Dijlah University College is a general university in Iraq. The university was founded in Baghdad according to decree No. 3322, issued by the Ministry of Higher Education and Scientific Research on 27 October 2004 in accordance with the law establishing private colleges and universities No. 13 of 1996..
Steel guardrails on expressways are a vital piece of traffic safety infrastructure. Unexpected events, such as accidents, might cause a freight vehicle travelling on the road to lose control. Because it may prevent the freight vehicle from speeding off the road, a steel guardrail can help keep the driver safe. As a result, the steel guardrail’s guiding ability, anti-collision performance and safety behaviour are crucial indices to measure expressway steel safety in collision accidents between freight vehicles and the steel guardrails. In this study, finite element (FE) simulation is carried out on the collision between freight cars and an expressway three-wave steel guardrail. A two-wave beam steel guardrail has been compared to the simulation results. The dynamic simulation results were predicted using the LS-Dyna FE simulator at a speed of 90 km/h and impact angles of 10°, 15°, 20°, 25°, and 30°. To estimate the steel guardrail’s protective function in real-time experimental approaches, freight vehicles clash with steel guardrails on expressways, resulting in the steel guardrail collapsing and the freight car rushing off the road. To accurately anticipate the safety of highway steel guardrails, FE modelling is the best option. The expressway three-wave steel guardrail absorbs more than 60% of the freight car’s principal translational momentum in a collision, reducing collision force transmitted to passengers and highway accident severity.
This research article presents an experimental investigation of a two-stage scroll compressor using R32 refrigerant, focusing on the effects of vapor injection in heat pump applications. The study highlights the performance improvements achieved through vapor injection, with particular emphasis on operation at low ambient temperatures. Key performance indicators—heating capacity, coefficient of performance (COP), and discharge temperature—were selected to evaluate system efficiency. Notably, at an ambient temperature of − 20 °C, the vapor-injected system exhibited a 17.01
The COVID-19 pandemic has created unique challenges for health systems worldwide, requiring rapid and effective tools to forecast patient outcomes. This research proposes a Neuro-Fuzzy Inference System (NFIS) as a hybrid intelligent system to predict COVID-19 patient prognosis based on clinical, demographic, and laboratory information. The NFIS model can handle uncertainty and non-linearity present in medical data through the combined function of the human-like reasoning capability of fuzzy logic along with the ability to learn from experience via artificial neural networks. The system is trained and tested on a dataset including essential indicators such as age, oxygen saturation, comorbidities, CT scan findings, and inflammatory biomarkers. Performance measures such as accuracy, sensitivity, specificity, and F1-score illustrate that the new NFIS model attains high prediction reliability in separating recovery from critical/fatal outcomes. The explainability of the fuzzy rules also enhances clinical decision-making with clear insights into the factors responsible for each prognosis. This study highlights the usefulness of neuro-fuzzy systems as good decision-support systems in the management of COVID-19 and future infectious disease outbreaks.
Purpose The aim of this study was to investigate the clinical characteristics, refractive error profile, and management approaches in Iraqi patients diagnosed with keratoconus (KC), emphasizing the disease severity, sex distribution, and visual outcomes. Patients and methods A retrospective study was conducted on 405 eyes of 405 patients diagnosed with KC at Ibn Al-Haytham Teaching Eye Hospital, Baghdad, Iraq, between January 2022 and January 2025. The collected data included age, sex, KC severity (classified by keratometry), refractive error type and axis orientation, treatment modality (optical or surgical), and uncorrected and best-corrected visual acuity. Results Most patients (57.28%) were females and aged 21–30 years (43.46%). Mild and moderate KC accounted for 38.77 and 32.35% of the cases, respectively. Oblique compound myopic astigmatism was the most prevalent refractive error (42.47%), followed by with-the-rule compound myopic astigmatism (26.67%). Rigid gas-permeable and scleral lenses were the most common treatments used (34.57 and 32.10%, respectively), while 24.69% of the patients underwent corneal collagen cross-linking and 18.52% required surgical intervention. The visual acuity improved significantly after treatment, with 40.99% of the eyes achieving 6/6 best-corrected visual acuity and fewer than 10% remained below 6/18. Conclusion KC in this Iraqi cohort demonstrated a female predominance and a high frequency of oblique compound astigmatism, particularly in early disease. Stage-specific treatment strategies effectively restored the visual function, including early contact lens fitting and collagen cross-linking. These findings support the need for early detection and access to tailored interventions in low-resource settings.
Large Language Models (LLMs) perform excellently on natural language generation tasks but often fail at tasks requiring rich contextual understanding, accurate reasoning, and the use of domain-knowledge. Such limitations can surface as hallucinations, drift in contexts, and classification errors for complex tasks involving summarization, question-answering, or classification. Contextual knowledge graphs are an opportunity because they enable the external enhancement of LLMs via dynamic, context-aware knowledge. This paper investigates the integration of dynamic knowledge graphs into LLMs to enhance their reasoning ability, contextual accuracy, and coherent output generation. It aims to ensure seamless transition into the LLM workflow by developing methods for task-specific, real-time retrieval of relevant data from KGs. The proposal of a contextual relevance filtering algorithm prevents information overload and prioritizes task-relevant knowledge. This study measures the impact of integrating KGs on a plethora of performance metrics such as accuracy, coherence, and context preservation, and majorly, complete evaluation on NLP tasks such as open domain question answering, document classification, and text summarization. In order to search for points of improvement and measure the level of improvement, this set of proposed models were compared against the conventional LLMs. Through the integration of KGs’ structured reasoning skills with LLMs’ dependability and contextual awareness, this study opens up to more reliable and domain-sensitive NLP applications by combining the ability of LLMs to create unstructured text with the organized reasoning skills of KGs.
