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Cutting Digital Images Using Curvelet Transform Millimeter
Get PDFImage fragmentation into non-overlapping areas where the boundaries contribute significantly in the utilization of the image in many areas of study like medical, imaging, and military technology. Curvelet transform method is used here due to its great possibilities and many advantages in preserving the borders of the image and its edges, in addition to its ability to capture information for these borders or soft edges in the image which helps draw the geography of territory and borders that contradict each other but within the limits of the overall picture. This research suggests the use of a normalized cut algorithm, which depends on fragmenting the selected image that was selected and converted using curvelet transform This provides a clear definition of the edges in the image and is cut into a number of sections required after calculating the eigenvalue and eigenvector by divide The image to several sections required, the section to be studied in detail after being partially deducted from the original image identified and distinguished by the proposed algorithm the image which can give high-quality discrimination and accuracy, which is intended to this conclusion.
Challenges and Opportunities in Assistive Audio Recognition Technologies for Deaf and Hard-of-Hearing (DHH)
No full textThe advancements in assistive audio recognition technologies have progressed rapidly and have improved the accessibility for the Deaf and Hard-of-Hearing (DHH) community. This paper provides an extensive review of the current methods, including traditional techniques such as GMMs and HMMs, and modern deep learning-based techniques as Convolutional Neural Networks (CNN) and EfficientNet. The strengths and limitations of these methods are compared with respect to accuracy, computational efficiency, and noise robustness. Although these deep learning models have higher recognition rates, they impose significant computational requirements thereby restricting their usage in real-time and low-power devices. The review also revealed research gaps with a strong emphasis on energy-aware neural networks and their potential applications in smart environments, adaption strategies, and integration of IoT technol-ogies for practical implementation. As deep learning models grow in complexity and the required amount of labeled data increased to unsustainable level, future research will need to explore hybrid models that balance between performance gains and efficiency, to ensure that assistive audio recognition systems become increasingly reliable, usable, and generalizable in a broad range of acoustic environments
Designing a Non-Invasive Anemia Detection System for Sustainable Healthcare
No full textTechnology is revolutionizing healthcare by improving non-invasive medical techniques for diagnosing diseases, as disease images are crucial for numerous medical diagnoses. One of the common illnesses that affects people's health worldwide, particularly children and women of childbearing age, is anemia. By using cutting-edge technology to handle this problem, the prevalence will be significantly lower. A disorder known as anemia occurs when the blood's hemoglobin content falls below normal. In this work, we created a system to find people who are anemic using Mobile Net’s deep learning models, versions 2, 3 small, and 3 large. These models were trained and tested on created a dataset of 10,636 color palm images of adults that were labeled as either anemic or not anemic. The high training was Mobile Net v2 accuracy 99.9%, while v3s 81%, v3L 73.7%, and the best test results were in Mobile Net v2 (accuracy 95.77%, precision 96.05%, recall 95.51%, f1-score 95.78%). We have created models to evaluate medical images, automate estimations, reduce diagnosis time and error, and lower fatality rates. They support the sustainable development objectives, especially SDG 3, by encouraging early intervention for better patient quality of life
Improving the Mechanical Properties of Wind Turbine Blades Using Hybrid Composite Materials
No full textThis research investigates?the mechanical properties improvement of a wind turbine blade in different operating conditions using a hybrid composite reinforced with Kevlar fibers. Three samples of this material were manufactured using a hand layup method with various configurations: Sample 1 (Kevlar fibres with epoxy), Sample 2 (Kevlar with epoxy and iron powder), and Sample 3 (Kevlar with epoxy and graphite and iron powders). All samples were cut and machined according to ASTM D3039 and ASTM G65 standards. The three samples were then subjected to Wear, hardness, and tensile tests to verify their performance. The results showed that Sample 2 had the highest hardness (70 Vickers), indicating its high resistance to deformation. Meanwhile, Sample 1 exhibited the lowest wear rate (< 3.093 × 10?? g/cm³), indicating its high wear resistance. During tensile tests, the first sample achieved the highest strength in both the 90° (139.42 MPa) and 45° (237.5 MPa) fiber directions, with corresponding maximum loads of 2.9 kN and 5.7 % strain at failure in the 45° orientation. Adding iron and graphite powders had a minimal effect on mechanical performance, with Sample 1 exhibiting the best combination in terms of hardness, wear resistance, and tensile strength for use in wind turbine blades
Strategic Engineering Techniques and Their role in Activating The Sustainable Lean Manufacturing System /A Diagnostic and Analytical study of the opinions of a sample of employees in the General Company for Industrial Cement Industry/Badush Factory
No full textThis study aims to examine the relationship and impact between the dimensions of strategic engineering and sustainable lean manufacturing. The research was conducted at the General Company for Cement Industry / Badoush Cement Expansion Plant, based on a sample of (213) department managers, division officials, and engineers. A questionnaire was used as the primary instrument for data collection, and the data were analyzed using advanced statistical techniques through SPSS and AMOS software. The theoretical framework focused on strategic engineering and sustainable lean manufacturing, while the empirical framework tested the correlation and causal relationships between the study variables. The findings indicate that the company demonstrates a strong orientation toward adopting strategic engineering practices, which significantly enhance sustainable manufacturing agility. Moreover, the results highlight the importance of achieving high levels of manufacturing flexibility while integrating environmental considerations, thereby improving the company’s ability to meet customer requirements, ensure production continuity, and effectively respond to future environmental and market change
Diagnosing the Dimensions of Employees’ Creative Behavior: An Exploratory Study of Opinions of a Sample of Employees at University of Fallujah
No full textThis study aims to examine the dimensions of employees' creative behavior, )exploring opportunities, generating ideas, promoting ideas, and implementing ideas( at the University of Fallujah, Anbar Governorate, as one of the institutions of higher education in Iraq, and given the importance of this topic in the current situation. It seeks to assess the extent to which these dimensions are present among the university's employees. Adopting a descriptive-analytical approach, the study surveyed a sample of 261 employees using a questionnaire as the primary data collection tool. Data analysis was conducted using ( SPSS, Ver, 26) and various statistical methods, including frequency distributions, percentages, arithmetic mean, standard deviation, coefficient of variation, and response rate. The results confirmed the presence of employees' creative behavior dimensions within the studied organization. Based on the findings, the study recommends supporting employees who exhibit voluntary and creative behaviors through both material and non-material incentives
The availability of open innovation dimensions in the State Company for Northern Cement Industry An applied study in Badoush Cement Factory
No full textThis research aims to assess the extent of open innovation dimensions in the Badoush Cement Plant Expansion by analyzing the degree of collaboration with external parties. The current research problem arises from a main question: What are the perceptions of the research sample regarding the availability of open innovation dimensions in the Badoush Cement Plant, which is the subject of the study? To address this, the researchers adopted a descriptive approach, using a checklist as the main tool for data collection. This checklist was distributed to a sample of the workforce at the plant, with 124 valid responses collected from an initial 127 distributed questionnaires. The data were analyzed using the statistical programs (SPSS v26) and (PLS-Smart v3). The analysis led to several conclusions, including the availability of open innovation dimensions at the studied company. However, this availability was accompanied by some variation in these dimensions, which leads to the proposal that the plant management should increase its focus on open innovation dimensions due to their importance in various activities and processes within the plant
Prevalence of Epstein-Barr Virus Infections among Inflammatory Bowel Disease patients in Kirkuk City/Iraq
Get PDFChildren and adults worldwide are significantly impacted by Epstein-Barr virus infection. The illness typically stays dormant and is well-managed in people with robust immune systems. However, the virus can cause life-threatening infections in those with weakened immune systems, such as those with inflammatory bowel disease (ulcerative colitis and Crohn's disease,). This study aims to evaluate the presence of VCA IgM, IgG, and EBNA-1 antibodies against the Epstein-Barr virus in inflammatory bowel disease patients and explore potential connections between the prevalence of these antibodies, age, and sex. The research carried out in Kirkuk City included 100 individuals (56 patients with ulcerative colitis and 44 with Crohn's disease) and a control group of 100 individuals. Blood samples were collected from all participants in this study and subjected to analysis using the ELISA technique. EBV seropositivity was significantly higher in IBD patients (UC 76.7%, p-value <0.001, and CD 70.4% p-value 0.002) than in controls (6%) (p-value= 1.0). The highest seroprevalence occurred in the 31–40 age group (UC: 30.2%, CD: 38.7%) with a p-value <0.005. Males showed higher EBV positivity than females in Both UC 69.7%, p-value 0.008, and CD: 58.1% with a p-value < 0.002
Schiff base complexes derived from Trimethoprim
Get PDFTrimethoprim (TMP) is currently frequently utilized in modern medicine due to its organic features. Trimethoprim inhibits vulnerable organisms from converting dihydrofolate to tetrahydrofolate, which is the active form of folic acid. Its heterocyclic substances are crucial to the fields of medicine and pharmacology by reacting to their Schiff base derivatives with metals. The preparation and detailed analysis of these compounds were conducted using diverse physicochemical techniques, including measurements of electronic conductivity, UV-visible spectral analysis,1H-NMR, 13C-NMR spectroscopic studies, atomic absorption evaluations and CHN elemental composition determination. The antimicrobial activity of TMP-based compounds was demonstrated by their ability to suppress the development of pathogenic microorganisms, including Gram-positive (G+) strains such as Staphylococcus pyogenes and Bacillus subtilis, alongside Gram-negative strains like Shigella flexneri
Leveraging Deep Learning for Anemia Detection Using Palm Images: Innovative Solutions for Sustainable Development
No full textA wide variety of medical diagnoses depend on the study and testing of the dis-ease images. Anemia is one of the cases separated in the populations. Deep Learning (DL) is one of the subfields of artificial intelligence (AI) techniques applied in the healthcare system to diagnose diseases. In this work, YOLOv11 with its versions Nano (n), Small (s), Medium (m), Large (l), and Extra-large (x) produces deep-learning models to detect anemia based on the palm images. The models train on 4260 color images of children under 5 years labeled anemic and nonanemic. The models train using two different dataset splits, with two input image sizes, (64x64) and (128x128). The high training accuracy achieves at YOLOv11n 99.3% at the group two dataset with an input image size of 128 and YOLOv11n 98.9% at the group one dataset with an input image size of 128. All models are test, and the best test results obtains with the Yolov11n models, 98.9% and 99.5% of the two groups at input image size 128, which predicted correctly with a high percentage in a very short time. We produce these models to assess medical images, providing precise automated estimations and reducing diagnosis time and errors. Additionally, they help reduce death rates and promote early intervention to enhance the quality of life for patients