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Benchmarking Visit to UTM and UTEM for Open & Distance Learning (ODL) Program Development
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Benchmarking Visit to UTM and UTEM for Open & Distance Learning (ODL) Program Development

A delegation from Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) recently completed a successful benchmarking visit to Universiti Teknologi Malaysia (UTM) and Universiti Teknikal Malaysia Melaka (UTeM) from May 11 to May 14, 2024. The primary objective of this visit was to gather insights and best practices on Open & Distance Learning (ODL) to inform the development of UMPSA's own ODL program. 

Hajj Mubarak PSM UMPSA staff Assoc Prof Dr Mohd Sham Mohamad for year 2024M/1445H
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Hajj Mubarak PSM UMPSA staff Assoc Prof Dr Mohd Sham Mohamad for year 2024M/1445H

Assoc Prof Dr Mohd Sham Mohamad will be on leave for hajj from May 20 - July 14, 2024. May Allah's blessings enlighten your spirit and path, strengthen your faith, and bring your heart joy as you embark on this sacred voyage. May Allah be gracious to you and grant you safety and blessings on your journey.

RISKSmart Learning System Training 2024
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RISKSmart Learning System Training 2024

The RISKSmart Learning System Training for Kaneka’s RBI Team was held on May 16, 2024, at Kaneka Training Centre, Gebeng. The trainers are the RBI research team from the Centre for Mathematical Sciences, Assoc. Prof. Dr. Norhayati Binti Rosli, Mr. Khairul Hafiz Bin Khairuldin, Assoc. Prof. Dr. Noryanti Binti Muhammad, and Dr. Norhafizah Md Sarif. This training aims to equip Kaneka's RBI team with the skills to develop a scheme of inspection and perform risk analysis using the likelihood (probability) of failure due to flaws, damage, deterioration, or degradation, and the consequences of failure.

PSM UMPSA won GOLD Medal at ITEX’24
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PSM UMPSA won GOLD Medal at ITEX’24

The winning project, titled “Dynamic Learning System for BEV Charging Demand Prediction,” addresses a pressing issue in the transportation sector, which significantly impacts global emissions. The transition to battery electric vehicles (BEVs) is crucial for reducing emissions but poses challenges to national grids and electricity generation systems. This concern spurred Dr. Roslinazairimah and her team to create a software capable of accurately predicting electricity demand from the private BEV charging point. 

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