recurrent Soft Actor–Critic (RSAC) algorithm. RSAC is an AI method that enables train control systems to learn from past driving patterns and adapt to changing railway conditions over time.

“Our reinforcement learning-based algorithm was capable of adapting train driving control to a wide range of track and rolling stock conditions,” explains Prof. Miyatake.

Unlike conventional reinforcement learning methods,the proposed approach used arecurrent neural network that can retain information from previous train states. This allowed the system to capture time-based relationships associated with train operation, such as traction response delays, braking history, and inertial effects, thereby improving decision-making under partially observable conditions.The team also used a training approach that allowed the AI system to first learn from examples of expert driving behavior before learning on its own. By studying these optimized driving patterns early in training, the AI was able to learn faster, make better decisions, and develop more stable and efficient train control behavior.

Apart from this, the team also integrated a safety filter into the framework. The safety filter would override potentially unsafe control commands generated by the AI policy and ensure compliance with the operational constraints such as speed limits and braking feasibility. This mechanism also helped ensure safe operation when the learned policy encountered unfamiliar situations.

The proposed framework was evaluated through various simulations of urban railway operations over approximately two-kilometer sections between stations. The simulation environment was designed to closely resemble real-world railway conditions, including uphill and downhill tracks, changing speed limits, and required train arrival times.

Additionally, the proposed AI framework was also compared with several other widely used reinforcement learning algorithms. These included Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor–Critic (SAC), which are commonly used to train autonomous decision-making systems.

“Among all the methods tested, our developed frameworklearned faster and performed better than all tested methods,” explains Prof. Miyatake.

The research also demonstrated strong energy efficiency. While dynamic programming achieved the theoretical minimum energy consumption under ideal conditions, the developed system attained comparable performance while remaining suitable for real-time operation. Meanwhile, conventional reinforcement learning approaches exhibited less stable behavior and higher energy consumption.

The findings suggest that this framework could help introduce a cleaner and more convenient public transportation by facilitating accurate and energy-efficient train operation at relatively low cost. The study refines the advantages of railways as an energy-efficient and environmentally friendly transportation system contributing towards a sustainable future. Looking ahead, the study may contribute to enhancing the role of railways as a sustainable transportation system—supporting climate change mitigation and sustainable urban development.

The findings also highlight the potential of AI-driven railway systems to support safer, greener, and more reliable urban transportation in future smart cities.

RMetS Weather Photographer of the Year 2026 Opens for Entries

The world’s favourite weather photography competition returns today as the Royal Meteorological Society (RMetS) officially opens entries for the Weather Photographer of the

Year 2026. Now entering its second decade, the competition invites photographers from around the globe to submit striking images that capture the power, beauty and impact of

weather. It celebrates outstanding photography that showcases both dramatic weather events and the growing influence of climate change.

The dedicated Climate Award also returns for 2026, recognising images that connect weather with wider environmental change. As a visual medium, photography offers a

powerful way to communicate complex scientific ideas and engage global audiences.

Prof Liz Bentley FRMetS, Chief Executive of the Royal Meteorological Society, said:

 “The Weather Photographer of the Year competition continues to showcase the remarkable ways in which weather shapes our world, from moments of natural beauty to events of significant impact. As our climate changes, these images play an increasingly important role in helping people understand and engage with the science behind weather and climate.”

The competition is free to enter and open to photographers of all levels, from seasoned professionals to enthusiastic amateurs.

The 2026 categories include:

 • Weather Photographer of the Year – awarded to the most striking image of weather in action (£1,500 prize).

 • Mobile Weather Photographer of the Year – celebrating the best images captured on a smartphone or tablet (£500 prize).

 • Climate Award – recognising photography that tells a compelling story about our changing climate (£500 prize).

 • Public Favourite – decided by a global public vote.

 Key Dates

• Entries open: 11 June – 20 August 2026

• Shortlist announcement and public vote: 22 October 2026

• Winners announced: 17 November 2026