Akhil Gupta Chigullapally, Sharvan Vittala, Razin Farhan Hussian
et al.
The fast pace of modern AI is rapidly transforming traditional industrial systems into vast, intelligent and potentially unmanned autonomous operational environments driven by AI-based solutions. These solutions leverage various forms of machine learning, reinforcement learning, and generative AI. The introduction of such smart capabilities has pushed the envelope in multiple industrial domains, enabling predictive maintenance, optimized performance, and streamlined workflows. These solutions are often deployed across the Industrial Internet of Things (IIoT) and supported by the Edge-Fog-Cloud computing continuum to enable urgent (i.e., real-time or near real-time) decision-making. Despite the current trend of aggressively adopting these smart industrial solutions to increase profit, quality, and efficiency, large-scale integration and deployment also bring serious hazards that if ignored can undermine the benefits of smart industries. These hazards include unforeseen interoperability side-effects and heightened vulnerability to cyber threats, particularly in environments operating with a plethora of heterogeneous IIoT systems. The goal of this study is to shed light on the potential consequences of industrial smartness, with a particular focus on security implications, including vulnerabilities, side effects, and cyber threats. We distinguish software-level downsides stemming from both traditional AI solutions and generative AI from those originating in the infrastructure layer, namely IIoT and the Edge-Cloud continuum. At each level, we investigate potential vulnerabilities, cyber threats, and unintended side effects. As industries continue to become smarter, understanding and addressing these downsides will be crucial to ensure secure and sustainable development of smart industrial systems.
This study investigates the impact of artificial intelligence (AI) adoption on job loss rates using the Global AI Content Impact Dataset (2020--2025). The panel comprises 200 industry-country-year observations across Australia, China, France, Japan, and the United Kingdom in ten industries. A three-stage ordinary least squares (OLS) framework is applied. First, a full-sample regression finds no significant linear association between AI adoption rate and job loss rate ($β\approx -0.0026$, $p = 0.949$). Second, industry-specific regressions identify the marketing and retail sectors as closest to significance. Third, interaction-term models quantify marginal effects in those two sectors, revealing a significant retail interaction effect ($-0.138$, $p < 0.05$), showing that higher AI adoption is linked to lower job loss in retail. These findings extend empirical evidence on AI's labor market impact, emphasize AI's productivity-enhancing role in retail, and support targeted policy measures such as intelligent replenishment systems and cashierless checkout implementations.
The evolution of agentic systems represents a significant milestone in artificial intelligence and modern software systems, driven by the demand for vertical intelligence tailored to diverse industries. These systems enhance business outcomes through adaptability, learning, and interaction with dynamic environments. At the forefront of this revolution are Large Language Model (LLM) agents, which serve as the cognitive backbone of these intelligent systems. In response to the need for consistency and scalability, this work attempts to define a level of standardization for Vertical AI agent design patterns by identifying core building blocks and proposing a \textbf{Cognitive Skills } Module, which incorporates domain-specific, purpose-built inference capabilities. Building on these foundational concepts, this paper offers a comprehensive introduction to agentic systems, detailing their core components, operational patterns, and implementation strategies. It further explores practical use cases and examples across various industries, highlighting the transformative potential of LLM agents in driving industry-specific applications.
Artificial Intelligence (AI) is redefining the frontiers of scientific domains, ranging from drug discovery to meteorological modeling, yet its integration within industrial manufacturing remains nascent and fraught with operational challenges. To bridge this gap, we introduce Aethorix v1.0, an AI agent framework designed to overcome key industrial bottlenecks, demonstrating state-of-the-art performance in materials design innovation and process parameter optimization. Our tool is built upon three pillars: a scientific corpus reasoning engine that streamlines knowledge retrieval and validation, a diffusion-based generative model for zero-shot inverse design, and specialized interatomic potentials that enable faster screening with ab initio fidelity. We demonstrate Aethorix's utility through a real-world cement production case study, confirming its capacity for integration into industrial workflows and its role in revolutionizing the design-make-test-analyze loop while ensuring rigorous manufacturing standards are met.
Sathish Krishna Anumula, Sivaramkumar Ponnarangan, Faizal Nujumudeen
et al.
A mix of intelligent systems and robotics is making engineering industries much more efficient, precise and able to adapt. How artificial intelligence (AI), machine learning (ML) and autonomous robotic technologies are changing manufacturing, civil, electrical and mechanical engineering is discussed in this paper. Based on recent findings and a suggested way to evaluate intelligent robotic systems in industry, we give an overview of how their use impacts productivity, safety and operational costs. Experience and case studies confirm the benefits this area brings and the problems that have yet to be solved. The findings indicate that intelligent robotics involves more than a technology change; it introduces important new methods in engineering.
Abdulrahman Aljabbab, Ayman N. Qadrouh, Mamdoh S. Alajmi
et al.
The white sands from the Cretaceous Biyadh Formation, Saudi Arabia, are exploited as a raw material for the glass sand industry and contain approximately 15 wt% kaolinite. SEM reveals that the kaolinite develops as a blocky, pore-filling cement of euhedral, pseudo-hexagonal plates ranging from 5 to 10 µm in width and from 10 to 20 µm in length, suggesting its authigenic origin during the late diagenesis. Anatase occurs as very fine grains interspersed among the kaolinite flakes. The authigenic kaolinite is associated with remarkably high concentrations of rare earth elements (REEs) (average 1,126 ppm), Zr (average 1,133 ppm), and Li (average 187 ppm). REEs occur as the phosphate phase, while Zr is associated with the anatase, and Li is hosted by kaolinite. These high concentrations are probably due to the enrichment of these elements during the diagenesis of the studied kaolinite. Alteration of the source minerals during periods of subaerial exposure and/or at shallow burial depths due to meteoric water influx is deduced as a possible mechanism for the formation of the studied kaolinite. The supergene origin of the kaolinite is confirmed by the relatively high concentrations of Ti and Ce + Y + La. The geochemistry and large crystal size suggest the potential application of the kaolinite in paper coating and as a filler. While the high Al2O3 and low SiO2 contents suggest its potential application in super-standard porcelain and sanitary ware, partial removal of TiO2 will allow its application in the pharmaceutical and cosmetics industries. Additionally, REEs, Zr, and Li can be separated from the white sands as valuable byproducts, along with the kaolinite.
Akintayo Adeniji, Bamidele Dahunsi, David Kolawole
et al.
Lightweight sandwich panels are mainly utilized in the aerospace and automobile industries, and are increasingly explored for sustainable construction. This study investigates the mechanical performance of sandwich panels composed of mortar and concrete facings with expanded polystyrene (EPS) and particle board (PB) cores. Mortar facings were a 1:3 mix of cement and sand, a 0.65 water-cement ratio, and a 1:2:4 mix of cement, sand, and granite for the concrete, with a 0.5 water-cement ratio. Cube (75 × 75 × 75 mm3) and prism (100 × 100 × 400 mm3) samples were fabricated with 15 mm facings and 45 mm cores, then cured and tested for compressive and flexural strength at 7, 14, 21, and 28 days in line with BS EN 12390-3:2019 and ASTM C293. At 28 days, concrete-faced PB-core panels achieved a compressive strength of 8.18 N/mm², approximately 17% higher than 6.96 N/mm2 of EPS-core. Mortar-faced PB-core panels reached 6.44 N/mm², 64.13% compared to 4.13 N/mm² for mortar-faced EPS panels. Flexural strength followed similar patterns: concrete-faced PB-core panels increased by 96% from 9.18 N/mm² at 7 days to 9.48 N/mm² at 28 days, while EPS-core panels improved by 87.9% from 6.56 N/mm² to 7.46 N/mm². ANOVA (α = 0.05) confirmed statistically significant differences between core types, with PB consistently outperforming EPS due to higher stiffness and improved core–facing bonding. EPS and PB core panels remain suited for non-load-bearing applications.
Abstract The composite materials are widely used across industries however, these materials are prone to damages like cracking and delamination due to its complexity. The Electromechanical Impedance (EMI) technique offers a reliable non-destructive solution for detecting such damage using piezoelectric sensors and enabling effective structural health monitoring and enhancing safety and durability. This study explores the application of the EMI technique for monitoring damages in composite fibre concrete specimens. The specimens were prepared using Ordinary Portland Cement (OPC), fly ash, and polypropylene, glass fiber mixture, water, fine and coarse aggregates. The Piezoelectric sensors were employed to record conductance and susceptance signatures, enabling early detection and quantification of damages. The severity of damages were assessed using statistical indices such as Root Mean Square Deviation (RMSD), Mean Absolute Percentage Deviation (MAPD), and Correlation Coefficient (CC) revealing higher sensitivity. A notable leftward shift in EMI signatures with increasing damage was confirmed progressive structural degradation. Additionally, structural parameters equivalent stiffness and equivalent damping were evaluated, demonstrating a decrease in stiffness and an increase in damping with greater damage depth. Temperature effects on EMI responses were also investigated, necessitating compensation for reliable analysis. An Artificial Neural Network (ANN) model was trained using Levenberg-Marquardt (LM) algorithm and implemented to predict conductance values and damage depth. The developed ANN showed high accuracy, with strong agreement between experimental and predicted results. Overall, the findings confirm the EMI technique’s potential for SHM of composite fiber concrete and integration with machine learning for improved predictive its durability assessment.
Abstract Supplementary cementitious materials have been widely used to partially replace ordinary Portland cement. An increasing level of the substitution is a highly effective way to reduce carbon dioxide emission and energy consumption of cementitious materials. However, the availability of traditional supplementary cementitious materials (e.g., fly ash and slag) cannot meet the needs from the cement industry in the near future, particularly for the underdeveloped countries because they have limited industries with such by-products. In this study, calcined clay and limestone powder as supplementary cementitious materials are adopted to replace cement at ultra-high substitution levels for the production of sustainable cementitious materials. The influence of ultra-high substitution of calcined clay and limestone powder (i.e., from 50% to 80%) on the microstructure and mechanical properties of cementitious materials are investigated. The results show that the addition of calcined clay and limestone powder together with 50%-70% substitution of cement is beneficial for improving the toughness, densifying the microstructure, and refining the pore structure of cementitious materials even though the compressive strength is not obviously improved. This study reveals that the key factor to affact the substitution level is the availability of portlandite in the mixture, which controls the amount of calcined clay participating in the pozzolanic reaction.
Geopolymer Concrete is a new innovative type of concrete, and it is used widely in the construction industries. This type of concrete comes into place due to reduced cement content usage in the construction of structures. Already we are using cement as a binding material widely in the construction sector, but the problem is due to the cement content Co2 emissions are mainly produced and one more problem is greenhouse gases are increasing rapidly during the manufacturing of cement. Then after a lot of researchers, finally we got a geopolymer as a replacement for cement. By replacing cement content with geopolymer, we can reduce the cost of construction and reuse the structural materials. So, this type of concrete is different from standard conventional concrete. We can minimize Co2 and greenhouse gases’ problem in the atmosphere and make the structure an environmentally friendly solution. So, this type of concrete is very famous in the construction industry, and there are benefits also excellent. So, it can be used widely in construction sectors worldwide. This paper may help understand Geopolymer Concrete for everyone quickly. It gives a quick review of the Geopolymer Concrete.
Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-control robots with partial human control. Robots can be classified into three major kinds: industry robots, autonomous robots and mobile robots. Industry robots are used in industries and factories to perform mankind tasks in the easier and faster way which will help in developing products. Typically industrial robots perform difficult and dangerous tasks, as they lift heavy objects, handle chemicals, paint and assembly work and so on. They are working all the time hour after hour, day by day with the same precision and they do not get tired which means that they do not make errors due to fatigue. Indeed, they are ideally suited to complete repetitive tasks.
The realization of FDI and DDI from January to December 2022 reached Rp1,207.2 trillion. The largest FDI investment realization by sector was led by the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industry sector, followed by the Mining sector and the Electricity, Gas, and Water sector. The uneven amount of FDI investment realization in each industry and the impact of the COVID-19 pandemic in Indonesia are the main issues addressed in this study. This study aims to identify the factors that influence the entry of FDI into industries in Indonesia and measure the extent of these factors' influence on the entry of FDI. In this study, classical assumption tests and hypothesis tests are conducted to investigate whether the research model is robust enough to provide strategic options nationally. Moreover, this study uses the ordinary least squares (OLS) method. The results show that the electricity factor does not influence FDI inflows in the three industries. The Human Development Index (HDI) factor has a significant negative effect on FDI in the Mining Industry and a significant positive effect on FDI in the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industries. However, HDI does not influence FDI in the Electricity, Gas, and Water Industries in Indonesia.
Laureana Luciani, Juliana Cruz, Victor Ballestin
et al.
The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system’s expansion is hindered by renewable generation’s climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries’ energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers.
The cement industries as binder materials, produces 1.6 billion metric tons’ carbon dioxide (CO2) in 2022 and cause depletion in ozone layer. The cement is being used in modern construction and cause the environmental impact. Therefore, there is a quest for a sustainable alternative binder to reduce the CO2 emission from the conventional cement production and their effective disposal methods in construction and demolished (C&D) waste is paramount. This article delves into the production of recycled cement by leveraging various methods such as mechanical activation, chemical activation, and heat treatment of C&D waste, ultimately contributing to the carbon neutrality. A comparison with traditional cement production sheds light on energy consumption, CO2 emissions, and resource utilization. The recycled cement displays increased water demand, accelerated early setting, extended final setting time, and reduced compressive strength. However, when used solely as a cementitious matrix, recycled cement can readily achieve up to 30 MPa in 28 days. Utilising recycled cement in concrete applications demonstrates its efficacy in the construction industry. The insights provided herein offer a holistic understanding of the production processes, properties, and applications of recycled cement, reinforcing its potential as a viable and environmentally friendly alternative in the realm of construction materials.
Charissa Puttbach, Gary S. Prinz, Cameron D. Murray
The elastic stiffness of bulk concrete materials results from the complex interaction of aggregates, voids, and hydrated cement (which can have multiple hardened phases at multiple length scales). Given the complexities associated with understanding the arrangement of these particles within bulk concrete volumes, estimations for elastic modulus often rely on empirical correlations with unit weight and compressive strength. Such estimations are inherently scale-dependent and fail to capture variability in mix designs, particularly the variability found in specialty concrete mixes. To develop a scale-independent method for estimating elastic modulus from mix-design volume fraction information, this study explores a novel bottom-up approach using cement paste phase stiffness values determined through micro-mechanical experimentation and randomized Monte-Carlo spring arrangement simulations. Statistical representations of cement paste phase stiffness distributions and bulk volume fraction data are combined to provide estimations for elastic stiffness in both the composite cement paste and bulk concrete containing fine aggregate and fibers. Resulting a priori estimations of UHPC cement paste stiffness from the micro-mechanical upscaling simulations were within 4% of measured values (based on mix-design and void volume fraction information alone) for a selected sample of mix proportions. When applied to the two UHPC mixes containing fibers and fine aggregate, upscaling simulations consistently overpredicted the measured elastic modulus, likely due to the aggregate-cement interfacial transition zone (ITZ) properties that were not captured in the micro-mechanical testing.
Decarbonization of the cement and concrete industries is one of the top priorities on the path to a carbon-neutral economy. This article presents a novel model for evaluating the emissions from the production of metakaolin (MK) as a supplementary cementitious material used in ternary blended cements (e.g., 35% metakaolin, 15% limestone, and 50% portland cement) and an accompanying decisions-support tool (MKC-Tool). Applications with a case study in California showed 36%–39% reductions in greenhouse gas (GHG) emissions from ternary blends with MK compared to portland cement. Compared to commercially available blended cements, the ternary blend showed the lowest global warming potential. All the cements containing fly ash showed higher GHG intensities than the ternary blend (16%–42% higher GHG emissions). The development of cements made with portland cement, metakaolin, and limestone at an industrial scale will have the potential to contribute 5%–50% to the global reduction of GHG emissions from the cement industry.
A large amount of waste coming out from industries has posed a great challenge in its disposal and effect on the environment. Particularly fly ash, coming out from thermal power plants, which contains aluminosilicate minerals and creates a lot of environmental problems. In recent years, it has been found that geopolymer may give solutions to waste problems and environmental issues. Geopolymer is an inorganic polymer first introduced by Davidovits. Geopolymer concrete can be considered as an innovative and alternative material to traditional Portland cement concrete. Use of fly ash as a raw material minimizes the waste production of thermal power plants and protects the environment. Geopolymer concretes have high early strength and resistant to an aggressive atmosphere. Methods of preparation and characterization of fly ash-based geopolymers have been presented in this paper. The properties of geopolymer cement/mortar/concrete under different conditions have been highlighted. Fire resistance properties and 3D printing technology have also been discussed.