Soran University

Abstract The aggregation and precipitation of asphaltene along with viscous crude oils frequently constrain flow assurance. This work examines a multifunctional green nanoadditive based on Ni-doped CuO nanocomposite to control these issues at a minimal concentration. A series of experimental tests was systematically conducted to evaluate the performance of the nanocomposite, including modified asphaltene dispersion test (ADT), fluorescence microscopy, viscosity reduction at a fixed shear rate of 100 s-1, sulfur quantification via Energy dispersive X-ray fluorescence, and a H2S bottle. At a low dosage of 25 ppm, asphaltene was successfully stabilized, achieving inhibition efficiencies of 67-73% under high-salinity and high-viscosity conditions, while achieving 85% inhibition for up to five days in a viscous crude oil (API 22.7°). Rheological measurements showed that the viscosity dropped by about 37-40% at a temperature range of 20-60°C. Fluorescence microscopy further validated the results by showing improvements in asphaltene particle distribution and a reduction in aggregate size after treatment. Additionally, a strong affinity for sulfur-containing compounds was achieved after treating the oil, removing more than 40% of total sulfides and gaining about 90% of H2S elimination within one hour. This work proposes a single solution for eliminating several flow production challenges, eliminating the need for multiple chemicals to solve those issues, which supports the oil industry transition toward green and multifunctional nano chemicals.

Metal-oxide-based heterojunction solar cells have received burgeoning research interests owing to the prospect of high efficiency, low cost, and environmental benignity. Of all the systems, ZnO heterojunctions with either NiO or CuO have received much interest for next generation photovoltaic devices. This review summarizes the material properties, fabrication methods, interface characteristics, and photovoltaic performances of ZnO/NiO and ZnO/CuO heterojunctions in a comprehensive comparison manner. ZnO, as a wide-bandgap n-type semiconducting and high electron-conductivity material, with properties including optical transparency and chemical stability, has become a good candidate for the ETL layer. Because of its large band gap and high work function, NiO can efficiently extract holes and enhance the stability of devices. These merits make ZnO/NiO hetero-junctions particularly suitable for transparent and UV-active solar cells. By comparison, CuO with a small bandgap and high absorption coefficient in the visible region can also lead to efficient light utilization of ZnO/CuO heterojunctions through type-II band alignment at the expense of becoming more susceptible to interfacial defects and environmental deterioration. In this review, we present a critical overview of physical, chemical, and green-synthesis routes, band-alignment engineering strategies, interface-defect passivation methods, and degradation mechanisms affecting the two systems. This work, which discusses the tradeoffs among optical transparency, absorption efficiency, stability and interfacial recombination inform practical design guidelines for future advances in optimizing ZnO-based heterojunction solar cell devices for next generation of scalable and environmentally friendly photovoltaic technologies.

Abstract The genus Crataegus (Rosaceae) exhibits remarkable taxonomic diversity in Iran, with seventeen recognized taxa distributed across contrasting ecological zones. In recent years, hawthorn has gained prominence as a valuable botanical resource, with applications spanning both herbal medicine and functional food development. Historically, various plant organs, particularly fruits, leaves, and flowers, have been employed in traditional remedies targeting cardiovascular disorders, including hypertension, arrhythmias, and other cardiac complications. These therapeutic properties are primarily attributed to the rich array of secondary metabolites characteristic of hawthorn, notably phenolic compounds and anthocyanin pigments, which underlie its antispasmodic and analgesic effects. We investigated the morphological, biochemical, and phytochemical diversity of eight Iranian Crataegus pontica ecotypes collected from geographically distinct regions. Ecotype origin and environmental conditions significantly influenced all measured parameters ( p < 0.05). Substantial variation was observed in fruit morphometrics, with CPE3 (Miyaneh) and CPE5 (Taleqan) producing the largest fruits, while CPE6 (Sanandaj) and CPE7 (Khorramabad) yielded considerably smaller ones. Biochemical profiling revealed marked ecotypic differences in total soluble solids, soluble carbohydrates, vitamin C, carotenoids, and anthocyanins, reflecting the profound influence of environmental conditions on primary and secondary metabolism. High-altitude ecotypes, particularly CPE3, CPE4 (Alamut), and CPE5, consistently exhibited the highest concentrations of total phenolics, total flavonoids, and antioxidant capacity. HPLC-based phenolic profiling identified chlorogenic acid, hyperoside, rutin, quercetin, and kaempferol as predominant compounds, with ecotype-specific accumulation patterns distinguishing high-antioxidant genotypes such as CPE5, CPE6, and CPE1 (Mahabad). Strong positive correlations among total phenolic content, total flavonoid content, and FRAP values confirmed phenolic compounds as primary determinants of antioxidant potential. Multivariate analyses classified the ecotypes into three functional groups: sugar-rich, large-fruited types (CPE3–CPE4); phenolic- and antioxidant-enriched types (CPE5–CPE6); and intermediate types with moderate metabolite profiles (CPE1, CPE2, CPE7, CPE8). Canonical correspondence analysis revealed altitude, precipitation, and temperature as key environmental drivers shaping phytochemical biosynthesis. We conclude that C. pontica exhibits substantial metabolic plasticity in response to environmental variation, with high-altitude ecotypes demonstrating superior phytochemical profiles as promising candidates for breeding programs and nutraceutical development.

The construction industry is undergoing a transformative shift with the integration of intelligent monitoring technologies. Advanced sensing systems, including IoT-enabled devices, drones, and wearable technologies, are increasingly employed to track project progress, detect potential hazards, and predict environmental impacts. Coupled with artificial intelligence (AI) and machine learning algorithms, these technologies facilitate proactive decision-making, enhance operational efficiency, and reduce human error. This chapter explores emerging trends in intelligent construction monitoring, highlighting innovations in real-time data acquisition, predictive analytics, automated reporting, and adaptive risk management frameworks. It also examines challenges related to data integration, interoperability, cybersecurity, and ethical considerations, providing a comprehensive roadmap for researchers, practitioners, and policymakers to leverage intelligent monitoring systems for safer and more sustainable construction practices.

This study looks into the influence of institutional quality on economic development in European Union (EU) countries from 2000 to 2023. Baseline relationships are computed with pooled ordinary least squares (OLS), yet quantile regressions reveal varying impacts throughout the distribution, especially in the extremes. To handle the issues of dynamics and endogeneity, a two-step system generalised method of moments (GMM) estimator is used. Institutional quality is represented by government efficiency, political stability, and regulation quality, while the absorptive-capacity channels are tested with openness, foreign direct investment (FDI), and research and development (R&D) intensity, both ways. The study is a long-run growth study that does not estimate COVID-19 crisis-specific effects. Effective institutions facilitate growth, even though the extent of the facilitation differs across income levels. Openness to trade is a forceful predictor of the distribution. The return on FDI is lower without better institutions and local absorption. Short-term R&D intensity might even hold development back under weak institutional regimes because of the implementation inefficiencies and time taken to obtain the benefits. Multiple indicators and parameter settings yield consistent outcomes. The originality of this research lies in mapping the different and changing impacts on the EU through applied nonlinear interactions (institutions[Formula: see text] × [Formula: see text]openness/FDI/R&D) to uncover threshold processes. Focused improvements in regulation quality are among the proposed policies.

Swelling is recurring challenges, which can lead to formation damage and compromising the wellbore integrity. Synergizing nanomaterials with their distinctive properties, alongside cost-effective materials, can provide a significant approach in enhancing the treatment of wellbore stability. This study employs a green, simple, and economical approach for the biosynthesis of SiO₂/KCl/Xanthan nanocomposites (NCs), aimed at reducing swelling and improving the rheological and filtration properties of Water-Based Drilling Fluids (WBDFs). The impact of SiO₂/KCl/Xanthan nanocomposite on swelling was assessed, followed by a comprehensive investigation into fluid’s filtration and rheological properties, including apparent viscosity, plastic viscosity, yield point, gel strength, and filter cake thickness. The results confirmed that the green-synthesized NCs effectively reduced clay swelling by approximately 22.2%. Furthermore, they significantly improved the mud’s rheological characteristics, decreasing filter cake thickness and fluid loss by approximately 91.29% and 91.7%, respectively, under HPHT conditions. The findings strongly suggest that SiO₂/KCl/Xanthan NC is an effective additive for mitigating clay swelling, enhancing rheology, and reducing filtration challenges in marl rich formation.

This research detects phishing URLs using machine learning algorithms that analyze hyperlinks present on a website. The method incorporates novel hyperlink-specific features that are divided into 30 categories to enhance its accuracy in identifying phishing attacks. The proposed approach is entirely client-side and can detect websites written in any language. The effectiveness of the proposed approach was evaluated by testing it on a dataset containing both phishing and non-phishing websites using various classification algorithms. The Random Forest classifier achieved over 97.4% accuracy in detecting phishing websites. The study suggests that feature selection and classifier choice are essential in detecting malicious URLs. The research highlights the importance of detecting malicious websites to protect users against criminal activities. As web applications grow in importance, cybercriminals are becoming more sophisticated, and detecting malicious websites is crucial to protecting users. The proposed technique can be combined with different feature extraction models to test its usefulness in real-time scenarios for automatic detection of websites and web browser extensions. The research shows that machine learning algorithms can effectively detect phishing URLs and that further improvements can be made by incorporating more features, expanding the dataset, and using different classification techniques.