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Discoever Applied Sciences
No full textThe accelerating global shift toward renewable energy sources is largely attributed
to increased investments and the rising demand for electricity, driven by
technological progress, population growth, and escalating fuel prices associated
with traditional power generation. Despite their benefits, conventional energy
systems face challenges such as sensitivity to fluctuations in solar irradiance and
temperature, which lead to non-linear electrical behavior and reduced efficiency.
In Iraq, for example, the World Bank reports a significant power distribution loss of
approximately 51%. To mitigate these inefficiencies, this study introduces a gridconnected photovoltaic (PV) system employing Maximum Power Point Tracking
(MPPT) techniques—specifically, the Perturb and Observe (P&O) and Incremental
Conductance (I&C) algorithms. These approaches aim to enhance energy extraction
from PV arrays under dynamic environmental conditions. System modeling and
performance evaluation were conducted using MATLAB/Simulink, focusing on
optimizing output and regulating the DC–DC boost converter’s switching frequency.
Under varying irradiance (1000–250 W/m2
) and temperature (25–50 °C) conditions,
I&C algorithm achieved a higher MPPT tracking efficiency of approximately 98.7%,
compared to 95.2% for the P&O method. Additionally, I&C demonstrated faster
convergence with a response time of 0.15 s and exhibited reduced power ripple
(~1.2 kW) versus P&O (~3.8 kW), confirming its superior dynamic stability and
steady-state performance
Keywords I&C, MPPT, P&O, Photovoltaic system (PV), Renewable energyEffat Universit
Brain tumor diagnosis techniques key achievements, lessons learned, and a new CNN architecture
No full textBackground A brain tumor is an abnormal tissue growth in the skull that can damage healthy brain areas by exert‑
ing pressure. While early detection is vital for prevention, accurate diagnosis with computer-aided design (CAD)
systems remains challenging due to variations in tumor shape and location.
Aim This paper provided a structured literature survey (SLS) of various machine learning (ML) and deep learning (DL)
techniques that were utilized in detection, classification, segmentation, and fusion-based diagnosis involving multiple
diagnostic systems and a newly designed convolution neural network (CNN) architecture.
Method The SLS was based on reliable papers in the Web of Science (WoS) database and was organized into three
phases. The first evaluated recent review papers, identified the number of methodological studies in each, focused
on authenticated publications, and analyzed their diagnostic approaches, ending with a critical assessment
of the reviews. The second examined recent methodological works in brain tumor diagnosis that were not covered
in those reviews, assessing each by its performance metrics. Across these phases, 320 authenticated studies were
analyzed. The final phase introduced the detecting and classifying brain tumors (DCBT) system.
Results This system combined transferred EfficientNet-B0 (TR_EffNetB0) with a newly developed dual-path CNN
architecture, attaining an accuracy of 98.5%.
Conclusion The SLS concluded with intuitive key achievements and lessons learned, which made future research
easie
The Role of Family Support in Managing Anxiety and Depression
No full textThis research explores the impact of perceived family support on the management of anxiety and
depression among youth and young adults in Saudi Arabia. Based on a culturally particular
environment, the study investigates how emotional support, familial awareness of distress , and
mental health communication affect psychological well-being. A standardized questionnaire was
filled out by 215 individuals ranging in age from 15 to 31. It assessed family dynamics, mental
health symptoms, and support mechanisms. Descriptive data revealed that, while many
participants reported receiving some type of emotional or practical assistance, talks concerning
mental health were frequently limited or nonexistent, and a significant portion believed their
issues were not taken seriously by family members. Pearson correlation analysis using SPSS
revealed a strong negative connection between perceived family support and anxiety (r = -0.56, p
< 0.01) and depression (r = -0.61, p < 0.01). This indicates that higher levels of family support
are associated with reduced psychological distress. These findings emphasize both the protective
role of family involvement and the cultural barriers to effective communication and support. The
study underlines the need of culturally appropriate mental health methods that increase family
engagement, eliminate stigma, and build emotionally responsive environments in Saudi
households
Fixed Frequency Sliding Mode Control for Parallel Linked Distributed Generators in the Isolated AC Microgrid
No full textParallel linked inverter-interfaced distributed generators (DGs) have been utilized to increase the power capacity and system reliability and maintain the power supply at the critical load end. However, the control methods are crucial for ensuring a stable output voltage from the DGs. In this paper, the authors have proposed parallel linked inverter-interface DGs based on a voltage control strategy in an isolated AC microgrid (MG). Each three-phase parallel DG consists of an inductor-capacitor (LC) filter at the output and is connected to the AC distribution bus (DB) to meet the critical load demand. Fixed frequency sliding mode control (FFSMC) regulates the voltage at the output under steady-state, linear, nonlinear, and unbalanced load conditions. The constant switching frequency-based control design is employed, and the chattering phenomenon is diminished by smoothing the control law in a boundary layer. The proposed FFSMC provides stability against severe variations of load as well. The droop control method and virtual output impedance (VOI) loop are examined to ensure active and reactive power sharing between the parallel-linked DGs. The implementation of the FFSMC has been evaluated in a MATLAB/Simulink environment through simulations. The simulation outcomes reveal the efficiency of FFSMC in terms of quick response time, superior robustness, stable performance, and a low total harmonic distortion (THD)
Systematic Literature Review on Resilient Supply Chains in Saudi Arabia: The Impact of Big Data-Driven Decision-Making on Risk Management
No full textThis research examines how Big Data-Driven Decision-Making can strengthen supply chain
resilience in Saudi Arabia. In recent years, global events such as the COVID-19 pandemic, economic
instability, and regional uncertainties have exposed significant weaknesses in supply chains. These
challenges have increased the demand for more effective, data-based strategies to identify and
manage risks.
The study uses a Systematic Literature Review (SLR) approach to analyze 13 academic articles
published between 2015 and 2023. These articles focus on how Big Data Analytics (BDA) is being
used in key Saudi sectors—such as manufacturing, logistics, retail, and oil and gas—to support risk
identification, improve decision-making, and build more flexible supply chains.
The review highlights both the advantages and the limitations of using BDA in practice. It also
emphasizes the importance of integrating digital tools into national and corporate strategies to
improve supply chain performance. Finally, the research supports Saudi Arabia’s Vision 2030 by
showing how BDA can contribute to economic growth, innovation, and improved infrastructur
Exploiting Artificial Intelligence Approaches to Augment Decision-making Processes for an Intelligent Performance in Football
No full textThe world of Football has witnessed remarkable advancements, elevating the sport from being merely "The Beautiful Game" to a realm where numbers and data hold immense significance. While some clubs and teams have achieved success through research, there is a pressing need for further investment in research, particularly with the integration of Artificial Intelligence (AI) technologies. AI has the potential to revolutionize team performance analysis by providing real-time insights and facilitating the analysis of instant games. An illustrative instance of the application of reinforcement learning for the purpose of analyzing player styles and conducting comparative assessments among various players can significantly enhance the simulation of these players within video games, such as FIFA games. Furthermore, it can augment the process of recruiting new players in accordance with the distinct training methodologies employed by individual clubs within diverse leagues.In this paper, we propose a novel approach to developing an intelligent system that can analyze and predict Football players' performances in real-time. Our approach utilizes machine learning and deep learning techniques, as well as a recommendation system, to comprehensively analyze players' performance. This proposed system aims to support managers and players in making informed decisions by providing them with valuable insights based on diverse data sources. By leveraging this system, teams can enhance their performance and potentially attain higher international rankings
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No full textWe aimed to see the trend and geographic distribution of suicide research. We searched in Scopus to identify the articles published on suicide since 1991 and compared the country-wise output in regards to income and geographical location of the country. The study revealed more than twofold increase of articles from an earlier decade and a slight increased output from low and middle income countries (LMICs) over the decades. More than three-fourths of the articles published from top 15 countries where only two countries (India and China) from LMIC backgrounds were featured. About 85% of the papers were published from high-income countries, about 73% of the papers were published from Europe and North America, and about 78% of articles were published from two WHO regions (European Region and Regions of Americas). This study identifies an extreme disparity in research on suicide based on income and geographical location.N
A Study of 10 Minutes 38 Seconds by Elif Shafak
No full textThis study aims to explore the novel 10 Minutes 38 Seconds in this Strange World by Elif Shafak
through feminist literary theories. The theories of Simone de Beauvoir’s The Second Sex (1949),
Virginia Woolf’s Killing the Angel in the House (1931), and Sandra Gilbert and Susan Gubar’s
The Madwoman in the Attic (1979) are utilized to discuss the patriarchal society presented in the
novel and its effects on the protagonist’s life. The analysis includes discussions of the
exploitation and lack of protection for women in the novel. The female characters are presented
as victims of a society that adheres to the satisfaction and comfort of men. Leila’s suffering
began with a controlling father and an abusive uncle in her childhood. The patriarchal ideologies
in her society and her father’s beliefs play a part in her future profession as a prostitute.. Shafak
presents a society in the novel where the patriarchy fails to protect women from brutalities and
one that maintains a system that benefits men
Digital Avatars as a Guiding Agent for Enhancing User Experience
No full textDigital avatars are transforming how individuals receive guidance and support across fields like customer service, online education, and virtual shopping. They enable real-time interaction, simplifying complex tasks and enhancing user experiences. As digital environments grow more immersive, designing avatars that effectively support multitasking has become essential. Establishing standards for visually appealing, effective, and user-friendly avatar design is crucial to enhance engagement and accessibility.
The research problem addresses the lack of clear design standards for AI-driven avatars that ensure visual appeal, functionality, and user-friendliness. The importance of the study stems from the increasing use of AI-driven avatars in user interfaces. The objective is to explore key principles such as simplicity, intuitive design, personalization, and consistency to foster user trust and comfort. The research hypothesizes that avatars incorporating these principles will demonstrate improved usability, engagement, and emotional resonance with users. Using a descriptive methodology, including a case study on "Jamie," ANZ Bank’s AI-powered digital assistant, the study examines how avatars assist users with tasks such as credit card selection and loan applications. The study is limited to the context of banking and financial services. User surveys provide feedback on usability, engagement, and overall effectiveness, offering insights for refining avatar design, enhancing response speed, and improving emotional expression through optimized blend shapes