Abstract Adding a large amount of light elements such as aluminum to steels is not a new concept recalling that several Fe–Al–Mn–C alloys were patented in 1950s for replacement of nickel or chromium in corrosion resistance steels. However, the so-called lightweight steels or low-density steels were revisited recently, which is driven by demands from the industry where steel has served as a major structural material. Strengthening without loss of ductility has been a triumph in steel research, but lowering the density of steel by mixing with light elements will be another prospect that may support the competitiveness against emerging alternatives such as magnesium alloys. In this paper, we review recent studies on lightweight steels, emphasizing the concept of alloy design for microstructures and mechanical properties. The influence of alloying elements on the phase constituents, mechanical properties and the change of density is critically reviewed. Deformation mechanisms of various lightweight steels are discussed as well. This paper provides a reason why the success of lightweight steels is strongly dependent on scientific achievements even though alloy development is closely related to industrial applications. Finally, we summarize some of the main directions for future investigations necessary for vitalizing this field of interest.
In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision-making, but remains a major challenge in current data-driven approaches. In this work, we introduce a diffusion-based posterior sampling framework that inherently produces well-calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post-hoc calibration. In extensive evaluations on synthetic distributions, the Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty-aware modeling in industrial applications.
Dev Vikesh Doshi, Mehjabeen Tasnim, Fernando Landeros
et al.
Phishing attacks through text, also known as smishing, are a prevalent type of social engineering tactic in which attackers impersonate brands to deceive victims into providing personal information and/or money. While smishing awareness and cyber education are a key method by which organizations communicate this awareness, the guidance itself varies widely. In this paper, we investigate the state of practice of how 149 well-known brands across 25 categories educate their customers about smishing and what smishing prevention and reporting advice they provide. After conducting a comprehensive content analysis of the brands, we identified significant gaps in the smishing-related information provided: only 46\% of the 149 brands mentioned the definition of smishing, less than 1\% had a video tutorial on smishing, and only 50\% of brands provided instructions on how to report. Our study highlights variation in terminology, prevention advice, and reporting mechanisms across industries, with some brands recommending potentially ineffective strategies such as "ignoring suspicious messages." These findings establish a baseline for understanding the current state of industry smishing awareness advice and provide specific areas where standardization improvements are needed. From our evaluation, we provide recommendations for brands on how to offer streamlined education to their respective customers on smishing for better awareness and protection against increasing smishing attacks.
Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink for real-time processing, while SPARQL and SWRL-based reasoning provide context-dependent stream discovery. Experimental evaluations demonstrate the effectiveness of combining semantic models, context-aware reasoning and distributed stream processing to enable interoperable data workflows for Industry 5.0 environments.
As hardware design complexity increases, hardware fuzzing emerges as a promising tool for automating the verification process. However, a significant gap still exists before it can be applied in industry. This paper aims to summarize the current progress of hardware fuzzing from an industry-use perspective and propose solutions to bridge the gap between hardware fuzzing and industrial verification. First, we review recent hardware fuzzing methods and analyze their compatibilities with industrial verification. We establish criteria to assess whether a hardware fuzzing approach is compatible. Second, we examine whether current verification tools can efficiently support hardware fuzzing. We identify the bottlenecks in hardware fuzzing performance caused by insufficient support from the industrial environment. To overcome the bottlenecks, we propose a prototype, HwFuzzEnv, providing the necessary support for hardware fuzzing. With this prototype, the previous hardware fuzzing method can achieve a several hundred times speedup in industrial settings. Our work could serve as a reference for EDA companies, encouraging them to enhance their tools to support hardware fuzzing efficiently in industrial verification.
Despina Tomkou, George Fatouros, Andreas Andreou
et al.
This paper introduces a novel integration of Retrieval-Augmented Generation (RAG) enhanced Large Language Models (LLMs) with Extended Reality (XR) technologies to address knowledge transfer challenges in industrial environments. The proposed system embeds domain-specific industrial knowledge into XR environments through a natural language interface, enabling hands-free, context-aware expert guidance for workers. We present the architecture of the proposed system consisting of an LLM Chat Engine with dynamic tool orchestration and an XR application featuring voice-driven interaction. Performance evaluation of various chunking strategies, embedding models, and vector databases reveals that semantic chunking, balanced embedding models, and efficient vector stores deliver optimal performance for industrial knowledge retrieval. The system's potential is demonstrated through early implementation in multiple industrial use cases, including robotic assembly, smart infrastructure maintenance, and aerospace component servicing. Results indicate potential for enhancing training efficiency, remote assistance capabilities, and operational guidance in alignment with Industry 5.0's human-centric and resilient approach to industrial development.
Ali Bakhshi Movahed, Hamed Nozari, Aminmasoud Bakhshi Movahed
Technology plays an undeniable role in today's industrial world, especially in manufacturing and smart factories. Unlike previous industrial revolutions, humans are at the core of the fifth generation of the Industrial Revolution. One of the critical aspects of Industry 5.0 (I 5.0) is its emphasis on human-centricity. The integration of modern technologies can be clearly observed in smart factories, which offer enhanced comfort and professionalism. This study highlights the significance of I 5.0 and smart factory production (SFP). A total of 36 articles are reviewed and systematically categorized using the meta-synthesis methodology. The research emphasizes the influence of I 5.0 on SFP through the use of modern technologies and comprehensive policy frameworks. This new paradigm has the potential to streamline people's lives and bring a transformative shift to smart factory production lines. Enhancing the structure of factories appears feasible under this optimistic perspective.
Industrial Anomaly Detection (IAD) is a cornerstone for ensuring operational safety, maintaining product quality, and optimizing manufacturing efficiency. However, the advancement of IAD algorithms is severely hindered by the limitations of existing public benchmarks. Current datasets often suffer from restricted category diversity and insufficient scale, leading to performance saturation and poor model transferability in complex, real-world scenarios. To bridge this gap, we introduce Real-IAD Variety, the largest and most diverse IAD benchmark. It comprises 198,950 high-resolution images across 160 distinct object categories. The dataset ensures unprecedented diversity by covering 28 industries, 24 material types, 22 color variations, and 27 defect types. Our extensive experimental analysis highlights the substantial challenges posed by this benchmark: state-of-the-art multi-class unsupervised anomaly detection methods suffer significant performance degradation (ranging from 10% to 20%) when scaled from 30 to 160 categories. Conversely, we demonstrate that zero-shot and few-shot IAD models exhibit remarkable robustness to category scale-up, maintaining consistent performance and significantly enhancing generalization across diverse industrial contexts. This unprecedented scale positions Real-IAD Variety as an essential resource for training and evaluating next-generation foundation IAD models.
Sotiris Michaelides, Daniel Eguiguren Chavez, Martin Henze
With the ongoing adoption of 5G for communication in industrial systems and critical infrastructure, the security of industrial UEs such as 5G-enabled industrial robots becomes an increasingly important topic. Most notably, to meet the stringent security requirements of industrial deployments, industrial UEs not only have to fully comply with the 5G specifications but also implement and use correctly secure communication protocols such as TLS. To ensure the security of industrial UEs, operators of industrial 5G networks rely on security testing before deploying new devices to their production networks. However, currently only isolated tests for individual security aspects of industrial UEs exist, severely hindering comprehensive testing. In this paper, we report on our ongoing efforts to alleviate this situation by creating an automated security testing framework for industrial UEs to comprehensively evaluate their security posture before deployment. With this framework, we aim to provide stakeholders with a fully automated-method to verify that higher-layer security protocols are correctly implemented, while simultaneously ensuring that the UE's protocol stack adheres to 3GPP specifications.
The demand for sustainable, high-performance composites is increasing in industries such as automotive, aerospace, and construction. However, conventional fiber-reinforced polymer composites suffer from environmental limitations, poor bioactivity, and fatigue susceptibility. This study develops a novel epoxy matrix composite reinforced with 3 % (12 g) Al₂O₃ embedded banyan fibers to enhance mechanical strength and bioactivity. Experimental analysis demonstrates notable improvements in the sample with 12 g Al₂O₃, tensile strength (67.49 MPa), flexural strength (69.38 MPa), impact energy (19.47 kJ/m²), and Shore D hardness (47). Fatigue testing confirms durability, withstanding 40 MPa stress for 14,000 cycles due to improved fiber-matrix bonding and uniform filler dispersion. Scanning Electron Microscopy validates structural integrity, while antibacterial assessments show a 12 mm inhibition zone against Streptococcus pyogenes and significant biofilm reduction via Confocal Laser Scanning Microscopy. These findings highlight the composite’s potential for applications requiring mechanical durability and bioactivity, including medical devices and bioactive environments.
We conducted constant pressure Gibbs ensemble Monte Carlo molecular simulations to explore the adsorption separation of 3 binary gas mixtures: CH4/CO, C2F6/N2, and SO2/CO2 within slit pores. Key findings indicate that CH4/CO, a mixture of 2 supercritical gases at room temperature, shows modest adsorption selectivity of around 4, even at elevated pressures of 20 MPa. In contrast, the C2F6/N2 mixture, consisting of supercritical N2 and C2F6 near its critical temperature, exhibits significantly higher selectivity, reaching tens to hundreds. The SO2/CO2 mixture, with both gases in a subcritical state at room temperature, displays intermediate selectivity between the other 2 systems. Our simulations revealed that the adsorption selectivity for CH4/CO and C2F6/N2 mixtures displays distinct single- and double-peaked trends with varying pore widths under medium to high pressures, corresponding to monolayer and bilayer adsorption phenomena. The SO2/CO2 system, however, presented a more intricate adsorption mechanism, potentially involving 3-layer molecular adsorption within the pores. Expanding our investigation to 276 mixtures, we discovered an important trend: a higher ratio of critical temperatures between mixture components correlates with increased adsorption selectivity and simplified separation processes. Intriguingly, when this ratio approaches unity, separation difficulty escalates. Additionally, we identified a significant linear relationship between adsorption selectivity and the ratio of adsorption heats at low pressures (0.1 MPa) for a pore width of 0.8 nm, underscoring the impact of thermodynamic properties on separation efficacy. These insights are crucial for the development of energy-efficient gas separation materials, which are vital for applications such as natural gas purification and carbon capture and storage, contributing to a sustainable energy future.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
Abstract In the past few years the global need for low cost, high performance and good quality materials has caused a shift in research from monolithic to composite materials. In case of MMC's, aluminum matrix composite due their high strength to weight ratio, low cost and high wear resistance are widely manufactured and used in structural applications along with aerospace and automobile industry. Also a simple and cost effective method for manufacturing of the composites is very essential for expanding their application. Reinforcements like particulate alumina, silicon carbide, graphite, fly ash etc can easily be incorporated in the melt using cheap and widely available stir casting method. This paper presents a review on the mechanical and tribological properties of stir cast aluminum matrix composites containing single and multiple reinforcement. Addition of alumina to aluminum has shown an increase in its mechanical and tribological properties. Organic reinforcement like fly ash, coconut ash also improved the tensile and yield strength. Self-lubricating property of graphite improved the machinability of aluminum. Many authors have also reported about modified stir casting route.
Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier to use. However, LLMs' application in industrial settings is underexplored. This paper introduces a framework for integrating LLMs to achieve end-to-end control of industrial automation systems. At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism that provides real-time data for LLM inference. The framework supplies LLMs with real-time events on different context semantic levels, allowing them to interpret the information, generate production plans, and control operations on the automation system. It also supports structured dataset creation for fine-tuning on this downstream application of LLMs. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets for LLM fine-tuning and testing. This approach enables a more adaptive automation system that can respond to spontaneous events, while allowing easier operation and configuration through natural language for more intuitive human-machine interaction. We provide demo videos and detailed data on GitHub: https://github.com/YuchenXia/LLM4IAS.
Bhawna Sobti, Khulood Jaber Jasim Alnuaimi, Dema Saeed Bakhit Alneyadi
et al.
Date fruits (DFs) are lucrative fruits with both nutritional and medicinal benefits. Although DF byproducts have gained interest as potential functional food ingredients, the functional role of DF waxes remains unexplored. Most plant fruits, including DFs,had a lipophilic cuticular layer; and constitutes of epicuticular wax (EPW), which has an important role in respiration losses, mechanical support, fruit softening, and pathogen resistance. In this study, the compositional and antioxidant properties of EPW from Majdool, Khalas, and Fard DFs were investigated. Moreover, thermal transitions of EPW were examined. Results revealed the highest EPW yield in Majdool (16.62 ± 1.68 mg wax/cm2) and the lowest in Khalas (0.075 ± 0.008 mg wax/cm2). Ultraviolet–visible spectroscopy showed maximum absorptivity at 250 and 290 nm across all varieties, corresponding to conjugated dienes and trienes, respectively. Fourier transform infrared peaks confirmed the presence of hydroxyl, methylene, and carbonyl groups, including specific domains of phenolic compounds. Khalas and Fard EPWs reported more total phenolic content and scavenging activity than Majdool EPW. The variations were observed in the melting temperatures of EPW, ranging from 60 °C to 85 °C. These findings establish a theoretical foundation for the potential application of DF EPW in food and pharmaceutical industries.
Vafaeva Khristina Maksudovna, Chhetri Abhishek, Sudan Prerak
et al.
This research examines the characteristics and ecological viability of polymer matrix nanocomposites used in sustainable packaging. Nanocomposites were produced by combining varied proportions of polymer and nanofiller material. Through mechanical testing, it was determined that nanocomposite formulation 3 had the maximum tensile strength of 55 MPa, as well as a Young's modulus of 3.5 GPa, showing greater stiffness in comparison to the other formulations. The evaluation of barrier qualities revealed that nanocomposite formulation 2 exhibited the most minimal oxygen permeability at a rate of 8 cc/m2/day and the lowest water vapor transmission rate at 4.5 g/m2/day, showing very efficient performance in preventing the passage of gases and moisture. The environmental impact study showed that nanocomposite formulation 3 had the most efficient energy consumption during manufacture, with a rate of 1.8 kWh/kg. It also had the lowest waste creation, with just 0.08 kg/kg, and the lowest CO2 emissions, with only 0.4 kg/kg. Nanocomposite formulation 3 demonstrated substantial improvements in mechanical characteristics, barrier properties, and environmental impact indicators when compared to the reference formulations, as shown by the percentage change analysis. In summary, this study showcases the capabilities of polymer matrix nanocomposites, specifically formulation 3, as environmentally friendly packaging materials that offer improved mechanical properties, effective barrier performance, and reduced ecological footprint. These findings contribute to the development of sustainable packaging solutions across different industries.