مستودع البحوث والمنشورات العلمية — كلية هندسة النفط والغاز

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.

A predictive scheme results from combination of NMR log and core data, introduced to understand the association between most important outputs of the Nuclear Magnetic Resonance (NMR) log data with core data, which is used in the Mishrif Formation from five oil fields in Iraq in Buzurgan, Halfaya, West Qurna, Majnoon, and finally North Rumaila. The oil wells were studied from Buzurgan field (Bu-24, Bu-39, Bu-40, Bu-41, Bu-51). The methods are based on statistical techniques such as multivariate regression to cluster the variables of NMR (as FFI). We combined KCOAT, KSDR and Øe (pivotal properties) with the core data. The study focused on the flow units, storage units. The NMR fingerprint results show some of interactive depth in the permeability and porosity in Majnoon with a correlation. West Qurna and North Rumaila show heterogeneity, whereas Halfaya and Majnoon are more uniform.

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.

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.

As climate change intensifies, the agricultural sector faces escalating challenges in maintaining productivity and environmental sustainability. Among emerging strategies to enhance climate resilience, silicon (Si) has gained attention for its multifaceted role in soil and plant systems. This review synthesizes current scientific understanding of silicon-mediated mechanisms that contribute to climate change mitigation and crop improvement. Silicon enhances soil physical stability through increased aggregation and improved water retention, stimulates beneficial microbial activity, and modifies soil chemistry by optimizing pH and nutrient availability. Mechanistically, silicon contributes to carbon sequestration via phytolith formation and phytolith-occluded carbon (PhytOC), a long-term stable carbon pool in soils. It also mitigates greenhouse gas emissions by reducing methane (CH₄) through enhanced methanotrophic activity, lowering nitrous oxide (N₂O) emissions via pH regulation and improved nitrogen use efficiency, and indirectly minimizing CO₂ release through greater carbon stabilization. Furthermore, Si strengthens plant tolerance to abiotic stresses such as drought, salinity, heat, and heavy metals, as well as biotic stresses from pests and pathogens, primarily through the activation of antioxidant enzymes and structural fortification of tissues. Overall, this review highlights silicon as a pivotal element for integrating soil–plant–atmosphere processes, offering a sustainable strategy to enhance climate resilience, reduce greenhouse gas emissions, and improve global crop productivity. © The Author(s), under exclusive licence to Springer Nature B.V. 2026.

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.

Tire waste has emerged as a critical environmental concern due to the massive global production of tires and their resistance to natural degradation. End-of-life tires (ELTs) represent a significant portion of non-biodegradable solid waste, contributing to pollution and posing serious risks to ecosystems and public health. This review paper provides a comprehensive overview of current tire recycling technologies and their applications. Key recycling methods such as mechanical grinding, pyrolysis, and devulcanization processing are discussed in detail. The paper highlights the various value-added applications of recycled tire materials in civil engineering, construction, energy recovery, and manufacturing. Environmental benefits, economic viability, and legislative frameworks are also examined. Finally, challenges associated with tire recycling and potential future directions for sustainable development are outlined. This review aims to guide researchers, industry stakeholders, and policymakers toward more efficient and eco-friendly tire recycling strategies. © 2025 RSC.

The classification of histopathological imagery has garnered significant interest among researchers in the last decade due to the valuable outcome that could be obtained from classifying such microscopic fractions. This would significantly contribute to examining biological interactions. To do so, researchers in the literature have employed various machine-learning classification algorithms. However, the key to success for a precise classification task lies in utilizing an appropriate set of features with proper color space channels that can extract important characteristics from the histopathological images. However, the literature shows a limited feature extraction method with limited color space channel utilization. The accuracy of classification is significantly influenced by the color channels. This study aims to extend feature learning by using a wide range of feature extraction methods and different employs distinct color channels to categorize histopathological imagery. It utilizes two benchmark datasets pertinent to the imagery of breast and prostate cancer for the study. Additionally, the study incorporated a series of pre-processing procedures, such as segmenting the images and extracting salient features. Image segmentation in this research was conducted using four distinct methodologies, encompassing Lumen, Nuclei, Cytoplasm, and Stroma. The reason behind selecting such feature extraction methods and color channels is their popularity and differences, which ensure diversity. Finally, the study utilized SVM-RFE to select features and classify images. The assessment employed metrics such as sensitivity, f1-score, and accuracy. The empirical findings demonstrate that the RGB color space yielded superior performance particularly on the sensitivity evaluation metrics across both datasets, underscoring RGB’s efficacy in classifying histopathological images. © 2025, National University of Malaysia. All rights reserved.

Stroke is a sudden-onset and life-threatening medical condition, consistently ranked among the leading causes of mortality worldwide. Early detection is critical for reducing fatal outcomes, enabling timely medical intervention, and improving patient prognosis. This study presents a cost-effective, accessible, and non-invasive stroke prediction application designed to operate without the need for specialized diagnostic procedures, making it particularly valuable in resource-constrained settings. Several Machine Learning (ML) algorithms were systematically evaluated to identify the most effective predictive model. The Ensemble Learner (EL) demonstrated the highest performance, achieving a classification accuracy of 96.7%, thereby outperforming widely adopted models such as Random Forest (RF), which exhibited marginally lower accuracy. The predictive model was trained on a dataset containing general health-related features that are easily obtainable without frequent medical checkups, ensuring high applicability for large-scale use. To address the class imbalance challenge, the Synthetic Minority Oversampling Technique (SMOTE) was applied, significantly enhancing the model’s robustness and prediction reliability. Upon execution, the application issues an immediate risk alert when a high probability of stroke is detected, prompting users to seek urgent medical evaluation. This work offers a scalable and practical solution that integrates high predictive accuracy with broad accessibility, holding strong potential to reduce stroke-related morbidity and mortality through proactive early detection.

Bauxite residue (red mud) is a waste material from alumina refineries in the Bayer process, containing significant quantities of valuable metals, notably scandium (Sc). The objective of this study is to recover Sc (III) from Hungarian bauxite residue by using hydrometallurgical processes, including solvent extraction and leaching. Red mud directly leached with hydrochloric acid to generate the leachate solution. The significant iron content (∼38 %) in red mud makes it hard to recover scandium selectively due to comparable physicochemical characteristics. According to the findings, Fe (III) could be effectively extracted from hydrochloric acid leachate as HFeC14 using diethyl ether before Sc extraction. Protocol B demonstrated superior recovery efficiency compared to the other recommended protocols. The most effective Sc recovery efficiency was attained with Protocol B, which utilized triple solvent extraction by TBP: 81 % of Sc (869 ppm) with trace amounts of related elements like Ti, Fe, La, Y, and Al. Protocol B takes in the subsequent conditions: a triple solvent extraction utilizing 10 vol.% TBP, an aqueous to organic phase volume ratio of 200 mL:75 mL, and an extraction duration of 5 min. Copyright © 2025. Published by Elsevier B.V.

The identification of human motion intention through electromyography (EMG) signals is an important area of development in human–robot interaction. This technology aids amputees in controlling their prosthetic limbs in a more intuitive manner, facilitating the execution of daily activities. However, hand amputees face challenges in using dexterous prostheses due to control difficulties and low robustness in real-life situations. This study aims to enhance the accuracy of EMG gesture recognition by extracting spatial characteristics via multiple high density (HD) maps. A total of five HD-maps are generated utilizing the root mean square value (RMS), mean absolute value (MAV), zero crossings (ZC), sign slope changes (SSC), and waveform length (WL) features. The influence of each distinct HD-map, along with the synergistic effect of numerous HD-maps in the extraction of intensity features, is assessed with regard to its impact on classification accuracy. Three machine learning classifiers are employed to categorize nine hand movements of the Ninapro (DB5) dataset. The results show that features extracted from the combination of multiple HD-maps (CMHD) achieved a high accuracy in comparison to those of individual HD-maps. Moreover, the proposed features are superior to those of conventional TD features. The error rate is reduced by approximately 7.76% relative to time domain (TD) features. The results obtained confirm the significance of spatial features extracted from multiple HD-maps that ensure consistent information in different EMG channels. © 2025 Published by IRCS-ITB.

In this study, a new series of 3-Acetyl-2-phenyl-5,5-dimethylthiazolidine-4-carbohydrazide (A1-8) was synthesized and comprehensively characterized by FT-IR, 1H/13C NMR, and mass spectrometry. The cytotoxic potential of the compounds was evaluated in vitro against PC-3 human prostate cancer cells using the MTT assay. Among the series, compounds A4 and A5, bearing 4-chlorophenyl and 4-bromophenyl groups, respectively, exhibited the most potent cytotoxicity with IC50 values of 50.65 ± 0.98 μg/mL and 50.03 ± 0.98 μg/mL, closely comparable to standard drugs Darolutamide (55.4 ± 0.9 μg/mL) and R-Bicalutamide (80.34 ± 0.89 μg/mL). In contrast, the least active compound A6 which incorporate 4-methylphenyl group exhibited an IC50 of 120 ± 0.83 μg/mL. Gene expression analysis confirmed that these compounds induced significant upregulation of tumor suppressor genes p53 and p21, indicating a mechanism of cell cycle arrest and apoptosis. Density Functional Theory (DFT), Molecular Electrostatic Potential (MEP), and frontier molecular orbital (FMO) analyses were conducted to elucidate structure–activity relationships and chemical reactivity. Molecular electrostatic potential (MEP) mapping highlighted key electrophilic and nucleophilic regions across the molecule surfaces. ADME predictions revealed that all compounds, except A3 and A8, possessed high gastrointestinal absorption and zero Lipinski rule violations, with bioavailability scores of 0.55. POM analysis identified two antitumor pharmacophore sites (Oδ−, Hδ+) with interaction distances in the range of 2.058–2.180 Å. Osiris property predictions confirmed no major toxicity alerts. Overall, compounds A4 and A5 emerge as promising leads with potent antiproliferative effects, favorable pharmacokinetics, and strong computational backing, meriting further investigation as anti-prostate cancer agents. © 2025

The Heating, Ventilation, Air Conditioning, and Refrigeration (HVAC&R) system is a complex, nonlinear behavior with a high uncertainty control system that equips the thermal comfort desired but consumes significant electrical energy and costs in different types of buildings, such as residential, commercial, and industrial. This paper introduces a new approach for online controlling of HVAC&R systems using model-based reinforcement learning (MB-RL) style to diminish energy usage and energy cost, maintain the occupants’ comfort levels by controlling the buildings’ indoor temperature, and maintain the desired carbon dioxide levels simultaneously. For this purpose, a new model based on energy and mass conservation laws is presented to model the dynamic variations of temperature and CO2 concentration levels. The HVAC&R system control trouble is defined as a specific Markov Decision Processes (MDPs) model. The reward function balances the ability to increase energy conservation while preserving the interior comfort requirements of occupants. Employing the deterministic policy algorithm (DP), the proposed methodology can manage the dimensionality curse problem due to increased state-action space. Then, it overcomes the nonlinearity and the control system uncertainty. The MB-RL algorithm, which uses a unique DP called DP-MB-RL, can select the best decisions instead of stochastic policy to reduce the calculation time. A real case, a building in Basra City, Iraq, is simulated using MATLAB software. Devoting the MB-RL and DP-MB-RL techniques to online control of an HVAC&R system, the simulation results for both methods are provided. For instance, the parameters, like electrical power, internal comfort levels, energy consumed, and energy cost at different pricing schemes, such as fixed pricing (FP), timeofuse (TOU), and real-time pricing (RTP), are assessed. The results indicated that the suggested DP-MB-RL methodology had better indoor thermal and air quality satisfaction levels, energy-saving (more than 15%), and reduced the cost of electricity by more than 15%, 13%, and 10% for FP, TOU, and RTP pricing schemes, respectively, compared to the benchmark MB-RL style controller. The DP-MB-RL controller also performed better than the Takagi-Sugeno Fuzzy (TSF) controller for the same building, saving more than 21% energy. © 2025, Tikrit University. All rights reserved.