Al-Furat Al-Awsat Technical University

Sour oranges (Citrus aurantium L.) are propagated in two ways: by seed and by semi-hardwood cuttings. Producing sour orange seedlings from seed is time-consuming and expensive. Semi-hardwood cuttings offer a faster method of propagating sour oranges, reducing both time and costs. However, they are considered relatively difficult to root. The present investigation aimed to improve the growth and development of cuttings more quickly and at a lower cost by using growth regulators IBA (Indole-3-butyric acid) , BA (Benzyle adenine), and GA3(Gibberellic acid). The greenhouse experiment was conducted during the 2023-2024 season. A completely randomized design (C.R.D.) was used as a factorial experiment with three factors. The first factor was dipping the tips of sour orange cuttings in an IBA solution at 200 ppm for 2 periods (0,10) minutes before planting. The second factor was the sprayed growth regulator (BA) at three levels (0, 0.5, 1 ppm). Third factor was sprayed by (GA3) at (0.50, 100) ppm. The investigation was performed in three replicates, by six cuttings per experimental unit. The results showed that dipping Sour orange cuttings in IBA was more effective in plant height (52.48 cm), leaf area (31.16 cm²), chlorophyll content (32.42 spad), carbohydrate (36.74%), nitrogen (1.39%), and phosphorus (0.48%) compared to the control. Spraying by BA at 1 ppm and GA3 at 100 ppm had a significant impact on the studied parameters(plant height, leaf area, chlorophyll content, carbohydrate, nitrogen and phosphorus). Double and triple interactions had a significant effect on most of the studied characters.

This study focuses on how the introduction of digital currency, especially a Central Bank Digital Currency (CBDC), might affect the institutional performance of monetary policy in Iraq. The study uses mixed methods research design, which is a combination of a thorough literature review and quantitative survey of upper and middle management at the Central Bank of Iraq. The results demonstrate the overall positive attitude to the adoption of digital currency and its ability to improve the effectiveness of monetary policy. Respondents recognize that it can have positive effects, such as enhancing financial inclusion, better transmission, and ease of use of non-standard monetary instruments such as negative interest rates. Nonetheless, they are also aware of the difficulties, including the possibility of decreasing the efficiency of traditional tools and higher volatility of financial markets. The research hypothesis is highly supported by the results of regression analysis that indicate statistically significant and positive relationship between digital currency adoption and perceived monetary policy effectiveness. This quantitative data, together with the qualitative contributions of the literature review and descriptive analysis, gives a strong basis of the interpretation of the implications of digital currencies on the monetary policy in Iraq. The research finds that an effective and properly developed CBDC can make monetary policy in Iraq much more effective. Nevertheless, the Central Bank of Iraq must pay keen attention to the possible difficulties and work out the necessary plans to avoid risks linked to the usage of digital currencies. Among the suggestions can be developed a comprehensive CBDC plan, the development of financial infrastructure, the promotion of public trust and awareness, the cooperation with interested stakeholders, and the constant monitoring and updating of the policy framework.

The ground-surface reflection (albedo) significantly influences the amount of solar radiation absorbed by photovoltaic panels and, thus, the optimum tilt angle for maximizing annual energy generation. Nevertheless, the majority of design models presume a constant albedo value, therefore could not accurately represent actual field conditions. This study aims to identify the optimal tilt angle for each albedo value that maximizes the annual energy output of a stationary on-grid photovoltaic system of 20.48 kWp installed in Baghdad, Iraq. Seven albedo values, varying from 0.09 to 0.87, were simulated using PVsyst software, with the reference case established at an albedo of 0.2 and a tilt angle of 31°. The results indicate that the optimum tilt angle is directly proportional to the surface reflection. For albedo levels below the reference of 0.2 (0.18 and 0.09), increased energy generation occurred at reduced tilt angles of 30.5° and 29°, respectively. Conversely, for increased albedo values (i.e., exceeding the reference of 0.2, spanning from 0.25 to 0.87), greater tilt angles were necessitated, reaching 45° at an albedo of 0.87, where the annual energy rose from 35.212 to 36.999 MWh/yr, signifying a 5.07% increase relative to the reference condition. The results validate that the optimal tilt angle fluctuates with ground-surface albedo, as surface reflectivity affects solar irradiation and energy output. Integrating actual albedo values in photovoltaic models is crucial for precise tilt adjustment and enhanced system efficiency.

The technology of Heterogeneous Wireless Sensor Networks (HWSNs) is critical to the efficient operation and deployment of a variety of real-time Internet of Things (IoT) and Mobile Ad hoc Networks (MANETs). It is crucial for reducing overall energy dissipation and ensuring consistent energy distribution throughout the network. Bio-inspired hybrid optimization algorithms are emerging as a possible option for overcoming basic difficulties in Wireless Sensor Networks (WSNs), with a focus on sensor lifespan restrictions. A significant topic that must be considered prior to network configuration is attaining energy efficiency and optimal communication. Several papers have been published on the use of bio-inspired algorithms in WSNs. Few articles, however, addressed the hybrid strategy for routing and clustering in WSNs with communication. This research focuses on hybrid bio-inspired optimization algorithms and elaborates on their taxonomy and problem domains in WSNs. Furthermore, we explored and investigated the hybridization of the Whale Optimization Algorithm (WOA) with other meta-heuristic algorithms such as the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and others. This review can assist researchers in exploring the uses of such algorithms within and outside of this study area.

The antifungal activity of 2-Amino-5-(3-nitrophenyl)-1,3,4-thiadiazole is investigated against Aspergillus niger using combined experimental and computational approaches. The compound is synthesized via a one-step cyclization reaction and structurally characterized using FT-IR and ¹H NMR spectroscopy, confirming its chemical integrity. In vitro agar well-diffusion assays demonstrated significant, concentration-dependent inhibition of fungal growth, with the highest activity observed at 5000 µg/mL. Density Functional Theory (DFT) calculations revealed a HOMO–LUMO gap of 3.709 eV and intramolecular charge transfer characteristics, suggesting electronic stability and reactivity. Homology modeling of A. niger sterol 14-α-demethylase (CYP51) is performed using CYP51B from Aspergillus fumigatus (PDB ID: 6CR2) as the template, and model validation using MolProbity and QMEANDisCo confirmed high stereochemical quality. Molecular docking showed favorable binding interactions of the compound within the heme-containing active site, while QSAR analysis further supported structure-activity relationships correlating molecular features with antifungal potency. Collectively, these results highlight 2-Amino-5-(3-nitrophenyl)-1,3,4-thiadiazole as a promising lead for antifungal drug development against A. niger. From a nanotechnology perspective, the molecular size, electronic structure, and surface-interaction capability of 2-Amino-5-(3-nitrophenyl)-1,3,4-thiadiazole suggest its suitability for incorporation into nano-enabled antifungal systems. The compound’s favorable electronic properties, strong binding affinity to CYP51, and concentration-dependent biological activity support its potential use in nanoparticle-based delivery platforms or nano-structured coatings aimed at enhancing antifungal efficacy and selectivity. These findings highlight the relevance of the present study for the rational design of nanotechnology-driven antifungal therapeutics.

General Background Placenta previa is a major obstetric condition associated with antepartum hemorrhage and adverse maternal and neonatal outcomes. Specific Background Rising cesarean section rates and increasing maternal age have contributed to higher reported cases, particularly in high-risk hospital settings. Knowledge Gap Limited region-specific data are available regarding placenta previa prevalence and maternal risk factors among women undergoing cesarean delivery in Iraq. Aims This study aimed to estimate the prevalence of placenta previa and identify associated maternal risk factors among cesarean deliveries in AL-Muthanna and AL-Diwaniya, Iraq. Results A retrospective cross-sectional analysis of 300 cesarean deliveries identified placenta previa in 15% of cases, with advanced maternal age, multiple previous cesarean sections, high parity, elevated body mass index, anterior placental location, and comorbidities showing significant associations. Novelty This study provides stratified regional evidence linking surgical history and maternal characteristics to placenta previa within a high-cesarean population. Implications Early identification of high-risk women may support tailored antenatal surveillance and surgical planning to reduce maternal complications. Keywords: Placenta Previa, Cesarean Section, Maternal Age, Obstetric Risk Factors, Pregnancy Complications Key Findings Highlights: Placenta previa was identified in a notable proportion of cesarean deliveries within the study population. Advanced maternal age and repeated surgical deliveries showed strong statistical associations. Stratified analysis clarified maternal and placental characteristics linked to higher clinical risk.

The purpose of this study was to evaluate the effect of early feeding with probiotic-fermented feed on growth performance, intestinal microbiota structure, immune responses, and gene expression. Two hundred and forty-one-day-old African ostrich chicks were randomly divided into three groups (eight replicates/group). The control group was fed a basal diet (CON), whereas the PELF3 and PELF6 groups were fed the probiotic-fermented feed for the first 3 or 6 days post-hatching, respectively, after which, all chicks were fed the basal diet for 56 days. The results showed that adding PELF3 or PELF6 significantly enhanced body weight gain and the feed conversion ratio. Chicks fed PELF had higher superoxide dismutase (SOD, p < 0.05), immunoglobulin A (IgA), and IL-10 levels and lower IL-6 and malondialdehyde (MDA, p < 0.05) levels than those fed CON. Plasma cholesterol, low-density lipoprotein (LDL), creatinine, uric acid, and alanine aminotransferase (ALT) levels decreased; however, high-density lipoprotein (HDL, p < 0.05) levels increased in the PELF groups. The addition of PELF reduced the pathogenic counts in the intestines of chicks (p < 0.05). Moreover, increased expression of IGF-1 and MUC-2 genes was observed in the PELF3 and PELF6 groups, whereas the expression of SLC15A1 increased in the PELF6 group. In conclusion, growth performance, immunity, gene expression, oxidative stability, and gut microbiota can all be significantly enhanced by early feeding with PELF. This study demonstrated an effective technique for applying early feeding of PELF in ostrich chicks.

The rapid growth of the Internet of Things (IoT) expands the attack surface and increases the need for effective intrusion detection under strict privacy and resource constraints. Conventional intrusion detection systems (IDSs) commonly rely on centralized data collection, which can expose sensitive traffic data, increase communication overhead, and create single points of failure. This paper presents a federated learning-based IDS (FL-IDS) that trains a convolutional neural network (CNN) collaboratively across distributed IoT clients without sharing raw traffic data. Each client performs local training on its private data, and a central server aggregates model updates using Federated Averaging (FedAvg) to form a global model. We evaluate the proposed FL-IDS on the Bot-IoT dataset after preprocessing and balancing class via under sampling. Experimental results show that the global model achieves 98.12% accuracy with strong precision, recall, and F1-score, and the learning curves indicate stable convergence across clients. The study demonstrates the feasibility of combining federated learning with deep models for privacy-preserving intrusion detection in distributed IoT settings, while also highlighting key limitations and directions for future work such as evaluation under non-IID client distributions and computational cost analysis.