Mapping Flood Co-Adaptation Strategies in a Vulnerable River Basin: Engaging Youth in Collaborative Decision-Making - Zulfaqar Sa’adi; Zainura Zainon Noor; Nur Syamimi Zaidi; Ee Ling Yong; Fateha Abdul Razak; Nurul Hana Mohamed; Zainab Mat Lazim; Ainul Syarmimi Rosli; Mohd Faiz Foze; Siti Hanna Elias; Khairunnisa Abdul Halim; Muhammad Burhanuddin Bahrodin; et al. (Environmental Management, 2026)

Synthesis, biological evaluation, and in silico studies of chalcone-based ketamine derivatives with preferential COX-2 inhibitory activity - Syed Muzzammil Masaud; Humaira Nadeem; Abida Shamim; Muhammad Kazim Zargaham; Usman Shareef; Sana Ayaz; Babar Murtaza (Medicinal Chemistry Research, 2025)

A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication - Muhammad Adil; Songzuo Liu; Suleman Mazhar; Ayman Alharbi; Honglu Yan; Muhammad Muzzammil (Journal of Marine Science and Engineering, 2025)
The underwater acoustic (UWA) communication system faces challenges due to environmental factors, extensive multipath spread, and rapidly changing propagation conditions. Deep learning based solutions, especially for orthogonal frequency division multiplexing (OFDM) receivers, have been shown to improve performance. However, the UWA channel characteristics are highly dynamic and depend on the specific underwater conditions. Therefore, these models suffer from model mismatch when deployed in environments different from those used for training, leading to performance degradation and requiring costly, time-consuming retraining. To address these issues, we propose a transfer learning (TL)-based pre-trained model for OFDM based UWA communication. Rather than training separate models for each underwater channel, we aggregate received signals from five distinct WATERMARK channels, across varying signal to noise ratios (SNRs), into a unified dataset. This diverse training set enables the model to generalize across various underwater conditions, ensuring robust performance without extensive retraining. We evaluate the pre-trained model using real-world data from Qingdao Lake in Hangzhou, China, which serves as the target environment. Our experiments show that the model adapts well to these challenging environment, overcoming model mismatch and minimizing computational costs. The proposed TL-based OFDM receiver outperforms traditional methods in terms of bit error rate (BER) and other evaluation metrics. It demonstrates strong adaptability to varying channel conditions. This includes scenarios where training and testing occur on the same channel, under channel mismatch, and with or without fine-tuning on target data. At 10 dB SNR, it achieves an approximately 80% improvement in BER compared to other methods.

Rosmarinic acid attenuates photophobia, hyperalgesia and neuroinflammation during nitroglycerine induced migraine like headache in mice - Maira Anwar; Ismail Badshah; Babar Murtaza; Syed Muzzammil Masaud; Umair Ilyas; Amber Kiyani; Danish Patoli; Muhammad Tariq Khan (Natural Product Research, 2025)

Impact of sustainable practices on knowledge sharing, relationship commitment, and sustainable performance - Muzzammil Wasim Syed; Huaming Song; Muhammad Junaid (Environment, Development and Sustainability, 2024)

Addressing the Directionality Challenge through RSSI-Based Multilateration Technique, to Localize Nodes in Underwater WSNs by Using Magneto-Inductive Communication - Gang Qiao; Aman Muhammad; Muhammad Muzzammil; Muhammad Shoaib Khan; Muhammad Owais Tariq; Muhammad Shahbaz Khan (Journal of Marine Science and Engineering, 2022)
The deployment and efficient use of wireless sensor networks (WSNs) in underwater and underground environments persists to be a difficult task. In addition, the localization of a sensor Rx node in WSNs is an important aspect for the successful communication with the aforementioned environments. To overcome the limitations of electromagnetic, acoustic, and optical communication in underwater and underground wireless sensor networks (UWSNs), magneto-inductive (MI) communication technology emerged as a promising alternative for usage in UWSNs with a wide range of applications. To make the magneto-inductive underwater wireless sensor networks (MI-UWSNs) more efficient, recently, various research studies focused on the optimization of the physical layer, MAC layer, and routing layer, but none of them has taken into account the effect of directionality. Despite the directionality issue posed by the physical nature of a magnetic field, the unique qualities of MI communication open up a gateway for several applications. The directionality issue of MI sensors is a critical challenge that must be taken into account while developing any WSN protocol or localization algorithm. This paper highlights and discusses the severity and impact of the directionality issue in designing a localization algorithm for magneto-inductive wireless sensor networks (MI-WSNs). A received signal strength indicator (RSSI)-based multilateration localization algorithm is presented in this paper, where a minimum of 2 and maximum of 10 anchor Tx nodes are used to estimate the position of the sensor Rx nodes, which are deployed randomly in a 15 m × 15 m simulation environment. This RSSI-based multilateration technique is the most suitable option that can be used to quantify the impact of directionality on the localization of a sensor Rx node.

Wheat productivity responses in the rice-based system under different no-till techniques and nitrogen sources - Muhammad Hamid Rafiq; Riaz Ahmad; Abdul Jabbar; Hassan Munir; Muzzammil Hussain (Environmental Science and Pollution Research, 2017)