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All Journal International Journal of Engineering, Science and Information Technology Al-Istinbath: Jurnal Hukum Islam
Jawad, Haider Mahmood
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Religion Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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The Impact of Islamic Law on Human Rights Abuses in Conflict Zones Abduljabbar, Firas Meshhal; Ahmed, Sundus Serhan; Abdulameer, Nibras Arif; Jawad, Haider Mahmood; Umirbekovna, Kubeeva Zhanar
AL-ISTINBATH : Jurnal Hukum Islam Vol 9 No 2 (2024)
Publisher : Institut Agama Islam Negeri Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/jhi.v9i2.11165

Abstract

The article explores the interplay between international law and Islamic jurisprudence in mitigating human rights violations in conflict zones, emphasizing compliance and enforcement challenges. Given the persistent human rights abuses in crisis areas, ranging from torture to extrajudicial killings, this study highlights the role of international conventions and treaties in curtailing such breaches. Employing a mixed-methods approach, the study quantitatively evaluates human rights violations against international treaties and Sharia principles over the past 30 years, alongside qualitative case studies that consider geopolitical contexts and the influence of international organizations. Preliminary findings indicate that while some infractions have declined in nations that actively engage with international bodies and adhere to Sharia principles, others persist, particularly in regions lacking effective international oversight. The article concludes that both international and Islamic legal systems must reassess their frameworks to enhance human rights protections, as the enforcement of treaties and Sharia laws remains challenging in wartime contexts. Strengthening the integration of Islamic jurisprudence within international law may offer additional avenues for safeguarding human rights in these vulnerable areas.
Deep Reinforcement Learning-Based Control Architectures for Autonomous Maritime Renewable Energy Platforms Sabah, Sura; Hussain, Refat Taleb; Mohammed, Ismail Abdulaziz; Jawad, Haider Mahmood; Abbas, Intesar; Hariguna, Taqwa
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1209

Abstract

Autonomous vessels driven by renewable energy are increasingly envisioned as vital for sustainable ocean?operations such as environmental monitoring, offshore power generation, and long-haul unmanned surface vehicles. Implementing fine-scale control of these systems has proven challenging however,?due to time-varying sea-state dynamics, sporadic energy inputs, the possibility of failure at the component level, and the requirement for coordination between multiple agents. In the article, an end-to-end deep reinforcement learning-based hierarchical control solution with real-time navigation and?its synthesis for energy optimization is proposed. It combines high-level energy regulation with low-level actuator scheduling so as to react to the variations of?the environment and internal perturbations. Simulations using actual wave realizations, sensor failures, actuator outages, and network communication variation were used?to demonstrate the performance of the control system in the following 5 performance aspects: energy saving, navigation accuracy, communication reliability, fault tolerant and multi-agent coordination. Results indicate that the architecture sustained over 80% of the performance and achieved energy efficiencies up to 54.5% in the?best case under failure scenarios. Performance-measures demonstrated reasonable scalability?up to 5–7 agents without significant communication overhead. The findings support the applicability of deep reinforcement learning for real-time maritime control under uncertainty, offering a viable alternative to conventional rule-based or predictive control strategies. The framework’s modular design allows for future integration with federated learning, hybrid control models, or autonomous deployment. The article contributes to the growing field of intelligent marine systems by providing a robust and adaptable control strategy for sustainable and scalable operations in autonomous maritime environments.
Co-Authors Abbas, Intesar Abdulameer, Nibras Arif Abduljabbar, Firas Meshhal Ahmed, Sundus Serhan Hussain, Refat Taleb Mohammed, Ismail Abdulaziz Sabah, Sura Taqwa Hariguna Umirbekovna, Kubeeva Zhanar