البحوث والمنشورات العلمية

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.

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.

The need to strike a balance between separation performance and environmental responsibility has led to the development of polymeric membranes with green functional groups as a viable approach for sustainable wastewater treatment. Beyond descriptive reporting, this review critically synthesizes molecular findings connecting membrane structure–property–performance correlations to bio-based functional additives. The effects of naturally occurring functional groups, such as phenolics, flavonoids, polysaccharides, amino-rich biopolymers, and plant-based reactive moieties, on the shape, surface chemistry, and interfacial interactions of the membrane are methodically examined. In order to assess several classes of green additives and fabrication techniques based on their operational stability, durability, sustainability indicators, permeability-selectivity trade-offs, and fouling resistance, a comparative approach is presented. The review clarifies the main mechanisms for antifouling and separation, including hydrogen-bonding networks, surface charge modulation, hydration layer formation, radical scavenging, and antimicrobial activity. It also critically analyzes how these mechanisms result in better dye, heavy metal, and oil contamination removal. Significantly, the analysis reveals the discrepancies and knowledge gaps in published mechanistic interpretations, such as the long-term stability of bio-functionalized membranes under practical operating conditions, the relative contributions of surface chemistry versus bulk structural changes, and additive dispersion and leaching effects. Lastly, future research directions are discussed, with a focus on scalable manufacturing routes, hybrid green nanocomposite functionalization, and intelligent and stimulus-responsive bio-additives. All things considered, this review offers a comparative and mechanistic viewpoint that clarifies the actual and potential constraints of green advanced functional groups for developing next-generation high-performance wastewater treatment membranes. © The Royal Society of Chemistry, 2026.

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.

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.

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.

Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. According to this application, robust forecasting is required to obtain a signal identical to the original signal. This study proposes a forecasting technique that obtains a robust signal that can be used in different applications. A long short-term memory neural network (LSTM-NN) was used to predict future samples from present and past samples. An Electroencephalography (EEG) dataset was used to test this technique. Four channels were used as input examples, one of which was the predicted output. All four channel samples were fed into the four networks to predict the future samples. To decrease complexity, only one hidden layer is used for this purpose. The statistical results are promising for applications that require an almost perfectly predicted signal. The number of hidden cells is first very low (five cells only), which gives a Root Mean Square Error of less than 20, whereas when the number of hidden cells is increased to 100, the Root Mean Square Error (RMSE) is approximately 7.5 for all four channels.

Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. According to this application, robust forecasting is required to obtain a signal identical to the original signal. This study proposes a forecasting technique that obtains a robust signal that can be used in different applications. A long short-term memory neural network (LSTM-NN) was used to predict future samples from present and past samples. An Electroencephalography (EEG) dataset was used to test this technique. Four channels were used as input examples, one of which was the predicted output. All four channel samples were fed into the four networks to predict the future samples. To decrease complexity, only one hidden layer is used for this purpose. The statistical results are promising for applications that require an almost perfectly predicted signal. The number of hidden cells is first very low (five cells only), which gives a Root Mean Square Error of less than 20, whereas when the number of hidden cells is increased to 100, the Root Mean Square Error (RMSE) is approximately 7.5 for all four channels. © 2026, Iran University of Science and Technology. All rights reserved.

Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. According to this application, robust forecasting is required to obtain a signal identical to the original signal. This study proposes a forecasting technique that obtains a robust signal that can be used in different applications. A long short-term memory neural network (LSTM-NN) was used to predict future samples from present and past samples. An Electroencephalography (EEG) dataset was used to test this technique. Four channels were used as input examples, one of which was the predicted output. All four channel samples were fed into the four networks to predict the future samples. To decrease complexity, only one hidden layer is used for this purpose. The statistical results are promising for applications that require an almost perfectly predicted signal. The number of hidden cells is first very low (five cells only), which gives a Root Mean Square Error of less than 20, whereas when the number of hidden cells is increased to 100, the Root Mean Square Error (RMSE) is approximately 7.5 for all four channels. © 2026, Iran University of Science and Technology. All rights reserved.

Early detection of breast cancer significantly improves patient outcomes through timely and accurate diagnosis. This study proposes a hybrid image reconstruction method combining the Maximum Likelihood Expectation Maximization (MLEM) algorithm with the Distance Driven Method (DDM) for stationary digital breast tomosynthesis, aimed at producing high-resolution three-dimensional (3D) breast images. The method was initially validated using simulated projection data of a digital breast phantom modeled with two spheres of varying radii and attenuation coefficients. Fifteen projection images were generated over a 15° angular range (from +7° to –7° with 1° increments) to replicate realistic tomosynthesis acquisition. The focus plane was set 45 mm above the detector with a pixel size of 0.14 mm. Reconstruction results demonstrated enhanced image quality, with spatial resolution quantitatively evaluated using the Line Spread Function (LSF) across the smaller sphere. Compared to the Ray Driven Method (RDM), the MLEM-DDM approach provided better contrast, sharper edges, and fewer artifacts. Subsequently, the method was applied to experimental data from a real breast phantom. Volumetric reconstructions revealed detailed tissue structures across multiple planes, with spatial resolution assessed by line profiling through two aligned calcifications in the focus plane. The MLEM-DDM method preserved the shape and sharpness of these calcifications more effectively than MLEM-RDM. Overall, the proposed MLEM-DDM framework enhances spatial resolution and visualization quality in stationary digital breast tomosynthesis, demonstrating strong potential for improved early breast cancer detection and diagnostic accuracy. © 2026, University of Kufa. All rights reserved.

The development of heterogeneous catalysts remains a major focus due to their industrial importance and potential in emerging chemical processes. Mesoporous silica materials, particularly SBA-15, offer advantages as catalyst supports because their surface silanol groups act as intrinsic catalytic sites and allow for controlled functionalization. However, a limited understanding of structure–property relationships often restricts the optimal design of such systems. This study examines how solvent choice influences the grafting density of catalytic ligands on SBA-15 and the resulting catalytic performance. N-(2-aminoethyl)-3-aminopropyltrimethoxysilane (diamine) and 2-(diphenylphosphino)ethyltriethoxysilane (PPh2) were grafted onto SBA-15 using toluene or 2-propanol, followed by coordination with Fe(III) and evaluation in epoxide ring-opening reactions. Elemental analysis revealed that diamine-functionalized materials incorporated 15–20 times more ligand than PPh2-functionalized analogues. Despite their lower loading, PPh2-based catalysts exhibited roughly twice the activity reduction, resulting in significantly higher ligand-normalized turnover rates (up to 113 h−1). In contrast, diamine systems provided the highest Fe-normalized activity (up to 69 h−1), indicating improved metal stabilization. Additionally, 2-propanol consistently produced more active catalysts than toluene. Overall, the results demonstrate that solvent and ligand selection strongly govern metal accessibility and ligand efficiency, enabling the rational design of cost-effective Fe–SBA-15 catalysts. © 2026 Wiley-VCH GmbH.

The study focuses on the Mishrif carbonate reservoir in the X oilfield, southern Iraq, which presents characterization complexities of a difficult nature due to its complex geology. The field, sited in the Zubair subzone, the most prolific and the southernmost of the Mesopotamian Plain hydrocarbon production units, is a good example of the structural complexity of the unstable shelf of the region of southern Iraq, where salt tectonics, basement faulting, and deformation of the region together produce characteristic anticline structures. This study uses an integrated petrophysical method combining core analysis from Wells 1 and 5, nuclear magnetic resonance (NMR) T2 distribution measurements, and conventional well log data to characterize the heterogeneous Mishrif carbonate reservoir comprehensively. A hierarchical pore classification system was developed based on mercury injection capillary pressure (MICP) analysis and geological observations, classifying the pore network into three different types: micropores, mesopores, and macropores. Through systematic calibration with MICP data, specific T2 relaxation time cutoffs were established to partition total porosity into these pore-size classes. The analysis shows that micropores mainly contain bound fluids, with a T2 cutoff value of 50 ms effectively describing irreducible water saturation. Strong relationships between NMR-derived porosity division and MICP-based pore size distributions confirm the integrated methodology. The method successfully quantifies critical reservoir parameters, including porosity, permeability, and pore size distribution. The results demonstrate that integrating core-calibrated NMR analysis with conventional petrophysical evaluation significantly enhances characterization accuracy in complex carbonate systems. This methodology provides essential parameters for reservoir engineering applications and establishes a robust framework applicable to similar heterogeneous carbonate reservoirs. The successful application of this integrated approach in the structurally complex Mishrif Formation emphasizes its potential for improving reservoir characterization in challenging geological settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

Petrophysical analysis provides essential data for subsurface formation evaluation and resource estimation. Key parameters like porosity, permeability, and water saturation are not direct outputs but are derived through the interpretation of well logs, a multi-step process involving acquisition, processing, and calibration, each introducing uncertainty. This research aims to quantify porosity uncertainty using the Monte Carlo technique and to determine how it is affected by different parameters. First, we did a quick analysis of the data to understand the well lithology and the fluid type. Second, we calculated the input parameters (densities) from the logs, and due to the interpretation challenges and the lithology changing, we had to do a zonation of the log interval and assign a specific RHO matrix value for each interval. Then, we did an uncertainty analysis for the RHO matrix within one of the zones to clarify uncertainty, and we had ±0.05 standard deviation of matrix density. Finally, the effect of uncertainty on input parameters will be studied using Monte Carlo analysis. Our key finding shows that a ± 0.05 g/cm3 standard deviation in matrix density (RHO matrix) propagates to a ± 0.02 uncertainty in porosity value. The standard deviation of the interval changes from depth to depth, and an average was used in calculations. Results indicate that the uncertainty in porosity increases proportionally with the increase in standard deviation of matrix density due to the linearity of the porosity equation.

This work explores the optimization of poly (ether sulfone) (PES) mixed matrix membranes including titanium dioxide/bismuth ferrite (TiO2–BiFeO3) nanoparticles for improved dye-contaminated wastewater treatment. The manufacturing and operational factors that affect membrane compatibility, nanoparticle dispersion, and overall performance were emphasized. Within a central composite design framework, response surface methodology (RSM) in conjunction with analysis of variance (ANOVA) was used to methodically assess the effects of important variables, such as operating pressure (100–300 kPa), Congo Red (CR) dye concentration (0.1–0.3 g/L), and nanoparticle loading (0–0.1 wt%). Strong prediction ability was shown by the constructed model, which also found ideal conditions at 132 kPa operating pressure and 0.06 wt percent nanoparticle loading. In these circumstances, the membrane obtained a 99.88% CR dye rejection and a permeate flux of 44 kg·m⁻²·h⁻¹ . These results demonstrate the considerable potential of PES/TiO2–BiFeO3 mixed matrix membranes for advanced dye wastewater treatment applications by confirming their markedly improved permeability and separation efficiency. © 2026 The Authors.

The hydrogenation of naphtha is critical to producing stable, clean gasoline, yet current catalysts often lack selectivity and stability. In this work, a novel palladium-magnesium oxide (Pd/MgO) nanocatalyst was developed to address these challenges. The catalyst was prepared by reduction of PdCl4 on MgO support using sodium borohydride, resulting in well-dispersed Pd nanoparticles with an average size of 1.7 nm and a Pd loading of 0.9 wt. (nominally 1 wt.). The size, composition, and dispersion of the nanoparticles were confirmed using transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, hydrogen pulse chemisorption, inductively coupled plasma atomic emission spectroscopy, and hydrogen temperature programmed reduction measurements. Catalytic tests showed great activity of quinoline hydrogenation at 150 °C and 40 atm H2, with a corrected turnover frequency (TOFcorr) of 6400 h⁻¹, which was almost fourfold higher than Pd/SiO2 and Pd/Al2O3 commercial catalysts (TOFcorr = 1600 - 1800 h⁻¹). Linear alkenes were hydrogenated with the catalyst at mild conditions (25 °C and 10 atm H2). Moreover, during biodiesel upgrading, the conversion of polyunsaturated fatty acid methyl esters to stable monounsaturated products was achieved at (100 °C, 1 atm of H2 with >80% conversion). Recyclability tests proved that the alkene hydrogenation activity was stable in three cycles, and only slight deactivation in quinoline hydrogenation occurred. This work shows that the Pd/MgO nanocatalysts are promising for enhancing the quality of gasoline, minimizing the formation of gums, and increasing the stability of biodiesel because of their nanoscale dispersion, high selectivity, and recyclability. © This work is licensed under a Creative Commons Attribution 4.0 International License