Analyzing Thermal Efficiency with Pin Fins, Perforated Bases, SolidWorks 2023 Transient Analysis

A group of fifteen central processing unit (CPU) heat sinks of circular, hexagonal, and rectangular shapes with different f in designs and perforation directions was created and analyzed using SolidWorks 2023. In boundary conditions, a heat flux of 65 W was applied, while the surrounding air was at 25°C, and a heat transfer coefficient of 6 W/m2·K was considered. Perforation caused a weight decrease of up to 67.4% in the heat sinks, while perforation orientations had a greater effect on heat dissipation than perforation size. In heat sinks with vertically perforated pins and base R5, the lowest maximum temperature of 77.5°C, the lowest weight of 412.56 g, and the best heat dissipation occurred

Defining Characteristics of National BIM Adoption

Building information modeling (BIM) has become a transformative force in the Architecture, Engineering, and Construction industry, streamlining project coordination, reducing costs, and promoting sustainability. Governments worldwide are mandating BIM to enhance efficiency and digital transformation in public projects. However, a roadmap of national adoption requires clear regulations, standardized frameworks, and skilled professionals. This study critically examines policy frameworks, legal considerations, and workforce readiness, educational strategies, identifying key enablers and barriers to national BIM implementation. Utilising a qualitative methodology, the research reviews global BIM adoption models, educational strategies, and legal challenges that serve the BIM adoption in Kurdistan Region. Findings emphasize the importance of BIM maturity models, structured training programs, and data interoperability. Recommendations include developing national BIM standards, integrating digital permitting systems, and fostering public-private collaboration. The Kurdistan Region faces regulatory gaps that hinder BIM validation and industry-wide adoption. Addressing these challenges through the Kurdistan National BIM Standards, aligned with global benchmarks such as ISO 19650 and BSI PAS 1192, will drive digital transformation, enhance project efficiency, and position Kurdistan as a leader in digital construction

A Design of Power Amplifier using Enhanced Simple Real Frequency Technique

In this paper, a novel discrete wide-band microstrip power amplifier (PA) is designed and fabricated based on the enhanced simple real frequency technique (ESRFT) to achieve a higher power added efficiency (PAE) over a wide frequency range compared to the conventional simple real frequency technique (SRFT). To compare the ESRFT with the SRFT, a basic PA using the SRFT is designed additionally. The ESRFT applies the feedback effects that play an important role in the PA design process, especially for high-frequency applications. Unlike the SRFT, the ESRFT considers both the back-end and the front-end impedance of the active device, while it is loaded by the designed input matching network (IMN) and the output matching network (OMN), respectively. The parasitic feedback elements of the transistor disturb the designed IMN and OMN. Therefore, an iterative method can converge to a better result. The matching networks (MNs) are modeled as a single matching problem, formulated and calculated in the Richard domain. The MNs are constructed by cascading microstrip unit elements (UEs) to obtain a wide-band simple structure. The characteristic impedances of the UEs are derived using the Richard extraction process. The fabricated PA shows good agreement between the simulation and measurement results. It has a wide-band gain of up to 13 dB from 0.8 GHz to 1.6 GHz, with a maximum PAE of about 70% that has been improved compared to the designed basic PA. Furthermore, the third harmonic is suppressed up to 25 dBc

Immunological Impact of Calprotectin and Interleukin-34 in Immunocompromised Patients with Chronic Cytomegalovirus Infection

Cytomegalovirus (CMV) causes the most prevalent and severe opportunistic infection in immunocompromised patients following solid organ or hematopoietic stem cell transplantation, with the highest morbidity and mortality rates among herpesviruses. The study objective was to compare and determine the CMV chronic infection and related hematological and immunological markers in immunodeficient and immunocompetent participants. A prospective case–control study among 85 participants was designed to measure CMV-immunoglobulin G (IgG) and to evaluate interleukin-34 and serum calprotectin as biomarkers; total leukocyte, granulocyte, lymphocyte, and platelet counts were also measured following blood collection. A high CMV IgG positivity was observed across all groups in this investigation, indicating widespread chronic infection. CMV IgG, interleukin-34 (IL-34), and calprotectin levels did not differ significantly between immunocompetent and immunocompromised individuals. There was no significant association between CMV IgG and IL-34 or serum calprotectin. Furthermore, IL-34 showed a significantly higher mean in males compared to females (p = 0.002). An exploratory observation was that IL-34 had a moderate positive correlation with serum calprotectin (ρ = 0.609, 95% confidence interval: 0.450–0.731, p < 0.001) across all study participants. The study findings call for more research to elucidate the clinical roles of calprotectin and IL-34 in immunocompromised patients

Optimization of Wastewater Aeration Time in Decentralized Sequencing Batch Reactors in Duhok City, Iraq

Aeration costs represent a significant portion of operational expenses (OPEX) in wastewater treatment facilities. Optimizing aeration time can enhance plant sustainability and also contribute to global CO2 reduction goals. However, research on aeration time optimization in full-scale decentralized Sequencing Batch Reactors (SBRs) remains limited. This study presents the first systematic application of BioWin to evaluate the impact of aeration time optimization on energy consumption, operational costs, and CO2 eemissions in full-scale decentralized SBR plants treating domestic wastewater. Operational, laboratory, and field data were used to develop influent profiles and simulation models. BioWin’s simulation based results indicated that aeration in the equalization (EQ) basin is not required to meet Iraqi effluent standards and that bioreactor aeration times can be optimized without compromising treatment performance, indicating that existing SBR systems are over-aerated. Optimized aeration time resulted in 58.70% reductions in energy use, operating costs, and CO2 emissions, resulting in annual OPEX cost savings of 234,049,308 IQD and demonstrating that aeration time optimization is a practical and cost-effective approach for improving the sustainability of decentralized SBR plants

Biofabrication and Characterization of Zinc Oxide Nanoparticles Using Staphylococcus aureus

Due to their large surface area and catalytic properties, zinc oxide nanoparticles (ZnO-NPs) are highly effective in biological applications. In this study, ZnO-NPs were biofabricated using the American Type Culture Collection Staphylococcus aureus (ATCC 25923) and characterized by double-beam ultraviolet-visible spectroscopy, with a characteristic absorption peak at 351 nm, verifying the synthesis of ZnO-NPs. Proteins and carboxyl and hydroxyl groups that function as reducing and stabilizing agents were found on the surface of biosynthesized ZnO-NPs, as revealed by Fourier transform infrared analysis. The hexagonal structure was validated by X-ray diffraction analysis. Spherical shape confirmed by Field Emission Scanning Electron Microscopy, and the main elements detected by energy-dispersive spectroscopy were Zinc 79.91% and oxygen 18.33%. Transmission electron microscopy investigation revealed that the ZnO-NPs produced were predominantly quasi-spherical to irregular shape, with diameters in the nanoscale range (approximately 40–60 nm). The results indicated that the diameter of inhibitory zones against methicillinresistant S. aureus (MRSA) using varying concentrations of 50, 25, 12.5, 6.25, and 3.125 μg/mL of ZnO-NPs in an agar well diffusion test ranged from 7 to 24 mm, and the cytotoxicity of ZnO-NPs was assessed by MTT assay. Human embryonic kidney 293 cells were used. Our research demonstrates that biofabricated ZnO-NPs using standard S. aureus exhibit successful characterization and strong anti-MRSA activity, which may represent a promising path for the development of innovative antimicrobial agents, particularly for treating MRSA as multidrug-resistant isolates

Documentation and Classification of Heritage Buildings’ Styles through Machine Learning: Dohuk’s City Center as a Case Study

Duhok, a city in Kurdistan Region of Iraq, has important historical and cultural evident in its archaeological sites. The city center contains many neglected heritage buildings that necessitate documenting as a crucial preliminary step for their preservation, facilitating a comprehensive understanding of their architectural, historical, and social significance. Consequently, the study aims to document buildings in the study region employing several documentation methods and looking at various aspects, such as architectural, historical, social, and cultural dimensions. The second goal of the study is to build an automated model utilizing advanced machine learning techniques, such as convolutional neural networks, transfer learning, and neural architectural search, to create a robust model for identifying and classifying architectural styles across various regions of the Kurdistan Region. The initials result of the study shows the unique attributes of Islamic, vernacular, modern, and postmodern architectural styles within Duhok’s legacy. The machine learning categorization of the model is very accurate, highlighting its potential as a reliable analytical tool for identifying and classifying architectural styles in Duhok city and across Kurdistan Region

Improving Breast Cancer Classification with Adaptive Synthetic Sampling, Feature Selection, and Hyperparameter Optimization

Breast cancer is a major global health concern, highlighting the need for accurate and efficient diagnostic solutions rather than persistent issues with detection accuracy. This study presents an enhanced machine learning framework to improve breast cancer classification by addressing key limitations: Class imbalance, irrelevant features, and suboptimal hyperparameters. Adaptive synthetic sampling (ADASYN) was used to balance class distribution and various feature selection techniques. Univariate Selection and recursive feature elimination improved feature relevance, and arctic puffin optimization (APO) was applied for hyperparameter tuning. Multiple classifiers were evaluated using the Wisconsin Diagnostic Breast Cancer dataset. The random forest (RF) with ADASYN approach, optimized using APO, achieved outstanding results – 99.53% accuracy, 100% precision, 99.07% recall, and 99.53% F1-score – with only one misclassification out of 569 samples. This framework, while not modifying ADASYN or RF algorithms themselves, significantly enhances diagnostic performance and serves as a robust foundation for clinical decision support systems

AraFashion: A Novel Fashion Captioning Dataset Leveraging Attention-Based EfficientNet and xLSTM

The significance of creating models that can produce precise textual descriptions of photographs has become apparent, particularly in specialized domains such as fashion. Arabic suffers from a severe shortage of publicly available resources, particularly fashion picture databases, in contrast to the wealth of databases and studies about the English language. This restricts the creation of Arabic language models and impedes scholarly research in this area. By creating a hybrid model for automatically producing Arabic descriptions of fashion photos, our study seeks to close this gap. Based on the EfficientNet-B4 architecture, this model incorporates an attention mechanism to extract visual features and, for the first time in this field, links it to an xLSTM module for text creation. This study produced a new dataset with Arabic captions called AraFashion; the Arabic descriptions were translated into English through Google Translate. Using real Arabic data improves the model’s accuracy and realism, as seen by the model’s top BLEU-1 score of 0.7335 for Arabic descriptions. This study suggests growing Arabic databases in the fashion industry and highlights the need to support the Arabic language in AI technology

Prevalence and Molecular Diagnosis of Cryptosporidium ssp. and Giardia lamblia in Fresh Vegetables from Kurdistan Region/Iraq

Giardia lamblia and Cryptosporidium species are common intestinal parasites that contaminate fresh vegetables and create significant public health problems. This study investigates the prevalence and genetic diversity of six fresh vegetables sourced from agricultural fields and markets in the KRG-Iraq. The vegetable samples (n = 210) are obtained from local farms and markets during summer and autumn, 2024, including garden cress (Lepidium sativum), leek (Allium ampeloprasum var. porrum), lettuce (Lactuca sativa), parsley (Petroselinum crispum), spinach (Spinacia oleracea), and rocket (Eruca vesicaria).  Samples are examined microscopically, stained with acid-fast stain, and subjected to molecular identification through polymerase chain reaction, followed by nucleotide sequencing. Molecular analysis revealed that 75 samples (35.7%) tested positive for Cryptosporidium, whereas 16 samples (7.6%) tested positive for Giardia. Cryptosporidium exhibited a higher prevalence. Garden cress had the highest level of contamination, with 68.6% of its samples testing positive for both parasites. Lettuce and leek exhibited the lowest percentage, approximately 11%. Sequencing identified the isolates as Cryptosporidium parvum and G. lamblia. A significant correlation (p < 0.05) existed between the kind of vegetable and the incidence of Cryptosporidium cases; however, no such correlation was observed between the type of vegetable and the incidence of Giardia cases. Overall, this study provides important information on the frequency of Cryptosporidium spp. and G. lamblia detection in six common vegetables consumed in Kurdistan, Iraq. Moreover, it shows how important molecular identification is for correctly identifying species and coming up with good ways to stop the spread of these diseases through food