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Estimated serum Interleukin-2 levels in beta-thalassemia patients infected with Hepatitis B and C viruses
Get PDFBeta thalassemia is the most common inheritable disease worldwide. Thalassemia patients are more susceptible to blood-borne viral infections, such as hepatitis B (HBV) and hepatitis C (HCV), because of the frequency of blood transfusions. This study aimed to detect HBV and HCV infections among patients with beta-thalassemia and estimate interleukin-2 (IL-2) levels in the serum of infected patients compared with a control group. The study was conducted on a total of 151 patients (83 males and 68 females). HBs antigen and anti-HCV antibody tests were performed for the detection of HBV and HCV infections using enzyme-linked immunosorbent assays (ELISA). Of the 151 thalassemia patients, 12 (7.9%) had HCV (10 males [6.6%] and 2 females [1.3%]), and none of them was HBsAg positive. There were significant increases in IL-2 levels (Mean 159.33 pg/ml) in HCV-infected thalassemia patients compared with the control group (38.67 pg/ml), (P < 0.05). A statistically significant relationship was found between HCV infection and the frequency of transfusions and splenectomy in patients with thalassemia (P < 0.05). No statistically significant relationship was found between HCV infection and either type of beta-thalassemia or residence (P > 0.05). The current study demonstrated that there were significant increases in serum IL-2 levels in patients with thalassemia infected with HCV compared with the healthy control group. There were statistically significant relationships between transfusion frequency, splenectomy, and HCV infection in thalassemia patients. The types of beta-thalassemia and residency were not statistically significantly related to HCV infection in thalassemia patients
Energy-Efficient HVAC Technologies and Strategies:A Comprehensive Overview
Get PDFThis review paper examines state-of-the-art energy-efficiency technologies and techniques in the heating, ventilation, and air conditioning (HVAC) field. Specifically, the analysis focuses on the following advanced technologies: Variable Refrigerant Flow (VRF) systems, Thermal Energy Storage (TES) systems, renewable energy integration (solar and geothermal), drying cooling, and state-of-the-art control technologies. This analysis focuses on the systematic study of the relevant peer-reviewed articles available in the Scopus, Web of Science, and ScienceDirect databases released between the years 2010 and 2024. The primary methodology included in the analysis was the use of experimental studies and high-fidelity simulations, which provided the evaluated criteria on which the research was premised. The advanced energy systems are more energy-efficient than the traditional air conditioning systems, and the research provides clear, demonstrable findings of this claim. For example, energy savings in cooling load use from VRF systems were around 70%. This rivew work also provided the geothermal heat pumps (GSHPs) of which the author focuses on a single performance parameter in this climate model: the GSHPs of which despite the high initial capital expenditure are the most energy efficient systems in extreme climates, which are the most energy. The review also provides evidence of wide scope limitations, including: A global absence of long-term comparative studies on the most mature systems (VRF and GSHP) in a variety of buildings, and a lack of comprehensive economic models, which effectively incorporate lifecycle analysis LCA and the local cost of electricity to facilitate meaningful investment. This review additionally provides an analysis of energy-efficient air conditioning technology, evaluates its performance, and suggests future research to fill the remaining empirical and knowledge-based economic voids
Performance Analysis of Network Efficiency Based on Multi-Level Encryption Algorithms
No full textWith an increasing rate of data exchange via networks rapidly, information security is a critical indicator of network effectiveness. Data transmission has to be secured, and that is crucially done through cryptography, where AES, DES, and multi-level encryption (AES+DES) are the significant methods. This research provides a comparative performance analysis of AES and DES. Execution times for encryption and decryption, efficiency ratios, and memory usage were recorded using various dataset sizes (1, 10, 100, 1,000, 10,000, and 50,000 images). Results show that AES is faster than DES in all cases with less memory. Experiments were conducted on both CPU and GPU: Results indicate that GPU acceleration does make a difference in accelerating encryption with up to a (6.68×) speedup for multi-level encryption and (8.13×) for AES with 50,000 images. GPU-memory usage was (35%) less than the respective CPU-based memory, thus being more efficient. Multi-level encryption presents a potential trade-off for more robust security, while DES is insufficient for bulk encryption because its performance is considerably lowered
The Reality of Job Security Among Employees at Northern Technical University and Its Affiliated Formations in Nineveh Governorate/ An Analytical Study
No full textThis Study Seeks to Answer a Central Research Question: to What Extent dose the Adoption of Strategic Vigilance Practices Contribute to Achieving Job Security Among University Employees? The Research Aims to Explore the Relationship Between Strategic Vigilance and Job Security, Focusing on the Four Key Dimensions of Job Security: Human, Social, Ethical, and Economic. the Investigation is Conducted Within the Academic Context of the Northern Technical University and its Affiliated Institutions in Nineveh Governorate.
To Address this Question, the Study Employed a Descriptive- Analytical Methodology, Using a Structured Questionnaire as the Primary Tool for Data Collection. The Questionnaire was Distributed to a Randomly Selected Sample of 311 Employees. Data Analysis was Conducted Using SPSS V26 and AMOS V24, and Included Various Statistical Methods such as Means, Standard Deviations, Frequencies, Response Rates, Coefficients of Variation, and Path Analysis for Correlation and Impact Measurement.
The Results Revealed a Strong and Positive Correlation Between the Dimensions of Strategic Vigilance and the Four Components of Job Security. These Findings Indicate that the Enhancement of Strategic Vigilance Practices Within the University Contributes Significantly to Reinforcing Job Stability and a Supportive Work Environment. The Study Concludes that Aligning Foresight-Driven Strategies with Employees’ Human and Organizational Needs is Essential for Promoting Organizational Sustainability and Long- Term Workforce Retention in Higher Education Institutions.
The Reality of Change Management Strategies According to SWOT Analysis: A Case Study of the Central Library at the Northern Technical University
No full textThe study aimed to explore change management strategies and their impact on employees at the Central Library of the Northern Technical University, as well as to understand employee performance and its relationship with the use of technology in completing tasks. The researcher used personal interviews for data collection and employed the descriptive-analytical method, along with the case study method, To enhance the practical aspect of the study, a SWOT analysis was conducted to identify the strengths and weaknesses within the internal environment of the library, as well as to identify the opportunities and threats it faces. This analysis contributes to understanding the current state of change management and provides insights into the future trends of the Central Library in light of technological and organizational changes The study arrived at a set of findings, the most important of which are as follows.
The interview results showed that approximately 82% of the library staff demonstrated a clear willingness to embrace change and implement modern work strategies, reflecting a positive work environment that encourages adaptation to technological and organizational transformations.
The findings revealed that 40% of the services provided by the library are still executed using manual methods, underscoring the urgent need to accelerate the digital transformation by developing electronic systems and increasing reliance on the internet in administrative procedures.
A Novel Face Emotion Recognition Based on Lite ResNet-50 for Embedded Systems
No full text Face Emotion Recognition (FER) is essential for improving human-computer interaction. Although deep learning has greatly enhanced the accuracy of FER, deploying these models on embedded devices is challenging due to limitations in computational power and memory. This study proposes a lite and effective FER system for real-time implementation in resource-constrained environments. Lite ResNet-50 and MobileNetV2 were compared for the classification of neutral and angry emotions using a carefully selected dataset. The focus was on measuring accuracy, inference speed, and resource efficiency. Real-time testing is also con-ducted to confirm their applicability in practice. The results show that Lite Res-Net-50 outperforms MobileNetV2 in all key areas, achieving an accuracy of 99.8, inference speeds of 229 ms and with 2 FPS. These findings establish Lite Res-Net-50 as the optimal choice for FER on embedded devices, bridging the gap be-tween deep learning advancements and real-world deployment to improve hu-man-computer interaction
Fault Classification and Localization in Power Transmission Lines Using LSTM and Vibration Data Analysis
No full textThe precise classification and localization of faults in power transmission lines are very important to ensure grid stability and to minimize service interruptions. Traditional methods of fault detection frequently have difficulty with complicated fault scenarios, faults of high impedance, and varying conditions of operation. Throughout this paper, an approach of a new machine learning-based for fault classification and localization in power transmission lines adopting Long Short-Term Memory (LSTM) networks and vibration data analysis is proposed. In contrast with conventional impedance-based and methods of traveling wave, the proposed approach takes advantage of temporal dependencies within vibration signals for enhancing predictive precision. Through capturing different faults types, simulation as well as location, a dataset of fault conditions is being generated. For recognizing distinguishing fault patterns and predicting location’s fault with high accuracy, LSTM-based model we proposed is trained. The experimental results show how the LSTM model is superior in dealing with sequential data and, subsequently, improves fault localization precision. Moreover, experimental outcomes confirm that the proposed approach is robust, which leads to high classification precision and minimum localization error. The findings show the potentiality of vibration-based machine learning models to revolutionize fault management within power grids, which offers a solution that is more adaptive and data-driven to the challenges of fault classification and localization
Enhancing Alzheimer’s Disease Classification by Employing Deep Learning and Optimization
No full textAlzheimer's Disease (AD) is a progressive neurological disorder that leads to the deterioration of memory and cognitive abilities due to damage brain's nerve cells. Deep Learning (DL) techniques can provide effective AD classification by using medical imaging data. In this study, a DL technique based on Convolutional Neural Networks (CNN) is established. Keras-Tuner Optimization (KTO) is applied since it is difficult to propose a CNN with the suitable architecture. The aim of the proposed Optimized CNN (OCNN) model is to classify AD into four groups Mild Dementia (MD), Moderate Dementia (MoD), Non-Dementia (ND), and Very Mild Dementia (VMD). Two datasets are utilized here namely: Best Alzheimer Magnetic Resonance Imaging (BA-MRI) and Alzheimer’s Disease Magnetic Resonance Imaging (AD-MRI). The Kaggle platform is the source and collection point for both datasets. After extensive implementations and OCNN models training, high classification accuracies of 92.44% and 93.17% are achieved for the AD-MRI and BA-MRI datasets, respectively
“The Language of Manipulation in Arabic Phishing Texts”
No full text?????? ??? ??????? ????? ????? ????? ?? ??????? ??? ??????? ??? ???? ??????? ????????? ???????? ?????? ???????. ????? ??????? ??? ????? ???? ????????????? ????????? ??????????? ???? ?? ???? ????? ????? ???? ????? ????? ?? ???? ???? ?????? ?? ????? ???????. ???? ????? ????? ????????? ?? ?? ?????? ?? ????? ????? ?????? ???? ?????? ??? ????? ??????? ?? ??????????. ??? ????? ??????? ?? ??????????? ??????? ???????? (Pathos) ????? ?????? ????????? ?? ?????? ????? ???????.
??? ?? ????? ??????? ???? (???????? ????? ?? Google Forms) ??????? ????? ??????? ?? ???? ??? ?????? ??? ??? ???????. ???? ?? ????????? ????? ??????? ????? ????? ?? ?????? ??? ????? ???????. ??? ?? ????? ????????? ?? ?????? ?? ????? ??????? ??????? ?? ????? ????????? ?? ?????? (Logos)? ?? ???????? ??? ????? ???????? ?? ?????? ?????? ???????? ???? ??????? ?? ???????.
????? ??? ?????? ??? ?? ??????? ??????? ?????? ????????? ?? ???? ??????? ???? ???????? ?????? ?????? ?? ??????? ??? ???????. ?????? ??? ???????? ??? ??? ??????? ?????????? (?????? ?????????) ???? ??????? ?????? ??? ???? ?????? ??????? ?????????.This study examines the utilization of language to influence readers in Arabic phishing texts. The objective was to discern the primary persuasive strategies and conduct a manual analysis of a collection of 10 authentic phishing texts. Following the deconstruction of the language in each message, a basic statistical analysis was conducted to determine the frequency of each strategy's occurrence. The findings indicated that framing and pathos were the predominant strategies employed to persuade victims. A brief survey (Questionnaire Google form) was conducted to compare these findings with real responses to these messages. Participants were instructed to rank the same 10 messages from most to least compelling. The results did not align with the manual analysis. Participants determined that logos (Appealing to Logic and Rationality)was the most persuasive, rather than framing and pathos. This difference indicates that the most often employed elements in the text do not necessarily have the most effective results on individuals, and both analytical approaches are crucial for a comprehensive understanding of phishing mechanisms
Using immersive marketing techniques to improve and develop product designs (A field study in a selected group of interior decoration companies in Nineveh)
No full textThe current research aims to test the relationship between immersive marketing techniques and their impact on product design and development, as the field of marketing has witnessed remarkable development with the emergence of immersive marketing techniques that rely on virtual reality and augmented reality to create rich interactive experiences for consumers. These techniques allow sensory and realistic interaction with products and brands. This type of marketing aims to transform the consumer's experience from simple traditional interaction to a multisensory immersive experience, enabling the customer to perceive product details, design, and test it virtually before making a purchase decision. The current research, within its conceptual framework, considers immersive marketing as an explanatory variable. also addressed the topic of product design and development as a responsive variable. The problem centered on how the researched companies can elevate their product design levels and develop them through the use of immersive marketing techniques. Accordingly, the importance lies in informing the researched companies about the clear role that immersive marketing techniques play in designing and developing their products, and in motivating customers to purchase them. To achieve this, a questionnaire was used as the primary tool for data collection, distributed randomly to 60 employees in the researched companies, with valid responses for analysis regarding the current state of the research variables. The data were analyzed using statistical tools to achieve the research objectives and test hypotheses, relying on the statistical software SPSS version 26. Based on the practical findings, the research reached several conclusions, upon which a set of recommendations was made to the management of the researched companies