The cement industry faces a number of challenges that include depleting fossil fuel reserves, scarcity of raw materials, perpetually increasing demand for cements and concretes, growing environmental concerns linked to climate change and an ailing world economy. Every tonne of Ordinary Portland Cement (OPC) that is produced releases on average a similar amount of CO2 into the atmosphere, or in total roughly 6% of all man-made carbon emissions. Improved production methods and formulations that reduce or eliminate CO2 emissions from the cement manufacturing process are thus high on the agenda. Emission reduction is also needed to counter the impacts on product cost of new regulations, green taxes and escalating fuel prices. In this regard, locally available minerals, recycled materials and (industry, agriculture and domestic) waste may be suitable for blending with OPC as substitute, or in some cases replacement, binders. Fly ash, Blast furnace slag and silica fumes are three well known examples of cement replacement materials that are in use today that, like OPC, have been documented and validated both in laboratory tests and in practice. The first is a by-product of coal combustion, the second of iron smelting and the third of electric arc furnace production of elemental silicon or ferro silicon alloys. This paper presents a concise review of the current state-of-the-art and standards underpinning the production and use of OPC-based cements and concretes. It outlines some of the emerging green alternatives and the benefits they offer. Many of these alternatives rely on technological advances that include energy-efficient, low carbon production methods, novel cement formulations, geopolymers, carbon negative cements and novel concrete products. Finally, the economics of cement production and the trends in the UK, US and the Gulf Cooperation Council (GCC) Region are presented, to help guide and inform future developments in cement production based on maximizing the value of carbon reduction.
Adi Munandir, Aurik Gustomo, Prawira Fajarindra Belgiawan
The Fourth Industrial Revolution has largely bypassed traditional, asset-heavy industries like cement manufacturing, which face significant challenges in digitizing operations while managing innovation resistance and multi-generational workforces, particularly in emerging economies. This study investigates how digital resource orchestration can overcome these barriers to create a competitive advantage, employing a single case study design with five-year longitudinal observation and twelve in-depth interviews from Indonesia’s largest cement manufacturer. The research reveals that innovation resistance is not a temporary hurdle but a persistent institutional feature that must be systematically managed. A comprehensive framework is developed, demonstrating that successful transformation requires orchestrating people assets (digital leadership, capability development), process assets (governance, resource mechanisms), and technology assets (infrastructure, integration). The findings show that organizations progress through five maturity levels—from Traditional to Transformed—by applying sequential orchestration states that address specific resistance patterns at each stage. This study contributes to digital transformation theory by reconceptualizing innovation resistance as an organizational capability and provides an empirically grounded model for traditional industries seeking to bridge the digital divide.
Vyron Kampourakis, Christos Smiliotopoulos, Vasileios Gkioulos
et al.
Traditional air gaps in industrial systems are disappearing as IT technologies permeate the OT domain, accelerating the integration of wireless solutions like Wi-Fi. Next-generation Wi-Fi standards (IEEE 802.11ax/be) meet performance demands for industrial use cases, yet their introduction raises significant security concerns. A critical knowledge gap exists regarding the empirical prevalence and security configuration of Wi-Fi in real-world industrial settings. This work addresses this by mining the global crowdsourced WiGLE database to provide a data-driven understanding. We create the first publicly available dataset of 1,087 high-confidence industrial Wi-Fi networks, examining key attributes such as SSID patterns, encryption methods, vendor types, and global distribution. Our findings reveal a growing adoption of Wi-Fi across industrial sectors but underscore alarming security deficiencies, including the continued use of weak or outdated security configurations that directly expose critical infrastructure. This research serves as a pivotal reference point, offering both a unique dataset and practical insights to guide future investigations into wireless security within industrial environments.
Naval Kishore Mehta, Arvind, Himanshu Kumar
et al.
Detecting and interpreting operator actions, engagement, and object interactions in dynamic industrial workflows remains a significant challenge in human-robot collaboration research, especially within complex, real-world environments. Traditional unimodal methods often fall short of capturing the intricacies of these unstructured industrial settings. To address this gap, we present a novel Multimodal Industrial Activity Monitoring (MIAM) dataset that captures realistic assembly and disassembly tasks, facilitating the evaluation of key meta-tasks such as action localization, object interaction, and engagement prediction. The dataset comprises multi-view RGB, depth, and Inertial Measurement Unit (IMU) data collected from 22 sessions, amounting to 290 minutes of untrimmed video, annotated in detail for task performance and operator behavior. Its distinctiveness lies in the integration of multiple data modalities and its emphasis on real-world, untrimmed industrial workflows-key for advancing research in human-robot collaboration and operator monitoring. Additionally, we propose a multimodal network that fuses RGB frames, IMU data, and skeleton sequences to predict engagement levels during industrial tasks. Our approach improves the accuracy of recognizing engagement states, providing a robust solution for monitoring operator performance in dynamic industrial environments. The dataset and code can be accessed from https://github.com/navalkishoremehta95/MIAM/.
Throughout the history from pre-industry 4.0 to post-industry 4.0, cybersecurity at banks has undergone significant changes. Pre-industry 4.0 cyber security at banks relied on individual security methods that were highly manual and had low accuracy. When moving to post-industry 4.0, cybersecurity at banks had a major turning point with security methods that combined different technologies such as Artificial Intelligence (AI), Blockchain, IoT, automating necessary processes and significantly increasing the defence layer for banks. However, along with the development of new technologies, the current challenge of cybersecurity at banks lies in scalability, high costs and resources in both money and time for R&D of defence methods along with the threat of high-tech cybercriminals growing and expanding. This report goes from introducing the importance of cybersecurity at banks, analyzing their management, operational and business objectives, evaluating pre-industry 4.0 technologies used for cybersecurity at banks to assessing post-industry 4.0 technologies focusing on Artificial Intelligence and Blockchain, discussing current policies and practices and ending with discussing key advantages and challenges for 4.0 technologies and recommendations for further developing cybersecurity at banks.
Marcos Kalinowski, Lucas Romao, Ariane Rodrigues
et al.
Lean R&D has been used at PUC-Rio to foster industry-academia collaboration in innovation projects across multiple sectors. This industrial experience paper describes recent experiences and evaluation results from applying Lean R&D in partnership with Petrobras in the oil and gas sector and Americanas in retail. The findings highlight Lean R&D's effectiveness in transforming ideas into meaningful business outcomes. Based on responses from 57 participants - including team members, managers, and sponsors - the assessment indicates that stakeholders find the structured phases of Lean R&D well-suited to innovation projects and endorse the approach. Although acknowledging that successful collaboration relies on various factors, this industrial experience positions Lean R&D as a promising framework for industry-academia projects focused on achieving rapid, impactful results for industry partners.
Zhyldyz Kurzhunbaeva, Ruggero Vigliaturo, Giulia Pia Servetto
et al.
Abstract Asbestos still represents a major public health problem on a global scale. In Central Asia chrysotile is still mined and used, claiming that it is safer with respect to amphibole asbestos within certain concentrations. However, the problem of asbestos exposure in Central Asia and its consequences on human health have been poorly investigated. We analysed, for the first time, samples of raw and wrought material coming from one of the two asbestos-cement industries, currently active, located near the city of Bishkek, the capital of Kyrgyzstan, as well as air samples collected on different sites of Bishkek and Kant and lung tissues taken from the general population during clinical autopsies. Air samples have been analyzed using a scanning electron microscopy (SEM) equipped with energy dispersive spectroscopy (EDS). Heavy air asbestos pollution was detected in Kant (30.2 ff/L), while Bishkek had lower levels. Lung tissue analysis in the general population, carried out using both SEM and transmission electron microscopy (TEM) with EDS, revealed the presence of both chrysotile and amphibole asbestos. Such findings underline that, even in countries where the use of asbestos is allowed based on the presumed pureness of chrysotile used and the lower carcinogenic potential of chrysotile compared to amphibole asbestos, the general population could be exposed also to amphibole asbestos.
The cement industry emits a significant amount of CO2, but utilizes a large volume of waste and by-products from other industries in Japan. However, few studies have comprehensively evaluated both the environmental burden from CO2 emissions and the environmental impact reduction by resource recycling. The environmental burdens often are not allocated to by-products such as blast furnace slag and fly ash, potentially leading to an overestimation of the environmental impacts of cement, especially blended cement. This study aims to assess how environmental burden allocation to by-products affects environmental impact evaluations of related industries and various types of cement. The study focuses on cement production, steelmaking, and coal-fired power generation in Japan, with a further subdivision of steelmaking processes. The environmental burdens related to by-products were allocated to them based on weight and cost. LIME3, a life cycle impact analysis method developed in Japan, was used to calculate the environmental impacts. The results of the environmental impact assessment of Portland cement and blended cement showed that Portland cement has a greater reduction in environmental impacts than blended cement in terms of no allocation, allocation by weight, and allocation by cost.
This research investigates the feasibility of using desert sand as a sustainable substitute for traditional construction materials in Egypt. Utilizing local sand resources in Egypt’s extensive desert landscape could substantially decrease dependence on energy-intensive materials like Portland cement and clay bricks, thereby reducing environmental impact and promoting decarbonization initiatives. This research takes a hierarchical approach to analyzing the geological distribution of distinct Egyptian sand kinds, assessing important national initiatives and legislation, in addition to laboratory findings and concluded mechanical qualities in recent publications. It exposes flaws in Egyptian adopted decarbonization and sustainability programs in concrete and masonry industries. It also investigates construction techniques, including autoclaved aerated concrete (AAC) blocks, which provide environmental and economic advantages compared to traditional clay bricks. Therefore, optimizing the use of sand in the concrete and masonry industries is very suitable for Egypt’s urban growth, as it will enhance decarbonization initiatives, improve thermal insulation, and decrease energy consumption. In accordance to this, the study highlights the necessity for additional experimental validation to incorporate sand-based materials into Egypt’s construction sector, which needs to be supported by policy interventions to standardize and generalize sand-based materials. It concludes with an indicative approach for the integration of the abundant desert sand into the production of Portland Pozzolana Cement (PPC) and the enforcement of AAC block usage for modular based construction through national standards and supply chain optimization.
Engineering (General). Civil engineering (General), City planning
The Industrial Internet of Things (IIoT) has become a critical technology to accelerate the process of digital and intelligent transformation of industries. As the cooperative relationship between smart devices in IIoT becomes more complex, getting deterministic responses of IIoT periodic time-critical computing tasks becomes a crucial and nontrivial problem. However, few current works in cloud/edge/fog computing focus on this problem. This paper is a pioneer to explore the deterministic scheduling and network structural optimization problems for IIoT periodic time-critical computing tasks. We first formulate the two problems and derive theorems to help quickly identify computation and network resource sharing conflicts. Based on this, we propose a deterministic scheduling algorithm, \textit{IIoTBroker}, which realizes deterministic response for each IIoT task by optimizing the fine-grained computation and network resources allocations, and a network optimization algorithm, \textit{IIoTDeployer}, providing a cost-effective structural upgrade solution for existing IIoT networks. Our methods are illustrated to be cost-friendly, scalable, and deterministic response guaranteed with low computation cost from our simulation results.
The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the "fourth industrial revolution", referred to as "Industrie 4.0". Current systems usually rely on wire-line technologies to connect sensors and actuators. To enable a higher flexibility such as moving robots or drones, these connections need to be replaced by wireless technologies in the future. Furthermore, this facilitates the renewal of brownfield deployments to address Industrie 4.0 requirements. This paper proposes representative use cases, which have been examined in the German Tactile Internet 4.0 (TACNET 4.0) research project. In order to analyze these use cases, this paper identifies the main challenges and requirements of communication networks in Industrie 4.0 and discusses the applicability of 5th generation wireless communication systems (5G).
Roman Bögli, Leandro Lerena, Christos Tsigkanos
et al.
TLA+ is a formal specification language used for designing, modeling, documenting, and verifying systems through model checking. Despite significant interest from the research community, knowledge about usage of the TLA+ ecosystem in practice remains scarce. Industry reports suggest that software engineers could benefit from insights, innovations, and solutions to the practical challenges of TLA+. This paper explores this development by conducting a systematic literature review of TLA+'s industrial usage over the past decade. We analyze the trend in industrial application, characterize its use, examine whether its promised benefits resonate with practitioners, and identify challenges that may hinder further adoption.
In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to renew the paradigms in practical applications due to their robust language capabilities and generalization abilities. However, despite their impressive problem-solving skills in many domains, MLLMs' ability in industrial anomaly detection has not been systematically studied. To bridge this gap, we present MMAD, the first-ever full-spectrum MLLMs benchmark in industrial Anomaly Detection. We defined seven key subtasks of MLLMs in industrial inspection and designed a novel pipeline to generate the MMAD dataset with 39,672 questions for 8,366 industrial images. With MMAD, we have conducted a comprehensive, quantitative evaluation of various state-of-the-art MLLMs. The commercial models performed the best, with the average accuracy of GPT-4o models reaching 74.9%. However, this result falls far short of industrial requirements. Our analysis reveals that current MLLMs still have significant room for improvement in answering questions related to industrial anomalies and defects. We further explore two training-free performance enhancement strategies to help models improve in industrial scenarios, highlighting their promising potential for future research.
In this study, an inorganic retarder and a synthesized dispersant (based on PCE) were used to improve the retention and workability of alkali-activated ground granulated blast furnace slag (GGBFS), with NaOH as the sole activator. The objective of the study was to formulate pumpable concrete mixtures with workable time of more than 90 min. The prolonged retention in slump was attained by the addition of the retarder. The effect of the dispersants, synthesized with different monomer to macromonomer ratios, on the workability of the paste was investigated by analyzing the fundamental rheological parameters. The addition of dispersant reduced the initial storage modulus and improved the workability of the alkali-activated paste mixtures. The interaction between the dispersant and NaOH-activated GGBFS systems was investigated by means of adsorption studies and zeta potential measurements. The dispersing ability and the amount adsorbed on GGBFS increased with an increase in the anionic charge of the dispersant. Zeta potential measurements suggested that the dispersion mechanism is primarily due to steric hindrance. Concrete mixtures of compressive strength in the range of ordinary concrete with pumpable workability for 90 and 120 min were achieved with the addition of both retarder and dispersant. The study concludes that a retarder is necessary to prolong the workable times, whereas a dispersant with a higher anionic charge is required to improve the workability of sodium hydroxide-activated GGBFS mixtures.
Syed Muhammad Mahad, Rehan Khan, Michał Wieczorowski
et al.
Slurry erosion presents a critical challenge in hydrocarbon and cement processing industries, as well as in abrasive water jet cutting systems, leading to diminished operational efficiency and elevated maintenance costs. This study investigates the erosive wear behavior of Poly-Lactic Acid (PLA) fabricated with varying infill microtextures—zigzag, concentric, and grid—under diverse pH conditions (2.73, 7.75, and 10.15) using garnet particles as the erodent. The results demonstrate that optimal operational conditions for PLA are achieved with a grid microtexture, a pH of 7.75, and a 325 μm erodent size. Conversely, the most severe wear occurs under a pH of 10.15, a 600 μm erodent size, and a zigzag microtexture. The grid microtexture is the most effective in minimizing erosion, while the zigzag pattern shows a 16.68% increase in wear when compared to the grid microtexture. Additionally, a shift from a slightly basic to a highly acidic environment increases wear by 1%, whereas a transition to a highly basic environment leads to a 32.6% increase in erosion within the grid microtexture. The study highlights the significant contributions of infill microtexture (64%), erodent size (23.7%), and pH value (11%) to the overall erosion rate.
Materials of engineering and construction. Mechanics of materials, Chemical technology
Maryam Mohammadi, Mostafa Karimzadeh, Ahmad Seifi
et al.
Electricity trade has emerged as a crucial element within the global energy market, necessitating strategic optimization of Iran's role within this system. This study endeavors to design a mathematical model for optimizing Iran’s electricity exchanges by analyzing the balance between direct electricity exports and indirect exports, particularly through product groups such as steel and cement. The research employs an applied quantitative design, utilizing supply and demand modeling, and integrating elements of trade network analysis and competitiveness theory. The statistical population encompassed Iran's electricity-intensive export sectors, with key products demonstrating high energy consumption and export relevance selected via a purposive sampling method. The study modeled decision-making scenarios related to electricity production, import, and export structures, employing a mathematical framework rooted in competitiveness indices and trade gravity models. Data analysis was performed using sensitivity analysis and network trade indicators. The results indicate that a moderate increase in electricity production capacity significantly supports enhanced profitability and regional competitiveness, while excessive expansion leads to diminishing returns. Steel was identified as a high-potential long-term export product, whereas cement proved to be a more viable short-term option. The study concludes that reforms in the supply structure and the implementation of targeted export strategies are essential for improving Iran’s electricity trade, noting that increased bargaining power alone will not guarantee higher profitability. The developed model serves as a strategic planning tool to strengthen Iran’s position in both regional and international electricity trade.
Introduction
Electricity is vital to modern infrastructure and economic development, and Iran—endowed with ample energy resources and strong generation capacity—has the potential to lead regional electricity trade. However, rising domestic demand, resource limitations, and regional competition require a strategic reassessment of export and import policies. While earlier studies mostly emphasized direct electricity trade, this study introduces a dynamic multi-objective optimization model that also includes indirect exports through energy-intensive goods like aluminum and steel. The model positions Iran as the central node in a regional trade network, with its electricity flows treated as decision variables and external flows assumed stable. It aims to optimize electricity allocation among domestic use, direct exports, and indirect exports, using criteria such as trade competitiveness, profitability, gravity index, and infrastructure constraints. The study evaluates trade with key partners like Turkey, Iraq, and Pakistan, and supports policymakers in identifying optimal strategies and understanding the economic rationale behind Iran’s electricity trade. The paper provides a structured analysis across five sections, offering practical insights and policy tools.
Methodology
This study develops a decision-making framework to optimize Iran's electricity trade, covering both direct electricity exchanges and indirect trade through electricity-intensive goods. The model uses a multi-objective optimization approach to enhance competitiveness and profitability while reducing costs. It assumes a fixed trade network with constant flows among other countries, treating Iran’s electricity inflows and outflows as decision variables. The mathematical formulation defines key sets for countries (V), electricity-intensive commodities (K), and domestic allocation scenarios (S).
Key Constraints:
Electricity Supply Constraint: This constraint ensures that domestic consumption, direct exports, and indirect trade do not exceed the total electricity capacity
Export Capacity Constraints: Exports cannot exceed the domestic production limits for energy-intensive goods. Demand Constraints: Trade must align with market demand limits. Minimum Electricity Allocation for Industry: To prevent the collapse of energy-intensive sectors, a minimum electricity allocation for substitute commodity exports is ensured, and also Total Trade Volume Calculation: Iran’s total trade accounts for both direct and indirect electricity flows.
Objective Functions:
Maximize Iran’s Trade Competitiveness:
Maximize Profitability:
This methodological framework enables policymakers to optimize Iran’s electricity trade position, balancing domestic electricity allocation, direct exports, and indirect exports.
Results and Discussion
This study introduces a mathematical model for optimizing Iran’s electricity trade by incorporating both direct exports and indirect trade through electricity-intensive goods like steel and cement. Using real-world data, the model identifies effective trade strategies to enhance competitiveness and profitability. Key findings show that steel is a valuable long-term export, while cement offers short-term gains. A moderate (10–15%) increase in domestic electricity production boosts trade performance, but returns diminish beyond that. Political efforts to increase demand are not financially effective under current conditions.
Policy Recommendations
Based on the study's findings, the following policy recommendations are proposed:
Prioritize Energy-Intensive Exports: Focus on industries like steel and cement over direct electricity exports to maximize economic gains.
Control Generation Growth: Keep electricity production increases within 10–15% to avoid diminishing returns.
Support Export-Oriented Plants: Promote private investment in power plants dedicated to exports to balance domestic supply and boost revenue.
Improve Trade Infrastructure: Strengthen legal and technical frameworks to reduce dependence on political negotiations and enhance regional trade efficiency.
Mana Alyami, Kennedy Onyelowe, Ali H. AlAteah
et al.
The growing demand for copper and related materials in various industries is driving increased copper mining globally. This surge presents a substantial challenge in managing and responsibly disposing of large volumes of copper mine tailings (CMT). Incorporating CMT as supplementary cementitious materials (SCMs) in concrete addresses two significant environmental challenges simultaneously: reducing the accumulation of CMT waste in landfills and lowering the carbon footprint by reducing cement usage. The investigation into recycling CMT as a cement substitute involves a thorough assessment of its impact on the compressive strength (CS) of concrete. This research introduces innovative hybrid machine learning (ML) models for estimating the CS of CMT concrete, aiming to streamline strength assessment processes and save valuable resources. The method involves integrating features from large public datasets with the limited available data on the CS of CMT concrete. Support vector regression (SVR) was combined with advanced optimization techniques: firefly algorithm (FFA), grey wolf optimization (GWO) and particle swarm optimization (PSO) to create new hybrid models for forecasting the CS of CMT concrete. Additionally, traditional ML techniques like decision tree (DT) and random forest (RF) were used to compare with these SVR-based hybrids. All three hybrid models demonstrated strong performance, with SVR-FFA emerging as the most effective among them. Notably, SVR-FFA achieved the greatest R² score of 0.96, indicating superior predictive accuracy compared to SVR-PSO (0.92) and SVR-GWO (0.90). Additionally, the DT model attained an R² score of 0.88, while the RF model achieved an R² score of 0.84. Moreover, the SHapley additive exPlanations (SHAP) and partial dependence plots (PDP) analyses underscore the positive effects of curing age, cement, blast furnace slag, and superplasticizer on the CS of CMT concrete. A graphical user interface was developed for predicting the CS of CMT concrete, allowing for instant predictions without the need for conducting experiments.
Materials of engineering and construction. Mechanics of materials