American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
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Reference Intervals for Haematological and Morphometric Parameters in Balkan Donkey in Serbia
Full text linkThe Balkan donkey is an indigenous primitive breed that is bred in the hilly and mountainous regions of Serbia and Montenegro. It represents an important resource in terms of preserving the genotype of our country. Today, the majority of donkey population in Serbia is situated in Special Nature Reserve “Zasavica”, Nature park of “Stara Planina and Kovilj vilage, near Novi sad. They are mainly used for the production of donkey milk and as a tourist attraction on farms. There are no many literature data related to of the values of basic hematological and morphometric parameters in donkeys. This led to the development of a this study, especially on donkeys from Stara Planina, whose end result were the definition of basic physiological values of triassic parameters, hematological and biochemical values of blood parameters and precise morphometric measurements. Knowing the values of these parameters is of great importance for veterinarians for the correct interpretation of clinical findings and diagnosis of diseases in donkeys
AI-driven and Non-AI Methods for Electronic Health Records Duplication Remediation for Healthcare Organizations
Full text linkDuplicate Electronic Health Records (EHRs) represent a critical challenge for healthcare organizations, leading to incomplete patient data, potential medical errors, increased operational costs, and compromised quality of care. Traditional methods such as deterministic and probabilistic matching, combined with Enterprise Master Patient Index (EMPI) systems and robust data governance, have long been the cornerstone in tackling duplicates. However, these approaches face limitations when handling large-scale, heterogeneous patient data. In response, AI-driven techniques—particularly Machine Learning (ML)—have emerged as powerful alternatives, enhancing record linkage accuracy, automation, and adaptive capabilities. This article provides an in-depth review of current non-AI (deterministic and probabilistic) and AI-based deduplication strategies, including advanced ML algorithms, biometric patient identification, and real-time re-checking services. We analyze case studies from leading healthcare systems, demonstrating a reduction of duplicate rates from over 20% to under 2%. Additionally, the paper explores key management and organizational factors for successful adoption of deduplication solutions, emphasizing the need for adequate training, policy development, and continuous monitoring. Concluding with practical recommendations and future directions, this research serves as a comprehensive resource for healthcare IT and data management executives aiming to ensure high-quality patient records, strengthen compliance, and support value-based care initiatives
Technologies and Methods for Optimizing Web Application Performance
Full text linkThe article reviews modern technologies and methods applied to optimize web application performance, emphasizing the direct impact of site speed on user experience, business performance, and competitive positioning. The authors analyze contemporary approaches for enhancing both front-end and back-end performance. Front-end strategies discussed include code splitting, lazy loading, server-side rendering (SSR), image optimization, and minimizing render-blocking resources. Back-end methods encompass various caching strategies, database query optimization, effective API design—particularly comparing GraphQL and REST—and deployment of Content Delivery Networks (CDNs) alongside edge computing solutions. A structured review methodology was applied, synthesizing recent peer-reviewed literature, expert reports, and empirical case studies from industry settings published within the past five years. Quantitative data are provided, illustrating significant performance improvements, including latency reduction, increased throughput, and enhanced user interaction metrics. The authors highlight practical implementation considerations and trade-offs inherent to each technique. Presented findings contribute valuable insights for developers, system architects, and researchers aiming to deliver faster, more reliable, and user-friendly web applications
Design and Evaluation of a Convolutional Neural Network Model for Automated Detection of Diabetic Retinopathy Using Retinal Fundus Photographs
Full text linkDiabetic retinopathy (DR) is a leading cause of preventable blindness globally, necessitating timely and accurate screening methods. This study presents the design and evaluation of a custom convolutional neural network (CNN) model, LightDR, for automated classification of DR using retinal fundus photographs. The Augmented_resized_V2 dataset, derived from the Eyepacs, Aptos, and Messidor collections from Kaggle, was used to train over 143,000 labeled images. The LightDR architecture was built using TensorFlow and optimized through data augmentation, class balancing, and performance-driven callbacks. Evaluation of the model yielded an accuracy of 84%, with precision and recall metrics indicating strong sensitivity to disease presence and reliable classification of healthy cases. The model demonstrated generalization and interpretability, supported by Grad-CAM visualizations and confusion matrix analysis. These findings suggest that LightDR offers a scalable and effective solution for DR screening, with potential for integration into clinical workflows pending further validation
The Transformation of Panoptic Power: The Internalized Discipline of the Individual in the Surveillance Society
Full text linkContemporary societies have evolved into a surveillance regime where Foucault’s concept of the Panopticon is reinterpreted due to the widespread adoption of digital technologies. The Panopticon, originally conceived through the traditional prison model, is no longer limited to physical spaces but manifests itself prominently in the digital realm. States, major technology corporations, and social media platforms have developed modern surveillance systems that constantly monitor individuals and shape their behavior.This paper revisits Bentham’s classic Panopticon, interprets it through Foucault’s lens of disciplinary society, and then expands the analysis using Gilles Deleuze’s control society and Shoshana Zuboff’s surveillance capitalism frameworks. It addresses how individuals participate in self-surveillance through algorithmic mechanisms, social media behavior, and AI-based monitoring technologies. Case studies such as China’s Social Credit System, the NSA revelations, and the Cambridge Analytica scandal are used to demonstrate contemporary applications of digital panoptic power.The study concludes that surveillance mechanisms today are not only instruments of observation but also tools for behavioral shaping and normalization. The article critically evaluates the limitations of the Panopticon metaphor and offers a comprehensive view of how surveillance reshapes perceptions of freedom, autonomy, and privacy in digital society
Integration of WebAssembly in Performance-critical Web Applications
Full text linkThis article explores the integration of WebAssembly into high-performance web applications as a response to the increasing demands for computational power, scalability, and security in the rapidly evolving landscape of web technologies and the Internet of Things (IoT). The study substantiates the relevance of transitioning from traditional JavaScript to WebAssembly, which allows code written in C/C++ or Rust to be compiled into a compact binary format, delivering near-native execution speed. The article analyzes the architecture of WebAssembly, its advantages, and its integration potential with other technologies, such as WebGPU for accelerated parallel computations. Special attention is given to the current limitations of WebAssembly (e.g., the lack of native garbage collection, debugging difficulties, and challenges in cross-language integration) as well as its promising development directions, including the standardization of WASI and enhancements through multithreading and SIMD support. In comparative experiments on 1024 × 1024 matrix multiplication, the SIMD?enabled WebAssembly module with block?optimized memory access outperformed the optimized JavaScript implementation by 1.64 × and delivered a 4 × improvement over the unvectorized Wasm build, while offloading computations to WebGPU achieved an ~50?fold reduction in execution time for both JavaScript+WebGPU and Wasm+WebGPU configurations. These results substantiate that the integration of WebAssembly and WebGPU brings near?native and GPU?accelerated performance to browser?based applications, laying a quantitatively validated foundation for high?load web and IoT systems.The paper demonstrates a way to accelerate client data processing using a combination of Web Assembly and Web GPU. The results of a comparative experiment are presented. This article will be of interest to professionals in web development and systems architecture who aim to optimize computational workflows and maximize the performance of modern web applications via WebAssembly. Additionally, the material provides valuable insights for researchers engaged in the analysis and development of advanced methodological approaches to optimizing high-load information systems
Leveraging Artificial Intelligence and Data Analytics for Decision-Making in IT Project Management
Full text linkThe article focuses on enhancing the justification of managerial decisions in large IT projects, characterized by persistent instability and escalating budgetary and schedule risks, through the systematic integration of artificial intelligence and advanced data analytics. The objectives of this study are twofold: first, to comprehensively describe the mechanisms for deploying predictive models and generative AI tools at every phase of the IT project life cycle; and second, to empirically validate the claimed effects using the PwC CEE IT practice. The novelty of the work lies in the combination of predictive machine learning, Monte Carlo simulations, AI scoring and the Copilot generative planner in a single decision-making loop, as well as in the fact that the author described in detail his experience of implementing and adapting these technologies in the real PMO process of PwC CEE IT. This article will be helpful to project leaders, PMO analysts, and developers of decision-support systems in IT project management
Yield Analysis of Boost vs Non-Boost Base Trader Joe Liquidity Pools
Full text linkThis comprehensive study presents an extensive quantitative analysis of the impact of Trader Joe’s Boost Incentive Program on Trader Joe’s liquidity pools. The Boost Incentive Program is a liquidity initiative designed to revitalize a specific DeFi ecosystem by enhancing user engagement and competitiveness. Following the success of a previous program from mid-2021 to early 2022, this new initiative aims to reignite growth and innovation by increasing Total Value Locked (TVL), attracting new protocols, and regaining market share within the DeFi space. The ongoing program focuses on supporting both new and existing DeFi protocols through liquidity mining incentives, direct liquidity deployment, and backing for new assets and products. The strategic use of incentives is designed to maximize impact by concentrating on core primitives and top native protocols, thereby driving substantial growth in TVL. By allocating incentives to specific strategies and liquidity pools, Trader Joe aims to offer higher yields to liquidity providers, thereby attracting more participants and increasing TVL on its platform. This approach aligns with the overarching goal of the Boost program to support innovation and new protocol growth. In the below analysis, I examine how these incentives affect yields will provide insights into the effectiveness of such programs in attracting liquidity and enhancing protocol performance. By integrating detailed data from incentive_analysis.xlsx and traderjoe_base_metrics.csv, we examine how incentive allocations, fee structures, and liquidity provider participation influence liquidity provision, trading volume, fees, and yields. The analysis incorporates statistical insights and trends within the dataset, covering rewards allocation, fee structures, liquidity provider participation, and average USD values across various token pairs. The aim is to offer deep insights into the effectiveness of incentive programs in enhancing protocol performance and user engagement within the decentralized finance (DeFi) ecosystem
Investigating the Relationship among High School Students’ Mathematics Anxiety, Attitude towards Mathematics and Mathematics Achievement
Full text linkThis research design is quantitative, cross-sectional survey design that investigated the relationships among 700 fourth-form students’ (aged 14 - 16 years) mathematics anxiety, attitude towards mathematics and their mathematics achievement. This study was conducted at a co-educational secondary school located in Providenciales, Turks and Caicos Islands. The data related to students’ mathematics anxiety, attitude towards mathematics and mathematics achievement were collected with the use of modified shortened math anxiety scale (mAMAS), the Short Form attitude towards mathematics scale (ATMI-Short Form) and a teacher-made End-of-Term mathematics examination, respectively. This data was analyzed using Pearson correlation and Spearman rank order correlation. The results showed that there was a negative correlation and a non-significant between 4th-form students’ mathematics anxiety and their academic achievement, a medium significant positive correlation between 4th-form students’ attitude towards mathematics and their mathematics academic achievement, and a great significant negative correlation between 4th-form students’ attitude toward mathematics and their mathematics anxiety. However, the correlation was greater between students’ mathematics anxiety and their attitude towards mathematics
Coastal Dynamic Characteristics and Hazards along the Coastal Region of Myanmar
Full text linkMyanmar’s coastal region, characterized by its dynamic interactions and vulnerabilities, is an area of immense environmental and socio-economic significance. This review examines the mechanisms shaping the coastal dynamics and the hazards that threaten the coastal region of Myanmar. The analysis begins by addressing the influence of Myanmar’s coastal currents, which play a pivotal role in the region\u27s hydrography and ecological balance. The wind-wave climate, a key driver of coastal processes, is scrutinized for its impact on erosion and sediment transport. A major focus of this study is the relationship between storm surges and tropical cyclones; frequent phenomena that contribute to widespread destruction along the Myanmar coast. Additionally, the review highlights the tsunami threats that have impacted the region, emphasizing their unpredictable nature and catastrophic potential. Coastal erosion, driven by natural forces and human activities, emerges as a persistent challenge, gradually reshaping Myanmar’s shoreline. The discussion extends to sea level rise, an ever-growing concern linked to climate change, and its far-reaching consequences on coastal stability. Moreover, anthropogenic impacts such as land reclamation, deforestation, and urbanization are underscored, revealing their significant role in altering the coastal landscape and exacerbating vulnerability. By synthesizing these elements, the review provides a holistic understanding of Myanmar\u27s coastal dynamics and hazards, offering critical insights into sustainable management and adaptive strategies to protect this fragile environment