Industrial Domains Research Articles

Nanomaterial Safety Management in Practice: Expert Perspectives on Implementation Challenges and Organizational Approaches in Singapore.

This study investigated nanomaterial safety management through semistructured interviews with 14 subject matter experts across industry, research, and regulatory domains. Expert perceptions, practical implementation of exposure controls, and barriers to effective safety management in Singapore's nanotechnology sector were examined. Thematic analysis using MAXQDA software identified 6 key themes: information management and organizational practices (28%), training and knowledge management (26%), documentation and risk management (21%), advanced manufacturing and implementation insights (14%), geographic and regulatory framework variations (11%), and measurement and characterization challenges (6%). The study identified significant variations in how organizations approach safety management, particularly in information sharing, training delivery, and control measure implementation. Technical challenges in exposure measurement and characterization emerged as critical barriers, while documentation and risk management practices varied considerably across different organizational contexts. This research contributes to nanomaterial safety management by providing insights into practical implementation challenges across diverse organizational contexts.

A Data-Driven Approach for Generating Synthetic Load Profiles with GANs

The generation of realistic electrical load profiles is essential for advancing smart grid analytics, demand forecasting, and privacy-preserving data sharing. Traditional approaches often rely on large, high-resolution datasets and complex recurrent neural architectures, which can be unstable or ineffective when training data are limited. This paper proposes a data-driven framework based on a lightweight 1D Convolutional Wasserstein GAN with Gradient Penalty (Conv1D-WGAN-GP) for generating high-fidelity synthetic 24 h load profiles. The model is specifically designed to operate on small- to medium-sized datasets, where recurrent models often fail due to overfitting or training instability. The approach leverages the ability of Conv1D layers to capture localized temporal patterns while remaining compact and stable during training. We benchmark the proposed model against vanilla GAN, WGAN-GP, and Conv1D-GAN across four datasets with varying consumption patterns and sizes, including industrial, agricultural, and residential domains. Quantitative evaluations using statistical divergence measures, Real-vs-Synthetic Distinguishability Score, and visual similarity confirm that Conv1D-WGAN-GP consistently outperforms baselines, particularly in low-data scenarios. This demonstrates its robustness, generalization capability, and suitability for privacy-sensitive energy modeling applications where access to large datasets is constrained.

From Detection to Solution: A Review of Machine Learning in PM2.5 Sensing and Sustainable Green Mitigation Approaches (2021–2025)

Particulate matter 2.5 (PM2.5) pollution poses severe threats to public health, ecosystems, and urban sustainability. With increasing industrialization and urban sprawl, accurate pollutant monitoring and effective mitigation of PM2.5 have become global priorities. Recent advancements in machine learning (ML) have revolutionized PM2.5 sensing by enabling high-accuracy predictions, and scalable solutions through data-driven approaches. Meanwhile, sustainable green technologies—such as urban greening, phytoremediation, and smart air purification systems—offer eco-friendly, long-term strategies to reduce PM2.5 levels. This review, covering research publications from 2021 to 2025, systematically explores the integration of ML models with conventional sensor networks to enhance pollution forecasting, pollutant source attribution, and intelligent pollutant monitoring. The paper also highlights the convergence of ML and green technologies, including nature-based solutions and AI-driven environmental planning, to support comprehensive air quality management. In addition, the study critically examines integrated policy frameworks and lifecycle-based assessments that enable equitable, sector-specific mitigation strategies across industrial, transportation, energy, and urban planning domains. By bridging the gap between cutting-edge technology and sustainable practices, this study provides a comprehensive roadmap for researchers to combat PM2.5 pollution.

Robust Watermarking of Tiny Neural Networks by Fine-Tuning and Post-Training Approaches

Because neural networks pervade many industrial domains and are increasingly complex and accurate, the trained models themselves have become valuable intellectual properties. Developing highly accurate models demands increasingly higher investments of time, capital, and expertise. Many of these models are commonly deployed in cloud services and on resource-constrained edge devices. Consequently, safeguarding them is critically important. Neural network watermarking offers a practical solution to address this need by embedding a unique signature, either as a hidden bit-string or as a distinctive response to specially crafted “trigger” inputs. This allows owners to subsequently prove model ownership even if an adversary attempts to remove the watermark through attacks. In this manuscript, we adapt three state-of-the-art watermarking methods to “tiny” neural networks deployed on edge platforms by exploiting symmetry-related properties that ensure robustness and efficiency. In the context of machine learning, “tiny” is broadly used as a term referring to artificial intelligence techniques deployed in low-energy systems in the mW range and below, e.g., sensors and microcontrollers. We evaluate the robustness of the selected techniques by simulating attacks aimed at erasing the watermark while preserving the model’s original performances. The results before and after attacks demonstrate the effectiveness of these watermarking schemes in protecting neural network intellectual property without degrading the original accuracy.

A Review of Assistive Devices in Synovial Joints: Records, Trends, and Classifications

This article presents a comprehensive review of assistive devices for synovial joints, addressing their definitions, classifications, and technological advancements. The historical evolution of artificial exoskeletons, orthoses, prostheses, and splints is analyzed, emphasizing their impact on rehabilitation and the enhancement of human mobility. Through a systematic compilation of scientific literature, patents, and medical regulations, the study clarifies terminology and classifications that have often been imprecisely used in scientific discourse. The review examines the biomechanical principles of the musculoskeletal system and the kinematics of synovial joints, providing a reference framework for the optimization and design of these devices. Furthermore, it explores the various types of artificial exoskeletons, and their classification based on structure, mobility, power source, and control system, as well as their applications in medical, industrial, and military domains. Finally, this study highlights the necessity of a systematic approach in the design and categorization of these technologies to facilitate their development, comparison, and effective implementation, ultimately improving users’ quality of life.

Advancements in Titanium Dioxide Nanotube-Based Sensors for Medical Diagnostics: A Two-Decade Review.

Over the past two decades, titanium dioxide nanotubes (TiO2 NTs) have gained considerable attention as multifunctional materials in sensing technologies. Their large surface area, adjustable morphology, chemical stability, and photoactivity have positioned them as promising candidates for diverse sensor applications. This review presents a broad overview of the development of TiO2 NTs in sensing technologies for medical diagnostics over the last two decades. It further explores strategies for enhancing their sensing capabilities through structural modifications and hybridization with nanomaterials. Despite notable advancements, challenges such as device scalability, long-term operational stability, and fabrication reproducibility remain. This review outlines the evolution of TiO2 NT-based sensors for medical diagnostics, highlighting both foundational progress and emerging trends, while providing insights into future directions for their practical implementation across scientific and industrial domains.

Randomized Feature and Bootstrapped Naive Bayes Classification

Naive Bayes (NB) classifiers are widely used for their simplicity, computational efficiency, and interpretability. However, their predictive performance can degrade significantly in real-world settings where the conditional independence assumption is often violated. More complex NB variants address this issue but typically introduce structural complexity or require explicit dependency modeling, limiting their scalability and transparency. This study proposes two lightweight ensemble-based extensions—randomized feature naive Bayes (RF-NB) and randomized feature bootstrapped naive Bayes (RFB-NB)—designed to enhance robustness and predictive stability without altering the underlying NB model. By integrating randomized feature selection and bootstrap resampling, these methods implicitly reduce feature dependence and noise-induced variance. Evaluation across twenty real-world datasets spanning medical, financial, and industrial domains demonstrates that RFB-NB consistently outperformed classical NB, RF-NB, and k-nearest neighbor in several cases. Although random forest achieved higher average accuracy overall, RFB-NB demonstrated comparable accuracy with notably lower variance and improved predictive stability specifically in datasets characterized by high noise levels, large dimensionality, or significant class imbalance. These findings underscore the practical and complementary advantages of RFB-NB in challenging classification scenarios.

Karaya and Kondagogu tree gum carbohydrate polymers: A sustainable source for biobased products.

Karaya and Kondagogu tree gum carbohydrate polymers: A sustainable source for biobased products.

Exploring Optimal Path Planning : A Comparative Study of Algorithms for Automated Guided Vehicles

In today's industrial and logistics landscape, automated guided vehicles (AGVs) play a pivotal role in streamlining operations and boosting productivity. A critical aspect of AGV functionality lies in optimal path planning, where these vehicles navigate intricate environments from a starting point to a predefined destination while considering factors such as obstacle avoidance, time efficiency, and energy consumption. This paper presents a comprehensive comparative study of three widely utilized path planning algorithms: Dijkstra, A*, and Breadth-First Search (BFS). Moreover, the study offers practical insights and recommendations for selecting the most suitable algorithm tailored to specific application requirements. By shedding light on effective path planning strategies for AGVs, this research aims to drive advancements in automated systems, facilitating the development of more efficient, reliable, and adaptable industrial and logistics solutions in the age of automation. This study contributes to the enhancement of AGV technology, paving the way for optimized operations and improved performance in industrial and logistics domains. Keywords: A*, Breadth-First Search (BFS), Computational Complexity, Dijkstra, Efficiency, Logistics, Obstacle Avoidance, Optimization, Path Planning Algorithms

Thermally Radiative Flow of Cattaneo–Christov Heat Flux in MHD Darcy–Forchheimer Micropolar Nanofluid With Activation Energy

ABSTRACTThe present inquiry examines the necessity for enhanced thermal transfer approaches across multiple industrial domains, such as energy generation and processing of materials, through an investigation of the intricate dynamics of micropolar nanofluids. The main objective is to numerically simulate the Cattaneo–Christov heat flux in magnetohydrodynamics (MHD) to investigate the radiative behavior of Darcy–Forchheimer micropolar nanofluids, including the effects of activation energy. The study presumes steady‐state conditions and employs particular constitutive equations to characterize the behavior of the nanofluid. The governing equations, which incorporate binary chemical interactions, radiation, and a thermal source, are reformulated with similarity variables into a system of nonlinear ordinary differential equations (ODEs). The BVP4C MATLAB software is utilized for obtaining numerical solutions. The study indicates that an increase in thermophoresis, thermal source, radiation, and Brownian motion factors improves thermal distributions in micropolar nanofluid flow. Moreover, increased radiation parameters result in a rise in the thermal transmission rate, while enhancing activation energy factors leads to a decrease. The findings are essential for enhancing temperature control in systems and for the development of efficient thermal appliances.

Multi-agent systems: the future of distributed AI platforms for complex task management

This article examines the transformative potential of multi-agent systems (MAS) as a paradigm shift in distributed Artificial Intelligence for complex problem-solving. Moving beyond single-agent architectures, these collaborative networks of autonomous agents offer unprecedented capabilities through specialization, parallel processing, and collective intelligence. The text surveys the current MAS landscape, exploring architectural patterns, core components, and real-world applications across smart cities, logistics, and industrial domains. It addresses technical challenges in communication efficiency, coordination mechanisms, and security frameworks while highlighting future directions including self-organizing networks, cognitive capabilities, and integration with emerging technologies like quantum computing, edge processing, and digital twins. Through critical evaluation of empirical evidence, this article demonstrates how multi-agent systems enable more robust, adaptive, and efficient solutions to increasingly complex problems across diverse domains, representing not merely incremental advancement but a fundamental reconceptualization of AI deployment strategies.

ЕВОЛЮЦІЯ ГАЛУЗЕЙ В ІТ-АУТСТАФФІНГУ УКРАЇНИ: АНАЛІЗ ДЕСЯТИРІЧЧЯ

This article presents the results of an interdisciplinary analysis of the transformation of the sectoral structure in Ukraine’s IT outstaffing industry over the period 2014–2023. The aim of the study is to identify shifts in the dynamics of demand for specializations within the outstaffing model and to establish the relationship between the popularity of specific sectors and the remuneration levels of professionals. The empirical basis of the research consists of data collected through surveys of Ukrainian IT specialists, which include information on the year of project participation, net salary, years of experience, level of English proficiency, job position, and the industry domain of the project. During the analytical process, the data underwent cleaning and normalization, particularly the unification of domain names, which enabled the construction of a representative database of annual frequency distributions. Subsequent modeling involved the calculation of the annual share of each sector among all respondents and the computation of Pearson correlation coefficients between sector popularity and average salaries. As a result, the study identified a group of consistently dominant domains forming the core of Ukraine’s IT outstaffing landscape, including financial technologies, e-commerce, mobile solutions, digital healthcare, and data analytics. Particular attention was also paid to emerging sectors that have shown growth between 2020 and 2023 and exhibit a strong positive correlation with salary levels - namely, digital education, educational services, agricultural technologies, and cloud computing. While these sectors remain relatively niche, they signal structural shifts in the market and the emergence of new opportunities for outstaffing companies. The findings have practical significance for executives, analysts, HR professionals, and strategists working in the IT services sector. They support not only the adjustment of HR policies and the redirection of investment toward the development of relevant competencies, but also enable forecasting of new domains with high complexity and profitability. This research contributes to a deeper understanding of the current dynamics of the IT outstaffing market and serves as a foundation for informed managerial decision-making in a time of continuous technological change. The results may be used to shape companies’ strategic development priorities, build adaptive business models, and position themselves more effectively in the international IT services market.

Artificial Intelligence in Generative Design: A Structured Review of Trends and Opportunities in Techniques and Applications

This review explores the intersection of Artificial Intelligence (AI) and Generative Design (GD) in engineering within the mechanical, industrial, civil, and architectural domains. Driven by advances in AI and computational resources, this intersection has grown rapidly, yielding over 14,000 publications since 2016. To map the research landscape, this review employed semantic search and Natural Language Processing, parsing 14,355 publications to ultimately select the 88 most relevant studies through clustering and topic modelling. These studies were categorised according to AI and GD techniques, application domains, benefits, and limitations, providing insights into research trends and practical implications. The results reveal a significant growth in the integration of advanced generative AI methods, notably Generative Adversarial Networks for direct design generation, alongside the continued use of genetic algorithms and surrogate models (e.g., Convolutional Neural Networks and Multilayer Perceptrons) to manage computational complexity. Structural and aerodynamic applications were the most common, with benefits including improvements in computational efficiency and design diversity. However, barriers remain, including data generation costs, model accuracy, and interpretability. Research opportunities include the development of generalisable foundation surrogate models, the integration of emerging generative methods such as diffusion models and large language models, and the explicit consideration of manufacturability constraints within generative processes.

Solution Strategy for High Gain Glass Fiber and MHz Mode‐Locked Laser

Abstract The advancement of Er‐activated fiber holds significant implications for applications in scientific and industrial domains such as optical fiber communication, precision measurement, and advanced manufacturing. It strongly relies on the performance of the Er‐doped active material, and a candidate with both high gain and robust mechanical properties is urgently required. Herein, a solution strategy for the development of heavily Er‐activated high‐gain silicate fiber and laser devices is proposed. Theoretical and experimental studies reveal that the incorporation of the inert rare‐earth ions provides a unique environment for the dispersion of Er3+ ions, thus greatly enhancing their radiative transition efficiency. In addition, a heavily Er‐activated silicate glass fiber (ESGF) hybridized with inert rare‐earth is designed and fabricated. It possesses excellent gain response with a net gain coefficient of ≈2.12 dB cm−1, which is the highest gain coefficient among the Er‐activated silicate fiber ever reported. Furthermore, utilizing a 3.8‐cm‐long ESGF, an all‐fiber‐integrated passively mode‐locked fiber laser device is successfully constructed, with a fundamental frequency repetition rate of 73 MHz and a spectral bandwidth of 12.46 nm. These findings are believed to bring new strategies for the exploration of advanced rare‐earth glass fiber materials and devices.

Redefining plastic terminology: The urgent need for standardised definitions in science and policy

Plastic pollution is a pervasive global threat, yet efforts to mitigate it are hindered by inconsistent terminology across scientific, industrial, and policy domains. Key terms, such as “polymer,” “plastic,” and “macromolecule” are often used interchangeably despite distinct meanings. This semantic confusion undermines research integrity, muddles regulatory frameworks, and impedes effective environmental management. Without universally accepted definitions or a clear classification system, data comparability, policy implementation, and interdisciplinary collaboration are significantly compromised. This study systematically examines the scope and impact of terminological inconsistencies in plastics discourse. We conducted a structured review of recent (2020 – 2025) peer-reviewed literature spanning polymer science and environmental policy to assess how definitional ambiguity affects research outcomes and decision-making. The findings reveal that ambiguous usage of fundamental terms has led to misinterpretations in scientific studies, inconsistent policy decisions, and fragmented mitigation strategies. In response, we propose a standardization framework guided by the International Union of Pure and Applied Chemistry principles, delineating clear criteria to distinguish polymers, plastics, and macromolecules. We recommend embedding these standardized definitions across academic publications, industry standards, and environmental policies to improve communication, ensure regulatory clarity, and support sustainable management practices. By establishing a coherent global terminology for plastics, this work underscores an urgent call for collective action. Standardizing the language of plastics will not only enhance data comparability and strengthen international policy initiatives, but also ensure that scientists, policymakers, and industry leaders can collaboratively craft effective, evidence-based solutions to plastic pollution.

Enhancing revenue generation in Bangladesh’s FinTech sector: a comprehensive analysis of real-time predictive customer behavior modeling in AWS using a hybrid OptiBoost-EnsembleX model

Customer behavior holds pivotal significance within the fintech industry, both in offline and online domains, influencing revenue generation. The application of data analytics to scrutinize customer behavior is a critical factor in optimizing financial outcomes. The anticipation of future customer conduct is a cornerstone for resource allocation in sales and marketing, enabling strategic decision-making in manufacturing operations, inventory planning, and point-of-sale scenarios. The intricate nature of customer behavior analysis necessitates innovative methodologies. This study introduces a real-time predictive model: OptiBoost-EnsembleX (Optuna-tuned CatBoost and LightGBM classifiers in a soft-voting ensemble framework) integrating data analytics and unsupervised machine learning techniques to discern and understand customer conduct. The investigation utilized a range of machine learning algorithms, such as random forest, support vector machine (SVM), XGBoost, CatBoost, and LightGBM, to develop models. This employs a unique dataset that consists of 10,000 examples of customer behavior. This investigation culminates in identifying CatBoost as the model that demonstrates the highest accuracy in predicting customer behavior. The selected model incorporated a real-time web application, representing a practical manifestation of the proposed solution. The seamless integration of the developed model into a machine-learning pipeline hosted on Amazon EC2 servers ensures its deployment in a production environment. This investigation makes a substantial contribution to the fintech industry by introducing a comprehensive and efficient method for analyzing customer behavior in real time, which has implications for improving decision-making and optimizing operations.

Differences Between Research Log Datasets and Development Field Logs and the Creation of the Complexity Evaluation Index

In the industrial domain, logs are widely applied in the management and maintenance of software systems to ensure reliability and availability. Furthermore, in the research field, various deep learning methods such as CNNs, LSTMs, and Transformers have been reported to achieve high accuracy in anomaly detection studies. However, there are challenges to their adoption in development fields. One reason is the limited datasets used in research, which lack a comprehensive evaluation for general applicability. To address this, we have prepared metrics to assess the complexity of log datasets necessary for creating a log generator for research purposes. We conducted a comparative study on the complexity of datasets in both research and industrial domains. Our evaluation of log sequence complexity, using frequency of occurrence and the Gini coefficient, showed that industrial logs are more complex across all metrics. This highlights the increased need for datasets close to the industrial domain for research purposes. Our study's findings suggest that a clear metric for dataset complexity can be achieved by converting logs into templates and then into sequences of size 10, evaluated using the Gini coefficient or kurtosis. Future work will involve developing a generator that produces logs close to those found in development environments, using these metrics as target values.

Deploying AI on Edge: Advancement and Challenges in Edge Intelligence

In recent years, artificial intelligence (AI) has achieved significant progress and remarkable advancements across various disciplines, including biology, computer science, and industry. However, the increasing complexity of AI network structures and the vast number of associated parameters impose substantial computational and storage demands, severely limiting the practical deployment of these models on resource-constrained edge devices. Although edge intelligence methods have been proposed to alleviate the computational and storage burdens, they still face multiple persistent challenges, such as large-scale model deployment, poor interpretability, privacy and security vulnerabilities, and energy efficiency constraints. This article systematically reviews the current advancements in edge intelligence technologies, highlights key enabling techniques including model sparsity, quantization, knowledge distillation, neural architecture search, and federated learning, and explores their applications in industrial, automotive, healthcare, and consumer domains. Furthermore, this paper presents a comparative analysis of these techniques, summarizes major trade-offs, and proposes decision frameworks to guide deployment strategies under different scenarios. Finally, it discusses future research directions to address the remaining technical bottlenecks and promote the practical and sustainable development of edge intelligence. Standing at the threshold of an exciting new era, we believe edge intelligence will play an increasingly critical role in transforming industries and enabling ubiquitous intelligent services.

Open-Source Collaboration and Technological Innovation in the Industrial Software Industry: A Multi-Case Study

Open-source collaboration, as both an open and cooperative software development paradigm and a novel production model in the era of the industrial internet, plays a pivotal role in overcoming technological bottlenecks in the industrial software industry. However, previous studies have often treated open-source collaboration as a single unified concept and have not explored the specific types of open-source collaboration and their differential effects on technological innovation. To address these gaps, this study aims to answer two core research questions: (1) What are the different types of open-source collaboration models based on their characteristics? (2) How do these collaboration models influence technological innovation in the industrial software industry? Drawing upon four representative collaboration cases in the industrial software domain, this study conducts within-case and cross-case comparative analyses to propose a typological framework based on the dimensions of coreness and complementarity. The analysis identifies four distinct open-source collaboration models: (1) single-core with high complementarity, (2) single-core with low complementarity, (3) multi-core with high complementarity, and (4) multi-core with low complementarity. The formation of these models is shaped by three key factors: strategic intentions, resource endowments, and technological capabilities. Moreover, different collaboration types exert varied impacts on organizational characteristics, innovation strategies, and technological impacts. Theoretically, this study makes an original contribution by opening the “black box” of open-source collaboration and revealing the internal mechanisms through which it shapes innovation dynamics. Practically, the findings offer targeted insights for enterprises, policymakers, and open-source communities in selecting appropriate collaboration models that align with innovation goals, thereby supporting technological upgrading and ecosystem resilience in the industrial software industry.

An Integrated and Flexible Review Framework to Evaluate the Evolution and Barriers of Digital-Twin Technologies in Industrial and Healthcare Domains

An Integrated and Flexible Review Framework to Evaluate the Evolution and Barriers of Digital-Twin Technologies in Industrial and Healthcare Domains