Papers by Iqtiar M Siddique
International Journal of Geoinformatics Science and Technology, 2025
Aerospace and defense systems increasingly face financial losses, schedule delays, and performanc... more Aerospace and defense systems increasingly face financial losses, schedule delays, and performance risks due to inadequate metadata management. The impact on domain-specific complex adaptive systems is reflected in substantial losses or delays, as in the case of Boeing's 737 MAX, Artemis I CubeSat Deployment, or ESA Vega Rocket, where these systems failed due to inadequate documentation, failure to adapt Metadata to mission-specific conditions, and providing crossverification of critical data and system integration. Conventional methods of managing metadata, such as hierarchical databases, Relational Database Management Systems (RDBMS), and centralized data warehouses, frequently fall short of effectively supporting data description, interoperability, and flexibility. This paper provides a thorough review of current approaches to metadata management within CAS, with a particular focus on how these approaches adapt to the complexities of systems that exhibit emergent behaviors. By assessing the limitations of existing methods, this research underscores the pressing need for decentralized, adaptive strategies that foster metadata alignment and traceability. This article proposes a fundamental approach for enhancing metadata management in dynamic project environments, encouraging collaboration, and optimizing project outcomes. The anticipated findings are expected to provide valuable understanding for scholars and professionals navigating metadata complexities in diverse projects, establishing a foundation for continued investigation in this area.
International Journal of Materials and Mechanical Structures Engineering, 2025
Between 2000 and 2024, a large number of small satellite missions fell short of their goals, with... more Between 2000 and 2024, a large number of small satellite missions fell short of their goals, with NASA pointing to structural and integration issues as common causes. Building satellites faster and at lower cost has become a major goal, especially as space systems grow more complex. Modular designs have emerged as one way to meet this demand, but the lack of reliable, reusable structural connections has limited progress. To better understand the trade-offs, this work looked at four connection methods for CubeSats: solenoid latches, rack systems, Wedge-Lok clamps, and screw fittings using L-shaped rails. Each one was reviewed using weighted criteria such as readiness for flight, expected weak points, physical size, and assembly ease. The screw and Wedge-Lok types came out ahead, mostly because of their simplicity and proven use in past missions. They also show promise for teams looking to simulate system performance before launch. Designs that allow lastminute changes, support system integration, and enable compatibility with digital models are especially useful as engineering shifts toward virtual prototyping and model-based development. The process used here can help teams decide on mechanical interfaces based on actual performance needs, not just legacy or convenience. What sets this work apart is the clear method it provides for selecting structural options that match modern space project demands. The main contribution is a flexible, criteria-driven approach for choosing CubeSat interface hardware suited to modular builds and digital system modeling.
ProQuest, 2025
Ineffective systems engineering practices continue to jeopardize aerospace missions, resulting in... more Ineffective systems engineering practices continue to jeopardize aerospace missions, resulting in multi-billion-dollar losses, delays, and fragmented development outcomes. As complexity intensifies and timelines shrink, conventional document-based methods increasingly fail to support early validation, cross-domain traceability, and synchronized tool usage. This research presents an integrated approach that addresses three critical gaps: synchronizing executable and traceable MBSE models across structural, behavioral, and requirements domains; applying scalable requirements prioritization techniques tailored for Complex Adaptive Systems; and establishing automated simulation feedback loops through digital toolchain integration. Central to this strategy is the Digital Trinity, which connects system models, simulations, and lifecycle data through a continuous digital thread. The proposed methodology begins with SysML-based modeling in Cameo Systems Modeler, followed by a multi-phase prioritization process using filtration, metadata scoring, and comparative weighting to evaluate over one hundred mission requirements. The approach is applied to the SidSat mission, a 4U CubeSat featuring a modular architecture with core avionics, a robotic arm payload, and an onboard AI/ML experimentation module. Requirements were allocated to model components, then verified using parameterized simulations. MATLAB was employed for mass, power, and thermal margin calculations; STK for orbital behavior assessment; Excel for value propagation and I/O management; and Teamcenter with AWS for model control and data traceability. Simulation outputs were dynamically linked to SysML constraint blocks and displayed through interactive dashboards in Cameo, ensuring transparency of compliance and coherence of the architecture state. Results demonstrate enhanced early-stage validation, improved stakeholder alignment, and reduced risk of misalignment between model logic and simulated performance. The final system model operates as a live digital reference across design and analysis phases, enabling iterative updates and real-time feedback. Although demonstrated on a CubeSat platform, the framework is extensible to larger systems where agility, traceable decision-making, and model-simulation convergence are essential for mission assurance.
International Journal of Artificial Intelligence in Mechanical Engineering, 2025
Millions of dollars and valuable resources are being wasted annually due to ineffective requireme... more Millions of dollars and valuable resources are being wasted annually due to ineffective requirement prioritization methods in managing the complexities of Complex Adaptive Systems (CAS). Traditional prioritization approaches struggle significantly to adapt to the dynamic and evolving nature of Critical Asset Systems (CAS), especially within critical industries such as aerospace, defense, healthcare, and environmental management. These rigid methods often lead to project delays, budget overruns, and suboptimal performance, ultimately failing to meet stakeholder expectations. This research introduces a transformative approach by integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques into the prioritization process, aiming to dramatically enhance adaptability and responsiveness. Utilizing a mixed-methods strategy, this paper provides empirical validation through detailed mathematical modeling, quantitative assessments, and in-depth qualitative case studies. Notably, AI and ML-driven prioritization frameworks have shown marked improvements in real-time adaptability, operational efficiency, and resource allocation compared to traditional methods. Additionally, the paper systematically addresses critical challenges, including data integrity, algorithmic bias, and the necessity for interdisciplinary collaboration. By offering a robust analytical framework and demonstrating the effectiveness of AI-driven prioritization, this research provides valuable insights and practical guidelines for stakeholders and researchers. Ultimately, this work contributes significantly to the ongoing discourse on requirement prioritization, setting the stage for more resilient and efficient CAS management strategies in complex and dynamic operational environments.
International Journal of Geoinformatics Science and Technology, 2025
Small satellite systems are increasingly transforming industries such as communication, earth obs... more Small satellite systems are increasingly transforming industries such as communication, earth observation, and scientific research. As these systems evolve, traditional Document-Based Systems Engineering (DBSE) methods, which have long been employed in larger, more conventional space projects, often need to meet the fast-paced demands of small satellite development. Traditional DBSE frequently leads to inefficiencies, costly errors, and delays that escalate project timelines and budgets. The need for a more agile and adaptive approach to system design, integration, and validation has never been greater. Model-Based Systems Engineering (MBSE) offers a promising solution, providing a more integrated and flexible framework that can handle small satellite initiatives' complexity and rapid evolution. This paper explores the limitations of traditional systems engineering in this context and underscores the growing necessity of adopting MBSE. Through a comprehensive literature review, this study identifies critical advantages of MBSE, including improved stakeholder communication, enhanced traceability of requirements and design decisions, and earlier identification of potential system risks. These benefits translate into faster, more cost-effective engineering processes, ultimately leading to higher-quality small satellite systems. The findings suggest that MBSE not only addresses the challenges faced by traditional methods but also positions small satellite development for future success in a highly competitive field. This research highlights MBSE's transformative potential, offering valuable insights for industry practitioners and researchers seeking to improve engineering practices and promote innovation in small satellite systems.
Requirements Engineering (RE) is experiencing a significant transformation that requires urgent a... more Requirements Engineering (RE) is experiencing a significant transformation that requires urgent attention and creativity from industry experts to address the challenges of future systems and software development. At the core of this evolution is Requirements Engineering (RE), an essential process that defines, records, and maintains the fundamental aspects of software and systems design. With the rapid increase in complexity and interconnectedness across different industries, the issues linked to requirements engineering are becoming more pressing, significantly affecting costs, timelines, and overall performance. This article outlines current research trends in requirements engineering, focusing on emerging challenges and future pathways. We investigate advancements in RE techniques, methodologies, and tools, assessing their effects on building complex systems. Furthermore, we emphasize the growing significance of stakeholder engagement and agile practices in shaping requirements, highlighting their contributions to improving collaboration and adaptability. By evaluating the present state of practice and pinpointing critical areas of research concern, this article offers a guide for future requirements engineering studies, enabling researchers and practitioners to address the changing needs of software and systems engineering.
Carbon nanotube-based sensors -A review
Social Science Research Network, 2024
Sustainable Water Management in Urban Areas: Integrating Innovative Technologies and Practices to Address Water Scarcity and Pollution
Social Science Research Network, 2024
World Journal Of Advanced Research and Reviews, Apr 30, 2024
In this era of technological innovation, the integration of machine learning techniques with Geog... more In this era of technological innovation, the integration of machine learning techniques with Geographic Information Systems (GIS) has emerged as a transformative approach to spatial analysis and decision-making. This abstract explores the synergy between machine learning and GIS, highlighting their combined potential to unlock new insights from spatial data, automate analytical processes, and enhance predictive modeling capabilities. By leveraging machine learning algorithms such as neural networks, random forests, and support vector machines, GIS practitioners can address complex spatial challenges more effectively, ranging from land cover classification and urban growth modeling to environmental monitoring and disaster response. Through case studies and examples, this abstract demonstrates the practical applications of machine learning in GIS, illustrating its role in advancing our understanding of spatial phenomena and informing evidence-based decision-making across diverse domains. As the field continues to evolve, embracing the fusion of machine learning and GIS holds immense promise for unlocking the full potential of spatial analysis and shaping a more sustainable and resilient future. Illustrative case studies and examples showcase the practical applications of machine learning in GIS across diverse domains. From land cover classification and urban growth modeling to environmental monitoring and disaster response, machine learning algorithms offer versatile solutions to address a wide spectrum of spatial challenges. Through the fusion of machine learning and GIS, researchers and practitioners gain unprecedented insights into complex spatial phenomena, enabling them to make data-driven decisions that are both informed and actionable. Looking ahead, the fusion of machine learning and GIS holds immense promise for advancing our understanding of spatial dynamics and shaping a more sustainable and resilient future. As the field continues to evolve, embracing this interdisciplinary approach is essential for unlocking the full potential of spatial analysis, fostering innovation, and addressing pressing societal challenges at local, regional, and global scales. By leveraging the synergies between machine learning and GIS, we can chart a path towards a more data-driven, informed, and equitable world.
World Journal Of Advanced Research and Reviews, Mar 30, 2024
This review explores the synergistic relationship between advanced microscopy techniques and mech... more This review explores the synergistic relationship between advanced microscopy techniques and mechanical engineering, outlining their profound impact on materials science and mechanical system design. We delve into the multifaceted applications of electron microscopy, X-ray diffraction, and spectroscopic methods in understanding microstructural dynamics, mechanical properties, and failure mechanisms of materials integral to mechanical engineering. Through a comprehensive synthesis of recent research, we emphasize the pivotal role these techniques play in optimizing material performance, bolstering structural integrity, and driving innovation in mechanical design. By elucidating the intricate details of material behavior at the microscale, advanced microscopy contributes to informed decision-making in material selection and design processes. Furthermore, we address emerging trends and prospects, underscoring the continued synergy between advanced microscopy and mechanical engineering. This collaboration remains at the forefront of materials science and technology, promising ongoing advancements that will shape the future landscape of mechanical design and materials innovation.
World Journal of Advanced Engineering Technology and Sciences, Jan 30, 2024
This paper focuses on the application of Value Stream Mapping (VSM) within the context of the ele... more This paper focuses on the application of Value Stream Mapping (VSM) within the context of the electronics manufacturing industry, aiming to improve its operational efficiency and financial performance. The study thoroughly analyzes costs, integrating VSM to justify economic benefits. Data was collected directly from the manufacturing floor to create a current state map, enabling the identification of non-value-added activities and sources of waste. Areas for potential improvement were pinpointed to reduce or eliminate these inefficiencies. By implementing these proposed enhancements, the paper outlines a future state map for the electronics manufacturing process and presents the results of applying Value Stream Mapping. Comparing the current and future state maps, the study reveals that embracing Lean principles, in conjunction with Value Stream Mapping, can significantly benefit the electronics manufacturing industry. Specifically, it can reduce production lead times by 67.84% and decrease costs by 8.69%. This research underscores the financial implications of adopting Value Stream Mapping, illustrating that by combining Lean principles with VSM, industries can offer rapid customer responses at lower costs, a crucial factor for improving competitive performance in the existing market landscape.
World Journal Of Advanced Research and Reviews, Feb 28, 2024
Accurate prediction of accident risks plays a crucial role in proactively implementing safety mea... more Accurate prediction of accident risks plays a crucial role in proactively implementing safety measures and allocating resources effectively. This paper introduces an innovative approach aimed at improving accident risk prediction by harnessing unique data sources and extracting insights from diverse yet sparse datasets. Traditional models often face limitations due to a lack of diversity and scope in the available data, which hinders their predictive capabilities. In response to this challenge, our study integrates a broad spectrum of heterogeneous data, encompassing traffic flow, weather conditions, road infrastructure details, and historical accident records. To overcome the difficulties associated with sparse data, we employ advanced data science techniques such as feature engineering, imputation, and machine learning. The paper introduces a novel dataset that amalgamates various data types, establishing a robust foundation for our predictive model. Through meticulous analysis, we derive valuable insights from these diverse sources, significantly enhancing our ability to assess accident risks. The proposed approach offers numerous advantages, including the capacity to predict accidents in areas that were previously underrepresented and under varying conditions. We rigorously evaluate the model's performance through extensive experimentation and validate its accuracy using real-world accident data. Our results indicate substantial improvements in prediction accuracy compared to conventional models. This research contributes significantly to the field of accident risk prediction by highlighting the potential benefits of integrating heterogeneous sparse data and leveraging advanced data science techniques. The study underscores the importance of tapping into novel data sources and extracting concealed patterns and insights to promote safety and optimize resource allocation in accident-prone regions, fostering more secure environments.
Enhancing Operations Quality Improvement through Advanced Data Analytics
Journal of computer science engineering and software testing, 2024
This study focuses on the application of data analytics algorithms for real-time monitoring in ad... more This study focuses on the application of data analytics algorithms for real-time monitoring in additive manufacturing processes. The utilization of advanced analytics plays a pivotal role in enhancing the quality control and efficiency of these manufacturing techniques. The research explores how data-driven insights can be harnessed to identify, analyze, and rectify deviations in the manufacturing process, ensuring optimal performance and product quality. By integrating sophisticated monitoring algorithms, the study aims to create a robust framework that continuously analyzes various parameters during additive manufacturing. This includes monitoring factors such as temperature, pressure, and material properties in real-time. The collected data is processed through advanced analytics tools to detect anomalies or deviations from the expected standards. The implementation of machine learning algorithms further facilitates predictive maintenance and proactive adjustments, contributing to the overall reliability and effectiveness of additive manufacturing processes. The outcomes of this research hold significant implications for industries relying on additive manufacturing technologies, providing a foundation for improved process control and product quality. The study contributes to the growing field of Industry 4.0 by showcasing the integration of data analytics as a key enabler for efficient and reliable additive manufacturing.
Next-Gen Production Excellence: A Deep Simulation Perspective on Process Improvement
Journals of Mechatronics Machine Design and Manufacturing, 2024
The pursuit of operational excellence and continuous improvement in the manufacturing sector has ... more The pursuit of operational excellence and continuous improvement in the manufacturing sector has led to the convergence of two prominent methodologies, Lean and Six Sigma. This abstract provides an overview of the comprehensive framework discussed in this paper, which blends the principles of Lean and Six Sigma to drive transformative enhancements in manufacturing processes. The proposed framework extends beyond surface-level improvements, delving deep into the intricacies of production systems. It encompasses a holistic approach that addresses process efficiency, waste reduction, quality enhancement, and cost optimization. By synergizing Lean's emphasis on eliminating waste and Six Sigma's focus on process variation reduction, this framework approaches a comprehensive strategy for manufacturers to succeed in operational excellence. Through real-world case studies and practical insights, this paper highlights the tangible benefits of adopting the Lean and Six Sigma deep framework and its potential to revolutionize the manufacturing landscape.
This study focuses on the implementation of modern and intelligent logistics vehicles equipped wi... more This study focuses on the implementation of modern and intelligent logistics vehicles equipped with advanced tracking and security features. In response to the evolving landscape of logistics management, the proposed system integrates cuttingedge technologies to enhance efficiency and ensure the security of the entire logistics process. The core component of this implementation is the incorporation of state-of-theart tracking mechanisms, enabling real-time monitoring of vehicle locations and movements. Furthermore, the system addresses the paramount concern of security by introducing advanced security measures. Through the utilization of sophisticated tracking technologies and security protocols, the proposed logistics vehicles aim to safeguard both customer and provider data. The implementation includes the integration of QR code concepts, creating a binary image system that conceals sensitive information and ensures access only to authorized users. In addition to tracking and security, the study delves into the realm of information mining, employing techniques such as classification, clustering, and recommendation to extract meaningful patterns from vast datasets. Collaborative filtering techniques are incorporated to enhance customer experience by recommending services based on user preferences and historical data. This abstract encapsulates the comprehensive approach of deploying modern logistics vehicles, emphasizing their intelligence through advanced tracking, robust security measures, and data-driven insights. The proposed system aims to revolutionize logistics management, providing a seamless and secure experience for both customers and service providers in the dynamic logistics landscape.
Enhancing Accident Risk Prediction with Novel Data and Findings from Heterogeneous Sparse Sources
Journal of data mining and management, 2024
Accurate accident risk prediction is paramount for proactive safety measures and resource allocat... more Accurate accident risk prediction is paramount for proactive safety measures and resource allocation. This paper introduces an innovative approach to enhance accident risk prediction by leveraging novel data sources and uncovering insights from heterogeneous sparse data. Traditional models often suffer from limitations in data diversity and scope, hindering their predictive capabilities. In response, our study integrates a wide range of heterogeneous data, including traffic flow data, weather conditions, road infrastructure, and historical accident records. To address the challenges of working with sparse data, we employ advanced data science techniques, including feature engineering, imputation, and machine learning. The paper presents a new dataset that combines diverse data types, providing a comprehensive foundation for our predictive model. Through rigorous analysis, we extract valuable insights from these heterogeneous sources to improve accident risk assessment. The proposed approach offers several advantages, including the ability to predict accidents in previously underrepresented areas and under varying conditions. We evaluate the model's performance through extensive experimentation and validate its accuracy against real-world accident data. Our findings demonstrate significant enhancements in prediction accuracy compared to conventional models. This research contributes to the field of accident risk prediction by showcasing the potential of heterogeneous sparse data integration and advanced data science techniques. It highlights the importance of utilizing novel data sources and the value of uncovering hidden patterns and insights to foster safer environments and more efficient resource allocation in accident-prone areas.
In this paper, we delve into the public discourse surrounding COVID-19 on Twitter to unearth the ... more In this paper, we delve into the public discourse surrounding COVID-19 on Twitter to unearth the collective sentiments, concerns, and spread of information during the pandemic. By leveraging a dataset of relevant tweets and corresponding ISO country codes, our analysis will map out the geographical and digital landscape of these conversations. The significance of this work lies in its potential to inform public health strategies, shape policymaking, and contribute to social research on crisis communication. Stakeholders ranging from health officials to the public have a vested interest in understanding the contours of this dialogue. Our objective is to craft a data-driven narrative through visualizations that reveal how the world engages with the pandemic on the digital front, providing actionable insights into global and local responses to COVID-19 using Machine Learning techniques.
Assessing the Sustainability of Bitcoin Mining: Comparative Review of Renewable Energy Sources
This study conducts a comprehensive assessment of Bitcoin mining's sustainability, concen... more This study conducts a comprehensive assessment of Bitcoin mining's sustainability, concentrating on the incorporation of renewable energy sources such as hydroelectric, wind, solar, and geothermal power. It investigates the adaptability and potential constructive collaboration of these energy systems in the context of the Bitcoin mining scenario. This research presents a detailed understanding of the possibilities and challenges involved with integrating hydro, wind, solar, and geothermal resources into sustainable Bitcoin mining processes by methodically examining energy efficiency, environmental effect, and economic viability. The study contributes to the discussion on establishing a sustainable future for digital currencies by exposing significant connections between renewable energy technology and the cryptocurrency industry. To achieve the stated goals, the study carefully examines the energy usage strongly associated with Bitcoin mining and transaction processing. It investigates the elements that contribute to Bitcoin's energy consumption, addressing the environmental consequences of its energy-intensive activities. The study aims to find feasible techniques for reducing Bitcoin's carbon footprint, to cultivate a thorough understanding of the complex relationship between the digital currency revolution and its environmental consequences. It emphasizes the importance of balancing technical developments with environmental responsibilities. The concept contributes to establishing a conscious and sustainable future for the digital currency ecosystem by addressing the numerous components of Bitcoin's energy usage and advocating a collective commitment to protecting the environment in the face of advancing technology paradigms.
Securing the Future: A Case Study on the Role of TPM Technology in the Domestic Electronics Industry amid the COVID-19 Pandemic
Journal of Industrial Mechanics, Dec 31, 2022
The global pandemic, particularly the COVID 19 crisis, has significantly impacted various manufac... more The global pandemic, particularly the COVID 19 crisis, has significantly impacted various manufacturing sectors, notably the electronics industry in Bangladesh. In the wake of the COVID 19 pandemic, industries faced unprecedented operational challenges, necessitating agile and innovative management strategies. This case study examines the role of Total Productive Maintenance (TPM) in bolstering the resilience of the domestic electronics industry during this global health crisis. As supply chains buckled and consumer demand fluctuated wildly, TPM principles provided a framework for maintaining equipment reliability and production efficiency when it was most needed. The study dissects how TPM's focus on proactive maintenance and employee empowerment helped organizations navigate the pandemic's complexities, ensuring minimal disruption in production despite stringent health regulations and workforce limitations. By analyzing real-world applications of TPM during this period, the research reveals the methodology's direct impact on mitigating downtime, preserving quality, and enhancing operational adaptability. The insights presented underscore the significance of TPM in fortifying the electronics sector against such systemic shocks, ultimately contributing to sustained industrial performance and economic stability in tumultuous times.
Contemporary technologies afford us the opportunity to augment and substitute traditional in-pers... more Contemporary technologies afford us the opportunity to augment and substitute traditional in-person classes with computer-based resources commonly known as virtual labs. Before the global pandemic, physical classrooms offered a hands-on learning environment for students. However, the pandemic has rendered in-person labs impractical, making it challenging for students to engage with faculty members directly. In the present scenario, the preference for remote education has surged due to safety concerns. With proper internet access, students now have the flexibility to conduct experiments from the comfort of their homes. Despite the inherent challenges associated with online learning, virtual labs serve as a valuable solution, addressing some of these issues. The utilization of simulation-based learning, especially in subjects like physics, chemistry, and biology, contributes significantly to students' comprehension of key concepts. Virtual labs play a pivotal role in fostering problem-solving skills, critical thinking, creativity, imagination, understanding of scientific processes, laboratory skills, as well as heightened interest and perception. Consequently, it becomes imperative for educators to integrate virtual labs into their teaching methodologies to enhance the quality of instruction and improve students' learning outcomes.
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Papers by Iqtiar M Siddique