Polaris Global Journal of Scholarly Research and Trends

Examining Job Satisfaction and Turnover Intent among Lawyers in the Maldivian Public Sector

Shafeega Naeem — 2025-01-12

Purpose: The primary objective of this paper is to ascertain how job satisfaction influences the reduction of turnover intention among lawyers in the public sector in the Maldives.

Design/ Method/ Methodology: Employing a quantitative research design, the study adopts a cross-sectional data collection approach, gathering data from 218 public-sector lawyers in the Maldives.

Findings: The analysis shows a significant negative relationship between job satisfaction and turnover intention.

Implications: The findings provide valuable insights for organizations aiming to improve their human resource management practices, particularly within the legal profession. Implementing strategies to enhance job satisfaction among lawyers could serve as a key mechanism for reducing turnover and fostering a more stable and productive workforce in public sector organizations.

Physics Olympics: An Enhancement approach for Students’ physics performance, critical thinking, and science process skills

Leah P. Pacaco — 2025-01-13

This quasi-experimental study aimed to determine the effect of the physics Olympics on Grade 7 students’ performance, critical thinking, and science process skills. The participants were the Grade 7 regular class students of a rural public high school in Panay Island. The participants were divided into two groups; one group was exposed to an inquiry-based approach while the other was to an inquiry-based approach enhanced with Physics Olympics. It utilized validated researcher-made and adopted-modified instruments. The data underwent statistical and thematic analysis. The study found that both the inquiry-based approach and with Physics Olympics were effective in improving students' physics performance, critical thinking, and science process skills. Though both groups were categorized as approaching proficiency level in terms of physics performance and critical thinking skills, the mean gain among the students who were exposed to intervention was higher compared to that of the students who were exposed to the traditional approach. In terms of the science process skills, students who were exposed to the inquiry-based approach were at the approaching proficiency level while those who were exposed to the intervention were at the proficiency level. The two groups had no significant difference in their mean gain scores in physics performance and critical thinking skills after the intervention. However, their mean gain score in science process skills was statistically significant which emphasizes the edge of inquiry-based teaching enhanced with physics olympics over inquiry-based approach. It can be recommended that science teachers teaching basic education should integrate physics Olympics as a mode of enhancing inquiry-based teaching approaches.

Clarifying the Scope of NLP: Language Processing vs. Neuro-Linguistic Programming

awaz shaban — 2025-05-19

The acronym “NLP” has come to represent two fundamentally distinct disciplines: Natural Language Processing (a computational subfield of Artificial Intelligence) and Neuro-Linguistic Programming (a psychological and therapeutic methodology). This paper presents a comprehensive comparative analysis aimed at resolving ambiguities surrounding the use of the term “NLP” in academic, clinical, and technological contexts. By examining definitions, methodologies, applications, and challenges, we delineate the separate paradigms these fields represent. We explore cutting-edge computational models such as BERT, GPT-3, and CASEml within NLP, juxtaposed with therapeutic tools like the Meta Model and anchoring techniques within NLP therapy. Case studies spanning medical informatics and clinical psychology underscore the efficacy of each domain, while highlighting the potential for confusion due to shared linguistic foundations. This study not only clarifies domain boundaries but also proposes research directions to mitigate terminological ambiguity and foster interdisciplinary collaboration.

Preservation of Heritage in Iraq using AR

Ahmed A. H. Alkurdi — 2025-03-11

Augmented reality (AR) has come a long way in the past few years. From requiring special devices and hardware into the palm of our hands. The advancement of smartphone and tablet hardware enables beautiful, precise and engaging AR experiences. This article explores the application of AR experiences in the context of cultural heritage digitization. AR has a lot to offer the digitization of cultural assets, especially when it comes to situations when it is crucial to preserve a country's culture and historic objects. This is particularly evident in nations with rich historical legacies and old civilizations, such as Iraq. However, violent situations, war, and catastrophic occurrences have continuously threatened to erase this rich past. Future generations may be protected by using AR as a potent tool for not only conserving but also presenting and teaching people about these priceless cultural artifacts. The proposed AR application is developed using SwiftUI’s Model View View Model (MVVM) programming model. The experience’s AR capabilities utilize ARKit 6 framework and RealityKit framework for AR. These frameworks provide unparallel AR abilities for tracking, positioning and rendering 3D objects. Furthermore, ARKit provides the ability of building collaborative AR sessions across multiple devices for a shared AR experience.

Development, validation, and utilization of localized sensory board for learners with special educational needs

Sammy Boy B. Guzman — 2025-01-12

This study aimed to develop, validate, and utilize a localized sensory board for Learners with Special Educational Needs (LSENs). A mixed-methods research design was used to systematically develop, validate, and utilize the localized sensory board. The respondents and participants of this study were twenty-two LSENs and twenty-two parents of LSENs. A random sampling procedure was used to select twenty-two LSENs and twenty-two parents. The data were collected through document analysis, validation rubric, and interviews. Based on the findings of the study, the following results were drawn: 1) The academic performance of LSENs before the utilization of the sensory board were at the following markings: 22.73% at Approaching Proficiency, 50.00% at Developing, and 27.27% at Beginning, 2) The experts in special education validated the material as highly acceptable to the following dimensions: Quality, Usability, and Technical Design, 3) The academic performance of LSENs after the utilization of the sensory board were at the following markings: 63.64% at Approaching Proficiency, and 36.36% at Developing, 4) Based on the responses of the parents, the impacts of using the localized sensory board are as follows: motivates students to learn, develops mathematics and numeracy skills, serves as transition activity, functions as supplementary activity, encourages calming activity, and functions as manipulative material, and 5) The results of the sample t-test revealed that there is significant difference between the academic performance of the students before and after the utilization of the localized sensory board at a very high level.

The Evolution of Fog and Cloud Computing in Distributed Systems: A Review of Architectures, Challenges, and Parallel Processing Techniques

hawar ahmad — 2025-06-18

Fog and cloud computing has revolutionized distributed systems through solving significant challenges like resource management, latency, and scale. The main characteristic of fog computing is to bring the computational resources close to the data source to allow near real-time processing for delay sensitive applications, improving the response time for those applications, while cloud computing centralizes the data for long life storage and massive processing. In this manner, these paradigms interoperate to yield hybrid architectures that address the increasing demands of networked systems such as the Internet of Things. In this review we listed the development, models, issues and parallel processing in fog and cloud computing. Energy-efficient task scheduling, privacy-preserving models, and fault-tolerant designs are some advancements that improve system reliability and performance. Additionally, containerized microservices and federated learning approaches also enable seamless integration and secure data management in various applications. However, there are still issues with strong interoperability, preserving the performance of a system under extreme load, and reducing security threats even with great advances. We analyze the gaps, propose solutions, and emphasize the key role of adaptive frameworks and innovative resource allocation methods in tackling those gaps. These results show how fog and cloud computing can change the landscape of distributed systems in the future.

Comparative Evaluation of Linear Regression, Tree-Based Regression, and Neural Network Models for Structured Car Price Prediction

awaz shaban — 2025-05-19

Accurate car price prediction plays a critical role in modern digital marketplaces, dealership platforms, and automated financial systems. Traditional valuation methods are often subjective, whereas machine learning (ML) enables data-driven modeling of complex, non-linear relationships among vehicle attributes. This study evaluates the performance of three ML models—Linear Regression, Decision Tree Regressor, and Multilayer Perceptron (MLP)—on a publicly available dataset of 205 used car listings. After comprehensive preprocessing and an 80:20 train-test split using Scikit-learn with fixed randomization, model performance was assessed via RMSE, MAE, and R² metrics. The Decision Tree Regressor achieved the best results (R² = 0.886), outperforming both the linear and neural models. Additionally, a literature-based benchmark against ten recent studies shows that interpretable models like Decision Trees can rival more complex techniques such as XGBoost, CNNs, and stacked ensembles when applied to well-prepared tabular data. These findings highlight the practical value of simplicity and interpretability in real-world pricing systems.

Data Analytics of the Scopus 2025 Dataset: Trends in Open Access, Publisher Influence, and Subject Area Distribution

Awaz Ahmed Shaban — 2025-04-19

This study performs an extensive analysis of the Scopus Sources Dataset
(February 2025 edition) using big data analytics and visualization
techniques. Through systematic preprocessing, exploratory data
analysis, and graphical interpretation, we investigate the structure of
global scientific publishing. Key insights include the distribution of
journals by source type, publisher dominance in both Open Access (OA)
and Non-OA domains, ISSN/EISSN patterns, and ASJC field classification
frequencies. Results show strong clustering in specific publishers and
fields, a moderate OA presence, and a considerable share of journals
with dual ISSN registration. Our findings provide a comprehensive
snapshot of the scholarly publishing landscape as captured in Scopus
2025.

Building Scalable Enterprise Systems: The Intersection of Web Technology, Cloud Computing, and AI Marketing

awaz shaban — 2025-04-11

The rapid evolution of digital technologies has transformed enterprise scalability, necessitating the integration of cloud computing, artificial intelligence (AI), and web technologies. Traditional enterprise architectures often struggle with dynamic workloads, operational efficiency, and adaptability to market demands. This study explores the intersection of web technology, cloud computing, and AI-driven automation in building scalable enterprise systems. Through an extensive literature review, comparative analysis, and extracted industry statistics, this research identifies key strategies such as hybrid and multi-cloud adoption, microservices-based architectures, AI-driven decision-making, and Zero Trust security frameworks. The findings highlight the importance of API-first architectures, predictive analytics, and automated cloud resource management in achieving business agility and cost optimization. Additionally, the study discusses the challenges of data security, integration complexity, regulatory compliance, and cost management in implementing scalable enterprise solutions. The research concludes that organizations that effectively implement AI, cloud technologies, and web-based solutions gain a competitive advantage through increased agility, operational efficiency, and digital resilience. Future research should focus on quantum computing, blockchain integration, ethical AI governance, and industry-specific scalability challenges to further enhance enterprise digital transformation efforts.