Articles in This Issue
Abstract
Semiconductor photocatalytic technology has become an area of extensive research due to the extensive environmental contamination. Zeolitic imidazolate frameworks (ZIFs) are a distinct class of metal-organic frameworks that exhibit promise in the field of photocatalysis. This is attributed to their inherent porosity structure, versatile functionalities, rapid electron transfer rate, and excellent chemical-thermal stability are exhibited by this material. In addition, the photocatalytic performance of ZIFs and their derivatives can be greatly increased with the incorporation of active metals or semiconductor materials that act as light collecting centers or electronic mediators. Lately, there has been a growing focus on producing and utilizing materials under consideration are derived from the Co-zeolitic imidazolate framework (ZIF-67).), due to their remarkably great surface area, controllable pore diameter, and outstanding responsively to visible light. This article provides an exhaustive review of the use of ZIF-67-based heterojunctions in visible light-promoted photo-catalytic breakdown of organic dye contaminants. The review discusses and summarizes representative works, with particular emphasis given to the synergistic impacts and proposed mechanisms of the ZIF-67 composite photocatalysts in boosting photocatalysis process. Finally, the recent achievements and challenges in this discipline are discussed, and potential avenues for future investigation are suggested.
Abstract
Three-phase induction motors (IM) are widely utilized in various applications such as fans, milling machines, conveyor systems, robotics, pumps, and heavy machinery. Tasks that require precise speed control often demand additional control systems to ensure efficient and accurate operation. Unfortunately, the speed control of induction motors is a difficult problem due to system nonlinearity, parameter variations, and external disturbances. Therefore, standard proportional-integral-differential (PID) control systems are among the various techniques available for motor speed regulation. In this paper, the PID controller is used as a criteria to assess the efficacy of the proposed approach. This work specifically addresses the use of a toolbox controller for active disturbance rejection control (ADRC) to control the speed of a three-phase IM. The ADRC toolbox is a software tool (often a MATLAB/Simulink toolbox or library) designed to help users implement ADRC methods in their systems without having to code the algorithms from scratch. The speed performance of the motor was compared between PID and ADRC controllers. The evaluation was performed under steady state operation, with the intention of establishing and maintaining the speed of 1400 rpm. By simulation, the performance of the controllers in respect of rise time, settling time and the current consumption in all the simulations was analyzed conducted through MATLAB/Simulink. Simulation data indicate that the ADRC system outperforms conventional PID controllers when used with induction motors. Performance indicators such as ITAE (14.3 vs. 19.7), ISE (234.01 vs. 251.13), and RMSE (644.21 vs. 660.62) demonstrate that the ADRC system provides higher accuracy, faster response, and more efficient speed regulation control compared to the PID system. The results reveal that ADRC has better performance, especially in disturbance suppression. starting current, which contributes to extending the motor’s operational lifespan. Additionally, ADRC ensures smooth acceleration of rotor speed with a lower rise time of 0.3s as well as settling time of 0.36s, allowing the system to reach steady-state speed more efficiently. ADRC is more adaptive to nonlinear and unpredictable settings than PID since it actively compensates for total disturbances and estimates them.
Abstract
Urban Heat Island (UHI) phenomena lead to elevated surface temperatures, increased energy consumption, and accelerated pavement degradation. This study aims to enhance the thermal and mechanical performance of asphalt mixtures by incorporating fly ash (FA), a byproduct of coal combustion, as a partial replacement for cement. Asphalt mixtures were prepared using various FA contents (0%, 2%, 4%, 6%) through the dry mixing method. Thermal conductivity was evaluated using the QTM-500 device, while Marshall tests assessed mechanical stability. Results showed that 6% FA reduced thermal conductivity by 20.13%, whereas 4% FA provided the best balance between thermal insulation and structural stability. A clear inverse relationship was observed between thermal conductivity and properties such as bulk density, Marshall stability, and air voids, indicating that FA can enhance insulation without compromising durability. The study focused on the surface asphalt layer, as it serves as the primary interface for solar radiation and heat exchange. Improving its thermal behavior reduces heat transfer to underlying layers, thereby extending pavement lifespan. Moreover, these improvements contribute to mitigating UHI effects, by lowering heat absorption during the day and minimizing heat retention at night supporting sustainable urban development.
Abstract
This paper tests the effect of noise on digital images. Since Gaussian noise can affect almost all computer and communication systems and therefore can come from various natural sources, it was interesting to measure the effect of such noise on features of the image. To generate the noisy images, a few images were used with Gaussian noise, and a data set was applied to this purpose. The wide variety of noise parameters was measured using the same input image with different mean and variance values each time. Gaussian noise was then removed with enhancement filters. In all fields of online communication, multimedia systems, medical imaging, and military communications, digital images have important roles to play. Internet and cellular networks are storing and delivering colored images. A lot of data needs to be protected from unwanted access. In cryptography, messages are encoded to render them unintelligible. Security of data is always essential. There are different cryptographic algorithms for encrypting and decrypting images. Our purpose of this study is to encrypt and decrypt the images with the help of the Advanced Encryption Standard (AES) algorithm for authentication. This paper guarantees that after processing, the quality of the image is maintained and no corruption was occurred
Abstract
The use of polymers to modify asphalt binder properties in road paving has been increased over the past few decades, producing asphalt mixes that can withstand the growth in traffic load concurrent with development. Therefore, the use of modified asphalt binders has become increasingly popular in recent years to enhance the properties of pavement roads by incorporating various materials, including sustainable ones. This study was conducted as part of a larger study for enhancing asphalt binder properties by using 1%, 2%, 3%, and 4% of sasobit (S) as a warm mix asphalt additive and 1%, 2%, 3%, and 4% of low-density polyethylene (L) as sustainable materials and the mix combination (MC) of both of them. The performance of the treated bitumen was evaluated using conventional tests, including penetration, softening point, and ductility, to assess the physical properties of the mixtures. More advanced tests, including a dynamic shear rheometer (DSR) and a rolling thin film oven test (RTFOT), were conducted. The results show that 4% of Sasobit and 1% of LDPE achieved the optimal values, which significantly enhances the performance of asphalt binders, offering a synergistic blend of sustainability and functionality. LDPE improves the binder’s elasticity and resistance to deformation, while Sasobit lowers mixing and compaction temperatures, boosting workability and energy efficiency. Their combination increases the softening point and reduces penetration and ductility, indicating better high-temperature performance. Viscosity is notably reduced, facilitating easier handling and mixing. Moreover, Sasobit enhances the binder’s resistance to thermo-oxidative aging, with improved ductility retention and thermal stability. The study concluded that using Sasobit and LDPE results in high-performance and durable asphalt mixes that may be highly resistant to permanent deformation and temperature susceptibility.
Abstract
Polymer composites reinforced with clay have garnered substantial academic and industrial interest owing to their environmental and mechanical benefits. This research focuses on the effectiveness of metakaolin-unsaturated polyester composites for sequestering Ni2+ ions from Ni(NO3)2 aqueous solutions and on their tribological performance. The composite materials were synthesized with varying metakaolin concentrations (1, 1.5, 3, and 5 wt.%) to evaluate the influence of clay content on nickel ion recovery and wear resistance. The experimental results show a direct correlation between the reinforcement percentage and the functional properties of the composites. The highest value (61.415%) of Ni+2 recovery efficiency was achieved at a metakaolin concentration of 5 wt.%, which is due to the increased density of active adsorption sites on the composite surface. Conversely, composites with lower metakaolin content exhibited diminished recovery rates due to a limited number of available sites for ion adsorption. Wear resistance results show the same trend, wherein the composite with 5 wt.% metakaolin exhibited maximum wear resistance, characterized by minimal material loss at the applied loads (5, 10, 15, and 20 N). Furthermore, Shore-D hardness measurements indicated a gradual enhancement in the material's hardness with increasing metakaolin content, which confirms the reinforcing role of the clay in enhancing the mechanical properties. Scanning Electron Microscopy (SEM) micrographs showed the formation of wear grooves aligned with the sliding direction during wear testing. In addition, agglomerates and surface deposits were formed that are correlated with the adsorption of metal ions onto the composite surface. These results prove that the incorporation of metakaolin as a reinforcing agent confers dual advantages: it significantly enhances the adsorptive capacity for Ni+2 ion recovery and improves the tribological and mechanical properties of the composite. This positions the material for use in environmental remediation, especially wastewater treatment, and in engineering applications requiring enhanced durability.
Abstract
Graphene is two Dimension-carbon based nanomaterial, and unique properties of the graphene made it interest to researchers. The objective of this work was to address the need for low-cost, efficient graphene preparation; the electrochemical exfoliation process and photosynthesis of the exfoliated solution were used. In this study, the exfoliated graphite mixture. The exfoliated graphite (Gr2, Gr3, Gr4) (0.08, 0.1, 0.2 M NaCl, respectively) was dried by sunlight and investigated; the results confirmed of multilayer graphene by Raman, UV–Vis, FESEM/EDS, grain size 4–9 µm) . Behavior of UV analysis related with number of single and multilayer of graphene depends on conjugative effect that arises from nanometer-scale sp2 clusters, and chromophore units as well as functional groups, also oxygen clusters may be removed and disappear the shoulder. FTIR peaks confirmed peeling off graphene layers. One of the most important reactions is the incorporation of Cl atoms into unsaturated sites. Grain size increased with increasing voltage, while it decreased in (0.2 M) NaCl. FESEM showed a smooth, crimped surface. EDS analysis showed a C/O ratio of (7.6, 4.6, 1.26), with Cl and Na observed in rinsed Gr3. D, G, and 2D peaks were displayed by Raman spectroscopy. In this study, it was concluded that NaCl-assisted exfoliation is a simple and low-cost route.
Abstract
The present research aims to develop a sustainable, high-performance grout mix for semi-flexible pavement applications by partially replacing Ordinary Portland Cement (OPC) with Metakaolin (MK) and Ground Granulated Blast Furnace Slag (GGBS). The experiments were carried out in two steps: Portland cement was partially replaced with MK (10%, 20%, and 30%) to determine the optimum mix proportions for rheological and mechanical behavior. The selected optimal MK content was mixed with GGBS at 50%, 60%, and 70% replacement levels. Flowability was evaluated using flow time tests, and DIOs assessed mechanical performance through compressive, tensile, and flexural strength testing. The results showed that a 20% MK replacement level achieved a balance between workability and strength, with an optimum flow time of 14.03 s and a compressive strength of approximately 32.2 kN at 7 days of age. However, incorporating GGBS with a 20% MK blend enhanced the sustainability of these blended grouts without sacrificing their mechanical properties. The MK-GGBS grout-60% GGBS (60% GGBS) showed the highest compressive and flexural strengths, which can be considered for semi-flexible pavement applications. The incorporation of MK and GGBS reduces energy consumption during cement production, which is widely known as a significant contributor to global warming. It increases the durability of grouting materials under traffic loading.
Abstract
The removal of Congo red dye from industrial wastewater before its discharge to the environment is a critical problem, as it is non-biodegradable, toxic, and carcinogenic. Activated carbon was synthesized from a suggested sustainable source (sugarcane waste) and used as an adsorbent for the dye. Isotherm adsorption models and adsorption kinetics were investigated, and a simulated adsorption column model was proposed and developed based on the modified general rate model. The batch results showed that the highest removal rate achieved was 87.1% of the initial dye concentration (30 mg/L). The Freundlich isotherm model best described the equilibrium data, as evidenced by the high correlation coefficient (0.9535). Meanwhile, the maximum adsorption capacity was found to be 149.25 mg/g using the Langmuir isotherm. Furthermore, the kinetic results indicate that the pseudo-second-order model accurately describes the concentration-time relationship. A simulated maximum dynamic adsorption capacity of 0.096 mg/g was achieved at an optimal volumetric flow rate of 0.1 mL/min, with a dye concentration of 30 mg/L and a bed length of 39.69 cm. The continuous column efficiency increased with column length, reaching a maximum of 75% at 66.15 cm. Also, as the dye concentration increased, the column efficiency and dynamic adsorption capacity decreased. The study demonstrated an innovative approach to removing Congo red dye from its aqueous solution in a neutral medium using sustainably produced activated carbon derived from a sustainable source. A continuous removal process was simulated by developing a mathematical model based on adsorption curves and adsorption kinetics, which successfully described the adsorption process. This type of modeling, rather than relying on pre-existing equations, enables more efficient, scalable design.
Abstract
In this study, an experimental and numerical approach is used to evaluate a transtibial bone-anchored prosthesis system that includes an osseointegration implant composed of Ti-6Al-7Nb. The work addresses three factors that are critical to clinical and biomechanical performance: (1) the mechanical properties and corrosion behavior of the implant materials, (2) enhancement of bioactivity via hydroxylapatite (HAp) surface coatings, and (3) structural response of the tibia-implant construct under physiologically representative gait loads. A compressive test was also realized, and the good mechanical behavior of Ti-6Al-7Nb (σu =1271 MPa; σy = 755 MPa) revealed its ability to be used for load-bearing applications. The HAp coatings had a homogeneous nanostructure, greatly enhanced corrosion resistance (89% protection efficiency), and induced apatite-like formation in simulated body fluid. Peak ground reaction force was 1060 N, recorded using gait analysis, and used for implant biomechanics analysis in FE modelling. The equivalent stress of the model was low (11.95 MPa), its deformation small (0.114 mm), and the safety factor high (11.7), indicating that the implant‐bone interface had good structural stability. Taken together, these data demonstrate that Ti-6Al-7Nb osseointegrated prostheses for transtibial amputees exhibit good mechanical reliability and bioactivity, offering a stable load-transfer profile suitable for long-term clinical application.
Abstract
Distillation columns are among the most critical units in chemical processes, especially in oil refineries. This research aims to develop a mathematical model for a nonlinear multicomponent naphtha distillation column using real experimental data. The naphtha distillation column used in this research consists of a column with a diameter of 2.2 m and a height of 16.6 m, containing 20 trays of type (valve tray). The process currently operates in Al-Dora Refinery as part of the Midland Refinery Company in eastern Baghdad, Iraq. Four manipulated variables (reflux flow rate, reboiler heat duty, light compound fraction in the feed, and feed flow rate) were tested for their impact on four control variables (light compound fraction in the top product, light compound fraction in the bottom product, amount of top product, and amount of bottom product). A 4x4 transfer function is produced by representing the obtained dynamic models for the variables under study using various second-order models (having different damping factors) with dead time.
Abstract
Accurate measurement of residual stress is crucial for assessing structural integrity and predicting fatigue life. Although significant work has been documented on applying two-dimensional (2D) and three-dimensional (3D) digital image correlation (DIC) independently to the hole drilling process, a direct comparison of their accuracy and performance under the same conditions (most importantly, with plastic deformation) has not yet been published. This work conducted the first explicit comparison between 2D and 3D-DIC combined with the hole drilling method for residual stress measurement using finite element (FE) analysis as a validation standard. Residual stresses were induced through plastic bending of a square beam made of aluminum alloy 7075-T651. The findings indicate that the 3D-DIC is more accurate (93%) compared to the 2D-DIC (88%), objectively tested against FE solutions, even on a flat surface, which is theoretically ideal for 2D-DIC.This difference in accuracy is attributed to the higher sensitivity of the 2D-DIC to out-of-plane deformation and camera misalignment. These results support the effectiveness of 3D-DIC in measuring residual stress and explain that there is a trade-off between the practical accuracy of 2D and 3D versions of the hole-drilling technique.
Abstract
Quantum key distribution (QKD) provides secure keys with information-theoretic security ensured by the principles of quantum mechanics. The flexibility and efficient deployment of free-space optical (FSO) quantum key distribution systems can make them highly advantageous. Despite the benefits of free-space transmission, there is still a chance of running into unfavorable weather conditions like fog. According to this study, free-space continuous variable (CV) QKD systems may be applicable when fog is present. Using attenuation based on the Kim and Kruse empirical visibility models, the performance of the models that explain the relationship between fog attenuation and air visibility is examined in order to evaluate its impact on the secure key rate (SKR) at wavelengths of 850 and 1550 nm. A comparative analysis between the two wavelengths is carried out to highlight their suitability under different weather conditions. Based on the results, 1550 nm performs better in foggy situations, reaching an SKR of 1.389379 bits/symbol, because of lower scattering losses, whereas 850 nm achieves a longer range of 12 km in clear air because of smaller diffraction divergence. The results demonstrate that wavelength selection plays a critical role in enhancing the robustness of CV-QKD systems that operate over FSO links. In addition, optimizing reconciliation efficiency (β) under thick-fog conditions greatly improved the SKR, indicating that 1550 nm operation has the ability to achieve reliable and secure quantum communication. Results show that varying β from 0.90 to 1.00 for both 850 nm and 1550 nm produces SKR enhancement of (0.63-0.80 bits/symbol) and (0.97-1.13 bits/symbol), respectively.
Abstract
The utilisation of fake news as a tool of soft power is regarded as a strategic component of contemporary warfare, posing an escalating threat to societal and political stability as well as public trust. Deep Learning models achieve superior performance; yet, they require substantial computational resources and possess interpretative restrictions, which hinder their practical application in real-time scenarios and impose constraints on resource configurations. This study evaluates a traditional machine-learning framework based on linear and ensemble classifiers, including Linear Support Vector Classification (LinearSVC), Logistic Regression, Multinomial Naïve Bayes, and Random Forest. The framework employs a fixed, explicitly specified TF–IDF vectorization configuration and a consistent preprocessing pipeline. The system was assessed using the WELFake dataset, which contains over 70,000 labelled news stories, employing accuracy, precision, recall, F1-score, and ROC-AUC as evaluation metrics. The findings demonstrate the superiority of LinearSVC, attaining the maximum accuracy of 95.65%, providing balanced performance metrics, and surpassing all other models. This study contributes by providing an interpretable, scalable, and domain-agnostic solution through the development of a practical fake news detection system designed to combat digital disinformation in real-world contexts.
Abstract
Alternative fuels come from non-traditional sources and can partly replace fossil fuels to cut environmental impact and support sustainable energy use. A single-cylinder diesel engine with rated power 5.2 Kw at 1500 rpm was tested with American Saffron Biodiesel blends. B0 (diesel), B10 (B10D90), B20 (B20D80), and B30 (B30D70), under different loads, and the results were compared with diesel. B20 showed the best improvement, with 13.41% higher brake thermal efficiency and 30.3% lower fuel usage than conventional fuel, at 100% load. B30 reduced HC by 5.88%, while B10 gave the lowest CO, reduced by 8.51%. The main contribution is the use of decision tree machine-learning regression to predict and optimize performance and emissions. The model achieved R = 0.91 and R² = 0.83, supporting the prediction of multivariable engine responses across operating conditions.
Abstract
Predicting the risk of operative delivery, especially Cesarean section, is important for improving outcomes for both mothers and newborns. In this study, a machine-learning-based framework is developed using explainable artificial intelligence techniques across three clinical datasets: fetal cardiotocography, maternal health parameters, and pregnancy outcome data.Three classification models—Logistic Regression, Decision Tree, and Random Forest—were evaluated to identify the most effective approach. Among these, the Random Forest model achieved the best performance, with an F1 Score of 0.88 and an Area Under the Curve of 0.99 on the fetal health dataset.To enhance interpretability in a clinical context, a dual-layer explanation strategy was adopted. SHapley Additive exPlanations was used to analyze global feature importance, while Local Interpretable Model-agnostic Explanations was applied to explain individual predictions. The results indicate that abnormal short-term variability is a key factor influencing operative delivery risk. The explanations generated by both methods were consistent with established clinical understanding, making the model both accurate and interpretable.
Abstract
This study develops a decision-oriented uncertainty quantification methodology for analyzing as-built surface roughness in laser powder bed fusion/direct metal laser sintering (LPBF/DMLS) SS316L. A Taguchi L9 design was used to vary laser power (300-360 W), scan speed (800-1000 mm·s-1), and layer thickness (20-80 μm), producing nine process settings with three independently fabricated specimens per setting, resulting in 27 total specimens. Surface roughness was measured by contact stylus profilometry using arithmetic mean roughness (Ra), root mean square roughness (Rq), and maximum profile height (Rz). The mean roughness varied within narrow ranges, Ra as 5.748-5.952 μm, Rq as 6.673-6.811 μm, and Rz as 28.828-28.892 μm, while within-setting scatter remained non-negligible, particularly for Rz. Probabilistic regression models were evaluated using leave-one-setting-out validation, negative log predictive density, interval coverage, calibration diagnostics, and reliability-driven accept/reject analysis. For Ra and Rq, a low-capacity linear mean model with pooled variance achieved the best predictive density, indicating limited transportable heteroscedastic structure under setting-wise extrapolation. For Rz, a nonlinear mean model with pooled variance performed best. Unregularized two-stage variance learning produced unstable uncertainty estimates, whereas shrinkage regularization improved calibration and reduced spurious setting-dependent variance effects. The decision analysis showed that calibration strongly influences process acceptance, reliability thresholds sharply reduced the number of accepted settings, and shrinkage-stabilized uncertainty produced a conservative and consistent decision frontier. The main contribution of this work is the integration of grouped validation, probabilistic calibration, variance-shrinkage modelling, and reliability-aware decision analysis for surface roughness qualification in LPBF/DMLS SS316L.
Abstract
The expansion of chemical and pharmaceutical data increases the need for enhanced computational systems that can interpret molecule structures and forecast chemical properties. Due to the fact that molecular visualization or prediction of chemical properties are presently performed separately during the chemical research and initial drug development processes, chemical research and the initial drug development process are not as efficient as possible. As such, the research presents a fully integrated model that predicts chemical properties with a single input method (SMILES strings) for both 2D and 3D representations of molecular structure, while at the same time utilizing SMILES strings to reconstruct the molecule and extract the features from the molecule. The research develops physicochemical descriptors and circular (Morgan) fingerprints which scientists use to create features for training ensemble-based machine learning models that predict essential molecular properties. The framework enables users to create interactive two-dimensional and three-dimensional molecular visualizations which show the molecular structure in an intuitive way while displaying the results of quantitative prediction methods. The proposed method enhances usability and analytical efficiency through its unified visualization system for prediction and interpretation which outperforms current standalone tools. The Python prediction and visualization pipeline with MATLAB verification tool enables independent assessment of three-dimensional molecular geometries to confirm spatial structure representation and conformer generation while maintaining model training and inference integrity. The framework enables organizations to incorporate new datasets and molecular descriptors and learning algorithms through its modular scalable and extensible design which demonstrates the successful application of cheminformatics-based representations and machine learning to drive data-based molecular research and decision-making in chemical and pharmaceutical fields.
Abstract
Historical records (1901–2021) of Iraq's air temperature and precipitation were studied and compared. A climatic multi-model ensemble and four emission scenarios with representative concentration pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) are used in this research to explain the changes in future climate indicators in Iraq. The results showed a rise and decline in temperatures and precipitation over the past century, with an average of 1.3 °C and 57mm since 1901. A remarkable rise and decline in temperatures and precipitation occurred at 0.36 °C and 4mm per decade over the past thirty years (1990–2020). A greater increase in temperatures occurred in the southern regions than in the northern regions during summer. A temperature increase of 1.5 °C was observed in Basra City and 1.3 °C in Duhok City. The projected temperature over Iraq is higher than historical temperatures in all scenarios, with a rise of about 0.6°C every ten years. Days in June and September with temperatures exceeding 40 ºC have a greater probability of experiencing temperature increases. Iraq is expected to have a decrease in annual precipitation under the high emission scenarios of RCP8.5. The predictions of the medium emission scenarios RCP4.5 and RCP6.0 are noticeably different from the measurements of Iraqi weather stations, especially in 2006. Precipitation decreases from November to May from the historical period in high emission scenarios (RCP8.5) for the foreseeable future. Increased air temperatures and a lack of precipitation have significant effects on the ecosystem because of droughts and frequent dust storms. Therefore, the most important decision that senior management must make is the formulation of policies and procedures for water routing and storage.
Abstract
The sun is an inexhaustible source of clean energy. For a long time, humans perceived the sun as a source of heat and light, without thinking of using it fully for their purposes. The worldwide solar photovoltaic (PV) market has been growing rapidly, with significant investments leading to increased capacity. Mathematical models are used to calculate solar radiation, taking into account various meteorological factors to predict solar energy generation. The construction of P-V curves helps understand the output of PV systems, crucial for optimizing energy conversion efficiency. The photovoltaic effect allows solar cells to convert light into electrical energy, highlighting the importance of understanding the relationship between current and voltage in solar panels. In this work, the mathematical model of PV is presented and the equations that describe the relation between PV and environment are presented. Finally, a complete model is designed in Matlab and the results are presented.