American Journal of Applied Science and Technology

Sustainable Self-Compacting Cementitious Composites and Intelligent Intrusion Detection for IOT Systems: A Dual-Lens Analysis of Resilience in Built and Digital Infrastructure

2026-04-03T02:11:18+00:00

Background: Contemporary infrastructure resilience is increasingly defined by two parallel demands: the need for durable, sustainable, and high-performance construction materials, and the need for intelligent, adaptive, and trustworthy cybersecurity mechanisms for highly connected Internet of Things environments. The references supplied for this study cover self-compacting concrete, reactive powder concrete, foam concrete, fiber modification, supplementary cementitious materials, sulfate and thermal resistance, and sustainable mix design. They also cover intrusion detection for IoT, wireless sensor networks, in-vehicle networks, graph neural networks, transformers, explainable artificial intelligence, network forensics, transfer learning, and benchmark datasets. Although these literatures are usually studied independently, both address the same deeper problem: how complex systems can remain reliable under uncertain, evolving, and often hostile operating conditions.

Objective: This article develops an original, publication-ready research synthesis based strictly on the provided references. Its purpose is to construct an integrated analytical framework that explains how resilience is designed, assessed, and improved in both material infrastructure and cyber-physical infrastructure.

Methodology: A text-based integrative research design was employed. The civil engineering literature was interpreted through the lenses of mix optimization, durability, thermal response, sustainable materials, and fiber-enhanced performance. The cybersecurity literature was interpreted through the lenses of data representation, lightweight detection, graph-based learning, explainability, transfer robustness, and IoT-specific attack detection. These domains were then compared at the conceptual level of system resilience, adaptive performance, and design under constraints.

Results: The analysis indicates that sustainable self-compacting and advanced cementitious composites achieve resilience through material tailoring, reduced defect sensitivity, and environmentally conscious substitution strategies. In parallel, IoT intrusion detection systems achieve resilience through feature engineering, lightweight models, graph-based learning, self-supervision, explainability, and robustness-preserving transfer methods. Across both domains, resilience emerges not from single-variable optimization but from structured balance among performance, adaptability, efficiency, and reliability.

Conclusion: The article argues that civil material innovation and cybersecurity intelligence should be understood as parallel sciences of resilience. Though their objects differ, both require multi-factor design, tolerance to uncertainty, and disciplined evaluation under real constraints. This integrated perspective offers a broader theoretical basis for future research on sustainable and secure infrastructure.

Theoretical Foundations of The Aerodynamics of An Improved Bag Filter Device Utilising Composite Filter Fabric

2026-04-09T05:00:33+00:00

This article examines the theoretical justification of the aerodynamic resistance of an advanced bag filter apparatus. The total pressure loss within the device is interpreted as the summation of losses occurring during the intake of the dust-laden gas stream, its longitudinal axial movement within the filter sleeve, the radial filtration process through the composite filter medium, and local resistances encountered within the housing and the purified gas discharge zones. The microstructural characteristics of the filter fabric, based on a combination of polyester, glass fibre, and basalt fibre—including parameters such as thickness, specific contact surface area, the fraction of open zones, and the degree of dust cake formation—are considered the primary determining factors of the radial resistance coefficient. Consequently, an analytical approach is proposed that enables the evaluation of the total aerodynamic resistance while accounting for the actual microstructural state of the composite filter fabric.

Harnessing Integrated Reporting Platforms and Dynamic UI Components for Timely Managerial Decisions

2026-04-08T07:09:26+00:00

The increasing reliance on data-driven decision-making in modern organizations has intensified the need for integrated reporting platforms and dynamic user interface (UI) components capable of delivering timely and actionable insights. This study examines the role of integrated reporting systems combined with adaptive UI technologies in enabling efficient managerial decision-making. The research is grounded in the intersection of business intelligence, machine learning, and human-computer interaction, emphasizing the transformation of raw data into strategic knowledge.

Integrated reporting platforms consolidate data from heterogeneous sources, providing a unified analytical environment for decision-makers. Dynamic UI components enhance this functionality by enabling interactive data exploration, real-time updates, and user-centric customization. This paper critically evaluates these systems through theoretical perspectives and empirical insights derived from studies on financial prediction, healthcare analytics, and enterprise dashboards. The work of Gondi et al. (2026) is particularly central, illustrating how dashboard-driven reporting systems facilitate real-time managerial insights and operational efficiency.

The study further explores the application of machine learning models in predictive analytics, drawing upon research by Dhokane and Sharma (2022), Hiransha (2018), and Zhang et al. (2019). These approaches demonstrate the capability of integrated systems to support forecasting and decision optimization. Additionally, insights from public health and mobile application studies (Institute for Public Health, 2019; Chandrashekar, 2018) highlight the importance of data accessibility and user engagement in effective decision-making.

The findings indicate that organizations leveraging integrated reporting platforms with dynamic UI components achieve improved decision speed, enhanced data comprehension, and greater strategic alignment. However, challenges related to system complexity, data quality, and user adaptability remain significant. The paper concludes by proposing a conceptual framework for optimizing these systems and identifying future research directions in intelligent decision-support technologies.

Simulation-Based Assessment of Power Losses and Stability in Distribution Networks with Highly Integrated Solar Photovoltaic Systems

2026-04-10T02:44:55+00:00

In this article, the power losses and the impact on network stability of highly integrated solar photovoltaic (PV) systems in distribution networks were simulated using ETAP and MATLAB/Simulink software. The IEEE 33-bus test system was used as the research object, and the PV integration level was varied from 0% to 100%. The results showed that optimally placed PV systems can reduce power losses by 50–70%, improve the voltage profile, and enhance network stability. Under the conditions of Uzbekistan (high solar potential), this approach is of great importance in increasing energy efficiency and expanding the share of renewable energy.

Leveraging Intelligent Monitoring Frameworks and Dynamic Interface Tools for Fast Strategic Response

2026-04-08T13:07:04+00:00

The rapid evolution of data-intensive environments has necessitated the development of intelligent monitoring frameworks and dynamic interface tools capable of supporting immediate strategic decision-making. Organizations increasingly rely on real-time data acquisition, adaptive visualization, and responsive analytical systems to address complex operational challenges across sectors such as environmental monitoring, smart agriculture, and urban infrastructure. This study investigates the integration of intelligent monitoring architectures with user-centric dynamic interfaces to facilitate rapid and informed strategic responses.

The research synthesizes theoretical foundations from Internet of Things (IoT) ecosystems, machine learning-driven analytics, and interactive dashboard technologies. It critically evaluates existing monitoring systems, including water quality assessment platforms, agricultural greenhouse monitoring frameworks, and intelligent environmental management systems, to identify limitations in responsiveness, scalability, and user adaptability (Cai et al., 2023; Han et al., 2024). Additionally, the role of real-time dashboard systems in enhancing decision-making efficiency is examined, with particular emphasis on data integration and visualization techniques (Gondi et al., 2026).

A conceptual framework is proposed that integrates intelligent monitoring layers, adaptive data processing mechanisms, and dynamic interface modules. The framework emphasizes real-time data flow, predictive analytics, and interactive visualization as key enablers of fast strategic responses. Through analytical modeling and scenario-based evaluation, the study demonstrates how the proposed architecture improves decision latency, enhances situational awareness, and supports proactive interventions.

The findings indicate that organizations adopting integrated monitoring and interface solutions achieve significant improvements in operational efficiency, risk mitigation, and decision accuracy. However, challenges related to data heterogeneity, system interoperability, and user cognitive load persist. The study concludes by outlining future research directions focusing on AI-driven automation, human-centered design optimization, and scalable architecture development.