The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.
The Transfer Matrix Method (TMM) stands as the ubiquitous computational backbone for analyzing 1D wave propagation in layered media, underpinning critical product designs in photonics, seismology, and acoustics -- industries collectively valued in the tens of USD billions. Despite its essential role, legacy implementations of TMM create significant technical (and therefore strategic) bottlenecks, primarily due to a lack of straightforward differentiability and high computational costs associated with Uncertainty Quantification (UQ). This white paper assesses the current market footprint of TMM, identifies the economic "hidden costs" of traditional workflows, and outlines an emerging industrial alternative -- Differentiable Programming and Neural Surrogates -- and their own limitations.
Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi
et al.
Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and speed. Yet, organizations in regulated industries face strict constraints on which LLMs they can use - many cannot utilize commercial models due to data privacy regulations and compliance requirements, creating a significant challenge for achieving high-quality code optimization while maintaining cost-effectiveness. We address this by implementing a Mixture-of-Agents (MoA) approach that directly synthesizes code from multiple specialized LLMs, comparing it against TurinTech AI's vanilla Genetic Algorithm (GA)-based ensemble system and individual LLM optimizers using real-world industrial codebases. Our key contributions include: (1) First MoA application to industrial code optimization using real-world codebases; (2) Empirical evidence that MoA excels with open-source models, achieving 14.3% to 22.2% cost savings and 28.6% to 32.2% faster optimization times for regulated environments; (3) Deployment guidelines demonstrating GA's advantage with commercial models while both ensembles outperform individual LLMs; and (4) Real-world validation across 50 code snippets and seven LLM combinations, generating over 8,700 variants, addresses gaps in industrial LLM ensemble evaluation. This provides actionable guidance for organizations balancing regulatory compliance with optimization performance in production environments.
Ahmed M. El-Khatib, Mahmoud I. Abbas, Malak H. Eid
et al.
Abstract Nuclear Radiation shielding materials are essential in various industries, especially nuclear power, medical imaging, and space exploration. This research delves into the exploration of radiation shielding efficacy by infusing cement and waste marble composites with micro and nanoparticles of Cadmium Oxide (CdO) and Aluminum Oxide (Al2O3), focusing on key parameters such as half value layer (HVL), tenth value layer (TVL), linear attenuation coefficient (LAC), and mass attenuation coefficient (MAC). These composites show potential in enhancing the shielding performance due to the unique properties of the nanoparticles. We systematically characterize the structural, morphological, and radiation-shielding properties of the composites through experimentation. In addition to the Absorption buildup factor (EABF) was held in this study to help in enhancing the shielding material as it is an important parameter. The outcomes demonstrated that the CdO-Al2O3 particle addition enhanced the composites’ radiation shielding capabilities. Raising the weight% (Wt.%) of CdO-Al2O3 particles improved the efficiency of the shield. The outcomes also showed that nano-sized CdO-Al2O3 particles outperformed micro-sized particles in radiation shielding. The work shows the potential of CdO- Al2O3 doped cement-waste Marble matrix composites for applications including shielding against gamma radiation. This study correspondingly investigates the shielding capabilities using the FLUKA Monte Carlo code. The simulation employed a wide range of photon and neutron energies, up to 100 and 20 MeV respectively. The results show the effectiveness of the introduced composite in attenuating both gamma rays and neutrons, highlighting their potential applications in radiation shielding.
Industrial AI is transitioning from traditional deep learning models to large-scale transformer-based architectures, with the Industrial Internet of Things (IIoT) playing a pivotal role. IIoT evolves from a simple data pipeline to an intelligent infrastructure, enabling and enhancing these advanced AI systems. This survey explores the integration of IIoT with large models (LMs) and their potential applications in industrial environments. We focus on four primary types of industrial LMs: language-based, vision-based, time-series, and multimodal models. The lifecycle of LMs is segmented into four critical phases: data foundation, model training, model connectivity, and continuous evolution. First, we analyze how IIoT provides abundant and diverse data resources, supporting the training and fine-tuning of LMs. Second, we discuss how IIoT offers an efficient training infrastructure in low-latency and bandwidth-optimized environments. Third, we highlight the deployment advantages of LMs within IIoT, emphasizing IIoT's role as a connectivity nexus fostering emergent intelligence through modular design, dynamic routing, and model merging to enhance system scalability and adaptability. Finally, we demonstrate how IIoT supports continual learning mechanisms, enabling LMs to adapt to dynamic industrial conditions and ensure long-term effectiveness. This paper underscores IIoT's critical role in the evolution of industrial intelligence with large models, offering a theoretical framework and actionable insights for future research.
As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide considerable advantages for smart robotized applications in the industry.Workflow efficiency, collective decision enrichment even for executives, as well as a natural, resilient, and sustainable robotized assistance for the workforce are potential advantages. Hence, the Metaverse could consolidate the connection between Industry 4.0 and Industry 5.0. This paper identifies and puts forward potential advantages of the Metaverse for robotized applications and highlights how these advantages support goals pursued by the Industry 4.0 and Industry 5.0 visions. Keywords: Robotics, Metaverse, Digital Twin, VR/AR, AI/ML, Foundation Model;
Biotechnology Industry 5.0 is advancing with the integration of cutting-edge technologies like Machine Learning (ML), the Internet Of Things (IoT), and cloud computing. It is no surprise that an industry that utilizes data from customers and can alter their lives is a target of a variety of attacks. This chapter provides a perspective of how Machine Learning Security Operations (MLSecOps) can help secure the biotechnology Industry 5.0. The chapter provides an analysis of the threats in the biotechnology Industry 5.0 and how ML algorithms can help secure with industry best practices. This chapter explores the scope of MLSecOps in the biotechnology Industry 5.0, highlighting how crucial it is to comply with current regulatory frameworks. With biotechnology Industry 5.0 developing innovative solutions in healthcare, supply chain management, biomanufacturing, pharmaceuticals sectors, and more, the chapter also discusses the MLSecOps best practices that industry and enterprises should follow while also considering ethical responsibilities. Overall, the chapter provides a discussion of how to integrate MLSecOps into the design, deployment, and regulation of the processes in biotechnology Industry 5.0.
Tita Alissa Bach, Aleksandar Babic, Narae Park
et al.
The maritime industry requires effective communication among diverse stakeholders to address complex, safety-critical challenges. Industrial AI, including Large Language Models (LLMs), has the potential to augment human experts' workflows in this specialized domain. Our case study investigated the utility of LLMs in drafting replies to stakeholder inquiries and supporting case handlers. We conducted a preliminary study (observations and interviews), a survey, and a text similarity analysis (LLM-as-a-judge and Semantic Embedding Similarity). We discover that while LLM drafts can streamline workflows, they often require significant modifications to meet the specific demands of maritime communications. Though LLMs are not yet mature enough for safety-critical applications without human oversight, they can serve as valuable augmentative tools. Final decision-making thus must remain with human experts. However, by leveraging the strengths of both humans and LLMs, fostering human-AI collaboration, industries can increase efficiency while maintaining high standards of quality and precision tailored to each case.
Murugan Muthu, Sanjeev Kumar, Adrian Chajec
et al.
Replacement of cement with electric arc furnace (EAF) slag at higher volumes causes volumetric expansion; therefore, such blends are not recommended in concrete production. In this study, the effect of this slag on the performance and microstructure of mortar samples based on wollastonite (CaSiO<sub>3</sub>) was examined. The samples were cured in a CO<sub>2</sub>-rich environment, resulting in the formation of non-expansive products, including aragonite, calcite, and traces of tobermorite in the microstructure. The addition of slag above 20% affected the workability and strength developments. However, the formation of pores above 100 nm reduced with increasing slag content to 60%, highlighting the beneficial effect of slag when used in higher volumes. EAF slag contains a higher amount of Fe<sub>2</sub>O<sub>3</sub> which limits its disposal at landfills, but its increased use in the production of CO<sub>2</sub> gas-cured wollastonite concrete can reduce the environmental burdens caused by the Portland cement and steel manufacturing industries.
Vigleik Nicolai Kjeldal, Jarrett Wise, Geir Hareland
et al.
Cementing around the casing in oil and gas wellbores provides multiple benefits such as proper zonal isolation, casing support, and prevention of fluid migration. Wellbore cement is an important part of the completion and abandonment process. However, wellbore cement has some drawbacks such as micro-annuli formation or loss of zonal isolation. Nanoparticles (NPs) have been shown to improve the characteristics of wellbore drilling fluids but have not been used extensively in cement. The objective of this paper is to show the effect of NPs’ concentration on wellbore cement characteristics such as thickening time, viscosity, and fluid loss properties. Nanoparticle barite and magnetite were added to heavy cement and bentonite was added to light cement in intervals of 1, 3, and 5 % by weight of cement to test the resulting cement characteristics. The results showed that the thickening time increased for all concentrations of nanoparticles, except for the 5 % magnetite. The resulting yield stress of both cement mixtures increased for all concentrations of nanoparticles. The viscosity for all concentrations of nanoparticles in the heavy cement was greater than the control case, while no change in viscosity was seen with the light cement. Fluid loss generally decreased by increasing nanoparticle concentrations for both heavy and light cement. The results of this work in combination with results from the literature show that the addition of barite, magnetite, or bentonite nanoparticles can enhance wellbore cement without diminishing the pumpability and curing time.
The Safeen Mountain is one of the main mountains in the Iraqi Kurdistan Region, it forms one of the long anticlines trending NW – SE. The exposed formations on the top of the mountain are Qamchuqa, Bekhme, and Shiranish, with carbonate rocks of different types and thicknesses. Sampling took place in the exposed rocks on the top of the mountain where a road crosses the mountain, and a total of 20 samples out of 84 m thickness of the outcrop were collected. The distance between sampling intervals was depending on the lithological variation of the bedrock and each sample was collected to represent the sampling interval. The samples were subjected to XRF to indicate the main oxides percentages in each sample. The acquired results from the XRF showed the studied rocks can be used for cement and paper industries based on Iraqi Standards. They can also be considered using it in the sugar industry after a slight modification as well as in the drug industry.
Christophe Labbez, Lina Bouzouaid, Alexander E. S. Van Driessche
et al.
Wet chemistry C-S-H precipitation experiments were performed under controlled conditions of solution supersaturation in the presence and absence of gluconate and three hexitol molecules. Characterization of the precipitates with SAXS and cryo-TEM experiments confirmed the presence of a multi-step nucleation pathway. Induction times for the formation of the amorphous C-S-H spheroids were determined from light transmittance. Analysis of those data with the classical nucleation theory revealed a significant increase of the kinetic prefactor in the same order as the complexation constants of calcium and silicate with each of the organics. Finally, two distinct precipitation regimes of the C-S-H amorphous precursor were identified: i) a nucleation regime at low saturation indexes (SI) and ii) a spinodal nucleation regime at high SI where the free energy barrier to the phase transition is found to be of the order of the kinetic energy or less.
Natural clinoptilolite zeolite has been a popular supplementary cementitious material (SCM) due to its acceptable pozzolanic performance and the overall lower environmental footprint. Previous research established that milling is an effective pretreatment technique to further increase the pozzolanic reactivity of zeolitic tuffs leading to an increased specific surface area and amorphous contents. Therefore, the present study characterized the zeolite particles after ball milling for 1 and 3 h using phase analysis by X-ray diffraction (XRD), particle size distribution by laser diffraction, microstructural analysis by scanning electron microscopy (SEM), moisture absorption rate, and relative chemical dissolution. The performance of milled clinoptilolite zeolite as a SCM with the replacement of up to 20% portland cement was evaluated through hydration kinetics (heat of hydration, setting time, chemical shrinkage, degree of hydration), workability, compressive strength, autogenous shrinkage, drying shrinkage, and alkali-silica reaction (ASR). Results revealed that 1 and 3 h of milling led to an increased specific surface area, moisture absorption capacity, and relative dissolution of particles, but had no visible effects on the crystalline structure of zeolite particles compared to the unmilled zeolite particles. For the hydrated system, both 1 and 3-h milled zeolite increased the overall heat of hydration leading to an increased silicate and aluminate reaction along with the acceleration effects in the setting time. The compressive strength of up to 20% milled (1 and 3 h) zeolite samples was increased by about 20 to 25% compared to the unmilled zeolite samples at an early age which suggested an increasing pozzolanic response of milled zeolite particles in the system due to an increased volume of hydrated phases and degree of hydration. Milling slightly decreased the workability by demanding a higher content of fresh water which was released at a later age leading to a higher drying and autogenous shrinkage. In addition, milling reduced the internal curing capacity leading to damage to the porous structure of zeolite particles. The use of up to 20% 3-h milled zeolite reduced the deleterious expansion by about 80% due to ASR compared to the control sample and the overall performance of milled clinoptilolite zeolite as the SCM was satisfactory in the hydrated system.
Nathalie Barbosa Reis Monteiro, José Machado Moita Neto, Elaine Aparecida da Silva
Purpose: Companies that manufacture poles generate several negative environmental impacts, whose extent needs to be assessed to find ways to mitigate them. Methods: In this research, Life Cycle Assessment (LCA) was used as a methodology to measure the potential environmental impacts throughout the poles' life cycle. Primary data (amount of cement, gravel, sand, steel rebars, energy, water) were collected from industries located in Teresina, Piauí, Brazil, and information from the Ecoinvent 3.7.1 database (transport, solid waste, liquid effluents, particulate matter) was used. Results and discussion: The literature addresses pole production from a different perspective, making this study relevant to disseminate the life cycle thinking in concrete pole production. However, the literature points to a correlation trend for ecotoxicity and human toxicity indicators, as well as the results found in this research. Waste disposal stands out as an important source of impact for these industries, confirming the necessity of efficient management of these materials at the end of their lifespan and during the production process. The scenario analysis showed that is possible to reduce the potential impacts of these industries. Conclusion: The reuse of waste within the industry itself is feasible (using a shredder for this purpose) and can contribute to decreasing the extraction of natural deposits in various production processes related to the poles' life cycle and reducing their accumulation in the environment. The use of inputs from closer suppliers is a strategy that contributes to mitigating the potential impact of gaseous emissions, reducing the impact that generates global warming and climate change. In addition, other papers show viable alternatives in different scenarios, based on complex laboratory studies. Nevertheless, his approach shows how impacts can be mitigated with the adoption of simple actions such as the reuse of effluents and residues from these industries. It is possible to redefine the production process through a scenario close to the ideal, bringing environmental sustainability to the sector.
Hani Muhsen, Mohammed Al-Mahmodi, Rashed Tarawneh
et al.
Green hydrogen and power-to-X technologies hold significant potential in the global energy transition towards net-zero emissions. This is attributed to the premise that these technologies can decarbonize numerous sectors worldwide by providing versatile and sustainable energy carriers and industrial feedstocks to replace fossil-based fuels and chemicals. To this end, the qualitative benefits of green hydrogen and power-to-X technologies have been thoroughly examined for various applications in past years. In contrast, quantifying the potential penetration of such technologies on national and global levels still requires extensive research. Therefore, this paper investigates the prospective integration of green hydrogen and power-to-X technologies within Jordanian industries, considering their quantitative utilization potential for current and future capacities. The findings showed that the Jordanian food processing and heavy industries emerged as major sectors with substantial potential for incorporating green hydrogen and power-to-X products as alternative fuels or chemical feedstocks. In detail, the total potential utilization capacity for these sectors stood at around 57 thousand tons per year. Specifically, fertilizers production, cement industry, steel reforming, and oil refinery possess an annual potential capacity of around 6.8, 11.8, 12.7, and 25.8 thousand tons, respectively. It is also worth mentioning that the current utilization capacity of hydrogen in Jordanian industries was found to be around 8.9 thousand tons per annum, which is completely covered by fossil-based hydrogen to date. These results imply that there will be a promising market for green hydrogen and power-to-X utilization in Jordanian industries, which will play a significant role in integrated energy transition efforts in the future.
The study investigates the impact of downsizing layoffs on the profitability of construction industries listed in BSE India. In India, construction industries have adopted downsizing long back in the organization to improve the firms performance. For the purpose of the study, Secondary data of 15 Construction companies listed in BSE India have been considered for a period of 10 years from FY.2010 to FY2019. Data has been taken from the companys official website. The variable considered for the analysis is Other Expenses, Returns on Net Worth, Employee Expenses, Number of Employees, and Profit Per Employee. The study has used the Co-integration test to see co-integration between the variables, Ordinary Least Square (OLS) and Vector Auto Regression (VAR) the model used for estimating the impact of downsizing on the profitability of construction companies. OLS and VAR model has been used to draw a conclusion based on the P values and R square. From the result, it can be concluded that, Expect Profit Per Employees are the downsizing variable that has no significant impact on the profitability of the firms performance. Whereas the other Downsizing variables Employee Expenses and the Number of Employee has a significant impact on the profitability of the firms performance
The production of building materials is one of the most polluting industries for the natural environment. To change this, solutions are being sought that can reduce the environmental impact of the construction industry. One such solution is the modification of cement mixes with the addition of supplementary cementitious materials (SCMs). Such materials include granite powder waste and fly ash. Granite powder waste is generated when processing granite rocks, and fly ash is produced by burning fossil fuels. Both materials cause significant environmental problems. Their addition to cement-based materials may not only improve the condition of the natural environment, but also reduce the amount of cement used by the construction industry. The possibilities of reducing the amount of cement in cement mixes by using these materials were determined. It was concluded that the combined use of these materials in cement mixes enables the achievement of better mechanical properties, a lower amount of consumed embodied carbon dioxide (ECO2), and a decreased price of the mix. In conclusion, the most favourable proportions of both materials in cement composites were identified with regards to mechanical and environmental aspects.
Chemical engineering, Computer engineering. Computer hardware