الأستاذ الدكتور رعد زعلان حمود (Raad Z. Homod)

This study examines the seismotectonic framework of the Zagros Fold-Thrust Belt in northeastern Iraq and southeastern Turkey, focusing on Arabian-Eurasian plate convergence and regional seismicity. Using historical earthquake records (1900–2012, M ≥ 3.0), agency data, and moment tensor inversions from 32 stations, we identify five active fault systems: Khanaqin, Diyala, Şırnak, Hakkari, and a transboundary system into western Iran. Results show reverse and strike-slip faulting at depths of 14–28 km, driven by NW-SE compressional stresses. Moment tensor solutions, derived with region-specific Green's functions and a 1D velocity model, constrain fault kinematics and the stress field. Due to sparse seismic coverage in the Iraqi Zagros, this study relies on only six well-constrained events (Mw 3.5–4.9). Thus, findings are a preliminary, data-limited characterization of local fault kinematics. The scope is descriptive seismotectonic characterization and preliminary structural interpretation, rather than quantitative hazard assessment or full petroleum system analysis. Our integrated approach combines seismic analysis, focal mechanisms, satellite imagery, and historical data to provide a preliminary, data-constrained characterization of active fault kinematics in northeastern Iraq. While based on a limited dataset of six well-constrained events due to sparse regional network coverage, this study provides the first reliable focal mechanisms for the Iraqi Zagros and demonstrates a replicable methodology for future investigations. This work provides one of the first attempts to integrate moment tensor solutions with historical seismicity and structural data across this transboundary region, linking fault kinematics to seismotectonic characterization. Based on structural geometry, reactivated listric faults may influence hydrocarbon trap integrity, but this interpretation is speculative and requires validation by subsurface data.

In the renewable energy technology industry such as wind and solar power, artificial intelligence (AI) technology is rejuvenating by implementing accurate prediction, automatic control, and predictive maintenance of various types of energy technologies. It is crucial to improve the reliability and efficiency of solar, wind, hydropower, and geothermal plants. In this paper, a recent progress of AI applications on solar, wind, hydropower, geothermal, and biomass systems integration in renewable energy sector is surveyed. The novelty is to integrate the AI forecasting, management and hybrid modeling approaches into a unified framework, which is practically valuable for smart grid and green energy policy. Energy prediction accuracy is also improved using ML and DL methods. And, hybrid models mixing AI with physical systems can boost performance and slash operational costs. Such models are, particularly, applicable in predictive maintenance since they shorten the time equipment off line and extend the life of renewable energy devices, such as solar panels and wind turbines. AI in renewable energy has a few roadblocks: issues with data quality and demand for heavy computing (as well as interpretability in AI-guided decisions). Environmental considerations also need to be included, including automation-driven job loss and bias in AI predictions. The future of that progress also brings improved energy distribution and security, and modernized energy trading if harmonized with technologies such as IoT, and blockchain. Robust legislative parameters and the ability to build AI algorithms would be very helpful in addressing these issues and helping us move toward a sustainable low-carbon energy future. Finally, a systematic responsible AI integration framework is presented in the conclusion of this study that explains the model, optimizes data-energy together and harmonizes policies.

Often referred to as next-generation power system, smart grid is regarded as a revolutionary and progressive progression of current power grids. More significantly, smart grid is anticipated to significantly improve distributed intelligence, demand response, and the efficiency and dependability of future power systems with renewable energy sources by integrating cutting-edge computing and communication technology. Because millions of electronic devices are connected by communication networks across vital power facilities, cyber security becomes a crucial concern in addition to the silent aspects of smart grid. This directly affects the dependability of such a vast infrastructure. This study provided a thorough analysis of smart grid cyber security concerns. Then, recent Machine Learning-based detection techniques are summarized.

The Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) protocol is an international standard for machine-to-machine (M2M) data interchange which has been widely accepted in the semiconductor and industrial automation industries for real-time equipment control and monitoring. But there is currently no security model that offers both full-spectrum protection, as well as light-weight performance and total resilience to quantum era cyber-threats. Existing improvements, e.g., SECS/GEMsec, Secured SECS/GEM, and ES-SECS/GEM either depend on traditional cryptographic building blocks susceptible to Shor-, Grover-based attacks or ensure security of the data only partially, thereby exposing the critical IIoT infrastructure to replaying, DoS, and post-quantum cryptanalysis vulnerabilities. To close this gap, we introduce Post-SECS/GEM, a post-quantum secure solution based on a combination of CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication that maintains seamless compatibility with current message formats utilized within SECS/GEM. The suggested architecture embeds a lightweight, lattice-based security construction ensuring the fulfillment of fundamental SECS/GEM security functionalities (e.g., mutual authentication, and preserving authenticity, secrecy and integrity of messages over the same channel), with respect to an adversary who could have unlimited computation power but no quantum capability in order to attack communication between devices without modifying or upgrading existing protocols or hardware. Performance evaluation on a typical IIoT testbed indicates that Post-SECS/GEM achieves better efficiency as compared to the current SECS/GEM security extensions, with decreased key establishment and authentication latency, predictable encryption/decryption performance, as well as acceptable computing and memory overheads in line with edge-class industrial devices. These findings demonstrate that Post-SECS/GEM is an efficient post-quantum secure approach for real-time SECS/GEM-based industrial communication in the present IIoT scenarios. © 2020 IEEE.

Although renewable energy sources offer enormous potential to improve environmental sustainability, maximizing economic benefits inside microgrids requires resolving their intermittency and irregularity. A viable alternative is to combine energy storage with renewable energy technologies. This article introduced a energy management system for hybrid renewable power plants that includes fuel cells, wind turbines, solar cells, battery energy storage devices, and micro-turbines. Optimization problem is formulated as Hyper Learning Binary Dragonfly Algorithm (HLBDA) for optimizing economic benefits and with objectives of minimizing operating costs and pollutant gas emissions. Suggested model is compared with existing methods like Genetic Algorithms (GA), and Crayfish Optimization Algorithm (COA). Also, stochastic framework is considered suitable solution for achieving optimal operation point in microgrids to cope with uncertain parameters. According to the simulation results, suggested method proves reductions in overall system costs and pollutant gas emissions. The proposed system achieved significant superiority across all indicators. In the area of cost reduction, the algorithms demonstrated remarkable progress. The algorithms achieved significant improvements in cost reduction compared to genetic algorithm (GA). HLBDA algorithm achieved a 12.4% cost saving compared to GA, and the COA algorithm showed a 3.24% improvement in cost reduction. In the area of carbon emission reduction, the algorithms also showed significant progress: the HLBDA algorithm recorded the highest emission reduction rate at 9.54%, and the COA algorithm showed a 2.40% improvement in emission reduction. © 2026 Alhasnawi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Portable generators are widely used in Iraq to supply electricity during blackouts. However, they are noisy due to the engine’s combustion chamber and moving parts, which can negatively impact the neuroendocrine, cardiovascular, pulmonary, and digestive systems. In this work, the noise reduction of the generator using an acoustic enclosure made of local materials has been studied experimentally. The enclosure was made of medium-density fiber, galvanized iron, glass wool, cork, air gap, and compressed sponge. A 2-kW generator was tested for sound level in two scenarios: with and without multi-layered enclosure containing shredded plastic. The outcomes included determining the sound level and reduction in noise caused by the generator in decibels during the day and at night for various load conditions ranging from 0 to 25%, 50%, 75%, and 100% load, and at a distance 5 m, as well as measuring the thermal performance of the generator when the enclosure was applied. The enclosure filled with shredded plastic at 5 m reached the permissible limit according to Iraqi Law No. 41 of 2015, as the limit reached with a load of 25% is 63.3 dB during the day. It approached the permissible limit during the night with a load of 50%, which is 62.9 dB. The generator head cylinder’s temperature was below the 300 degrees Celsius upper limit permitted for air-cooled generators. The daytime temperature was 177.8 °C, and the nighttime temperature was 164.5 °C. This study introduces a cost-effective multi-layered enclosure using recycled plastic and natural materials to reduce generator noise by up to 13 dB, while maintaining safe operating temperatures. © The Author(s), under exclusive licence to Metrology Society of India 2026.

Addressing contaminated water from various industrial practices has become a pressing concern. Methylene Blue (MB) dye is a prevalent industrial pollutant used in printing, dyeing, textiles, paper, plastics, and leather production. This study employed an efficient, cost-effective, environmentally friendly, and abundant adsorbent to remove Methylene Blue. Bentonite has been utilized as an adsorbent under varying dosages, acidity (pH), agitation, and salinity of contaminated wastewater. The adsorption capacity is enhanced by increasing the surface area and pore volume of the bentonite particles when they are transformed into nanoparticles. The adsorption capability increased with higher doses (10–50 mg) and longer shaking times (10–40 min), as well as with the concentration of the contaminated dye (5–25 ppm), but it decreased with rising pH values (2−12). The impact of temperature on the adsorption process was examined within the range of 25–55 °C. The results indicated that the adsorption capability is largely unaffected by wastewater salinity up to 10,000 ppm. The maximum adsorption capacities achieved under optimal conditions were 24.25 mg/g for micro-bentonite (μB) and 40.75 mg/g for nano-bentonite (nB), respectively. FTIR was employed to examine the adsorption of methylene blue dye by bentonite. BET, BJH, T-plots, and AFM analyses were conducted to determine the surface area, pore volume, pore diameter, and mean particle diameters for micro and nano bentonite. The results correlated more accurately using the Freundlich isotherm compared to the Langmuir and Tempkin models, due to its superior regression value (R2). The most suitable kinetic model for this investigation was the pseudo-second-order, in contrast to the pseudo-first-order, Elovich, and intra-particle diffusion models. © 2025 Elsevier B.V.

As energy consumption continues to increase, reducing peak loads and overall demand may become increasingly important in the design of smart buildings. This study explores the potential integration of triple-phase change materials (TPCMs) with machine learning techniques as a way to improve energy efficiency in smart building systems. By embedding TPCMs within building envelopes, it is believed that energy demand management could be optimized, operational costs potentially reduced, grid stress alleviated, and the coefficient of performance (COP) of chillers enhanced. A promising approach may involve the use of deep clustering for multi-agent reinforcement learning (DCMARL), which could facilitate strategic shifting of HVAC cooling loads. This method might help eliminate idle compressor runtimes and partial load inefficiencies, using off-peak cooling hours to boost system performance. DCMARL could also enable the optimal sequencing control of duct dampers, supporting more adaptive and responsive HVAC operations. To address the complexities of this control challenge, the study suggests dividing cooperative multi-agent policies into five piecewise segments using clustered Lagrangian trajectory curves. This segmentation method could help manage nonlinear regression challenges, potentially resulting in more efficient system behavior. Initial results indicate that TPCMs made from tetradecane and hexadecane may show phase change characteristics compatible with recommended indoor comfort ranges. If confirmed, their integration could greatly decrease the size of thermal energy storage systems—possibly to just 18.2 % of the volume needed for conventional PCM envelope strategies. Such a reduction could reveal a transformative potential in collaborative machine learning and PCM integration for energy demand management, cost reduction, and thermal storage efficiency. Depending on operational conditions across three test scenarios, the DCMARL algorithm may achieve energy savings from 4.5 % to 100 %, indicating a wide range of potential benefits. These insights could lead to more sustainable and resilient energy systems in future smart building applications. © 2026 Elsevier Ltd

The present review focuses on the issue of freshwater shortage and growing global request for freshwater, which requires a serious need for original technologies, predominantly solar stills combined to thermoelectric cooling (TEC) to improve desalination competence. The originality of this paper lies in directing a methodical review to analytically inspect design optimizations and performance enhancements in solar stills engaging TEC. Therefore, it goes beyond the prior efforts by resolving the insistent encounters of low productivity and energy inefficiency of conservative systems and discovering the developments made by the combined solar stills and TEC. Similarly, this review emphasizes appraising the helpfulness of different layouts and materials used in these systems through energy and exergy analyses. Important results elucidate that integrated TEC can meaningfully increase freshwater productivity, with reported gains of more than 570%. Effectiveness enhancements are ranged between 11.2 and 76.4%. Furthermore, the incorporation of nanofluids, mainly copper oxide nanoparticles at a 0.08% concentration, has improved freshwater productivity by 81% and exergy efficacy by 112.5%. Further benefits are stated by presenting hybrid designs that incorporate photovoltaic panels, phase change materials (PCMs), and heat pipes. Specifically, the hybrid designs afford the possibility of continuous 24-h operation at reduced freshwater production cost of less than $0.031 per liter. Referring to energy and exergy analyses, it can be assured that TEC can play an essential role in minimizing exergy destruction and maximizing thermal gradients within the system. Thus, it can be determined that TEC-integrated solar stills can offer a wonderful solution for sustainable freshwater production to tackle the progressive water scarcity issue. However, some other barriers are still existed that related to high energy consumption and economic viability that must be resolved. Future investigation should therefore put efforts toward developing optimal designs of TEC-integrated solar stills to ensure a balance between performance, cost, and scalability to enable broader implementation. © The Author(s) 2026.