Blog

New Delhi, June 12:Disha Publication , a leading name in India’s educational publishing space, has launched the Errorless 202 New Syllabus Chapter-wise & Topic-wise 25 Years JEE Main PYQ Series (2026–2002) for JEE 2027 aspirants to help students move beyond conventional question-solving and adopt a more structured preparation approach.

As competition intensifies and preparation strategies become increasingly data-driven, Previous Year Questions (PYQs) continue to play a central role in helping students understand exam patterns and question trends. However, most PYQ resources stop at practice. The Errorless 202 Series seeks to go a step further by integrating practice, revision, and testing within a single framework, enabling students to use PYQs as a more structured preparation tool.

Commenting on the launch, Avinash Agarwal, Director, Disha Publication, said, “For more than two decades, we have observed how students prepare for competitive examinations and how their preparation evolves as the exam approaches. While PYQs remain one of the most effective preparation resources, many students use them only for practice. They often lack a structured way to revisit mistakes, identify weak areas, and measure progress over time. The Errorless 202 Series was created to address this gap by bringing together practice, revision, and testing within a single framework, helping students prepare in a more organised and purposeful manner.“

The series follows a three-step Practice-Revision-Testing (PRT) framework. Students can work through 25 years of JEE Main questions arranged chapter-wise and topic-wise, supported by detailed solutions and separate sections for MCQs and Numerical Value Questions. A key differentiator is the Self-Grading Sticker System, which allows students to mark questions that require revision or those they find particularly challenging, creating a personalised revision repository as preparation progresses.

The series also includes three-level mini quizzes for self-assessment, NCERT Page Locators for quick concept revision, and last five years’ trend analysis to help students identify important topics. Aligned with the latest JEE syllabus, the series is designed to support students throughout their preparation journey rather than only during the practice phase.

The Errorless 202 New Syllabus Chapter-wise & Topic-wise 25 Years JEE Main PYQ Series is available on Amazon and Flipkart. Students can also purchase the series through the official Disha Publications website, where launch offers are available, including complimentary Physics Flashcards featuring 55 high-impact revision cards for Classes 11 and 12, designed around active recall techniques.

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.

Representational image

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.

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.