Hasil untuk "Cement industries"

Joel M Thomas, Abhijit Chakraborty

This study investigates the economic complexity of Indian states by constructing a state-industry bipartite network using firm-level data on registered companies and their paid-up capital. We compute the Economic Complexity Index and apply the fitness-complexity algorithm to quantify the diversity and sophistication of productive capabilities across the Indian states and two union territories. The results reveal substantial heterogeneity in regional capability structures, with states such as Maharashtra, Karnataka, and Delhi exhibiting consistently high complexity, while others remain concentrated in ubiquitous, low-value industries. The analysis also shows a strong positive relationship between complexity metrics and per-capita Gross State Domestic Product, underscoring the role of capability accumulation in shaping economic performance. Additionally, the number of active firms in India demonstrates a persistent exponential growth at an annual rate of 11.2%, reflecting ongoing formalization and industrial expansion. The ordered binary matrix displays the characteristic triangular structure observed in complexity studies, validating the applicability of complexity frameworks at the sub-national level. This work highlights the usefulness of firm-based data for assessing regional productive structures and emphasizes the importance of capability-oriented strategies for fostering balanced and sustainable development across Indian states. By demonstrating the usefulness of firm registry data in data constrained environments, this study advances the empirical application of economic complexity methods and provides a quantitative foundation for capability-oriented industrial and regional policy in India.

Puyu Zeng, Zhaoxi Wang, Zhixu Duan et al.

Code generation and comprehension by Large Language Models (LLMs) have emerged as core drivers of industrial intelligence and decision optimization, finding widespread application in fields such as finance, automation, and aerospace. Although recent advancements have demonstrated the remarkable potential of LLMs in general code generation, existing benchmarks are mainly confined to single domains and languages. Consequently, they fail to effectively evaluate the generalization capabilities required for real-world industrial applications or to reflect the coding proficiency demanded by complex industrial scenarios. To bridge this gap, we introduce IndustryCode, the first comprehensive benchmark designed to span multiple industrial domains and programming languages. IndustryCode comprises 579 sub-problems derived from 125 primary industrial challenges, accompanied by rigorous problem descriptions and test cases. It covers a wide range of fields, including finance, automation, aerospace, and remote sensing-and incorporates diverse programming languages such as MATLAB, Python, C++, and Stata. In our evaluation, the top-performing model, Claude 4.5 Opus, achieved an overall accuracy of 68.1% on sub-problems and 42.5% main problems. The benchmark dataset and automated evaluation code will be made publicly available upon acceptance.

Tejaswini Bollikonda

Large Language Models (LLMs) are increasingly deployed in sensitive domains such as healthcare, finance, and law, yet their integration raises pressing concerns around trust, accountability, and reliability. This paper explores adaptive trust metrics for multi LLM ecosystems, proposing a framework for quantifying and improving model reliability under regulated constraints. By analyzing system behaviors, evaluating uncertainty across multiple LLMs, and implementing dynamic monitoring pipelines, the study demonstrates practical pathways for operational trustworthiness. Case studies from financial compliance and healthcare diagnostics illustrate the applicability of adaptive trust metrics in real world settings. The findings position adaptive trust measurement as a foundational enabler for safe and scalable AI adoption in regulated industries.

Adrián Fagundo-Mollineda, Yolanda Freile-Pelegrín, Román M. Vásquez-Elizondo et al.

The massive influx of pelagic <i>Sargassum</i> in the Caribbean poses a serious environmental, social, and economic problem, as the stranded biomass is often treated as waste and deposited in landfills. This literature review synthesizes recent research highlighting its potential for valorization in various industries, turning this challenge into an opportunity. <i>Sargassum</i> has low levels of protein and lipids. Still, it is particularly rich in carbohydrates, such as alginates, fucoidans, mannitol, and cellulose, as well as secondary metabolites, including phenolic compounds, flavonoids, pigments, and phytosterols with antioxidant and bioactive properties. These biochemical characteristics allow for its application in renewable energy (bioethanol, biogas, biodiesel, and combustion), agriculture (fertilizers and biostimulants), construction (composite materials, cement additives, and insulation), bioremediation (adsorption of heavy metals and dyes), and in the health sector (antioxidants, anti-inflammatories, and pharmacological uses). A major limitation is its high bioaccumulation capacity for heavy metals, particularly arsenic, which increases environmental and health risks and limits its direct use in food and feed. Therefore, innovative pretreatment and bioprocessing are essential to mitigate these risks. The most promising approach for its utilization is a biorefinery model, which allows for the sequential extraction of multiple high-value compounds and energy products to maximize benefits, reduce costs, and sustainably transform <i>Sargassum</i> from a coastal pest into a valuable industrial resource.

Chaoran Zhang, Chenhao Zhang, Zhaobo Xu et al.

The combination of embodied intelligence and robots has great prospects and is becoming increasingly common. In order to work more efficiently, accurately, reliably, and safely in industrial scenarios, robots should have at least general knowledge, working-environment knowledge, and operating-object knowledge. These pose significant challenges to existing embodied intelligent robotics (EIR) techniques. Thus, this paper first briefly reviews the history of industrial robotics and analyzes the limitations of mainstream EIR frameworks. Then, a new knowledge-driven technical framework of embodied intelligent industrial robotics (EIIR) is proposed for various industrial environments. It has five modules: a world model, a high-level task planner, a low-level skill controller, a simulator, and a physical system. The development of techniques related to each module are also thoroughly reviewed, and recent progress regarding their adaption to industrial applications are discussed. A case study of real-world assembly system is given to demonstrate the newly proposed EIIR framework's applicability and potentiality. Finally, the key challenges that EIIR encounters in industrial scenarios are summarized and future research directions are suggested. The authors believe that EIIR technology is shaping the next generation of industrial robotics and EIIR-based industrial systems supply a new technological paradigm for intelligent manufacturing. It is expected that this review could serve as a valuable reference for scholars and engineers that are interested in industrial embodied intelligence. Together, scholars can use this research to drive their rapid advancement and application of EIIR techniques. The authors would continue to track and contribute new studies in the project page https://github.com/jackyzengl/EIIR

Ana Bergmann, Klaartje de Weerdt, Maxime Ranger et al.

This study investigates the effects of replacing Portland cement (PC) with metakaolin (MK) on the pore solution composition of cementitious binders. Using the empirical Taylor model and a thermodynamic model (GEMS), the required replacement levels of PC by MK are computed to achieve different threshold alkali metal concentrations. GEMS predicts that similar amounts of MK (15–20 %) are required regardless of w/b-ratio and PC alkali content, which does not match with the experimental evidence. The Taylor model captures better the effect of these variables, predicting replacement levels ranging from 0 to 40 %. Results are visualized through contour and 3D plots, highlighting the complex interactions and effects of SCMs on concrete durability.

Mengxin Bu, Biqin Dong, Muhammad Riaz Ahmad et al.

Clarifying the dissolution behaviour and mechanism of fly ash in acid activators is essential to understand the properties of fly ash-based silico-aluminophosphate geopolymer. This paper investigated the in-situ dissolution behaviour of fly ash (FA) in aluminium dihydrogen phosphate (MAP), phosphoric acid (PA), citric acid (CA), oxalic acid (OA), and tartaric acid (TA) using optical microscopy and electron probe microscopic analysis (EPMA). The phase and elemental changes before and after dissolution were further investigated using quantitative X-ray Diffraction (QXRD) and 2D-Fourier transform infrared (FTIR) spectrometry. In addition, the changes in dissolved elements were elucidated from a liquid phase perspective. The results showed that the capacity of each acid to dissolve FA was CA>MAP>PA>TA>OA. Ca-containing phases in FA were preferentially dissolved in all acids. The main contributor to FA dissolution in acid was the amorphous phase, and the SiOSi bond in quartz was more sensitive than other chemical bonds to acid. When FA was dissolved in OA and TA, new crystalline phases—calcium oxalate and calcium citrate—formed on the FA surface, inhibiting further dissolution.

Mengru Li, Zhenya Zhang, Xianyi Zeng et al.

Abstract The construction industry is crucial for economic and social development. Inorganic materials, which rely on natural minerals and are affected by uncertainties, hold a large share in the construction market. As building materials are from process—intensive industries, complex and continuous processing magnifies deviations, directly affecting product quality. Computational intelligent methods are effective for accurately predicting product quality. This paper focuses on inorganic building materials and systematically reviews computational intelligent techniques in this field. It comprehensively explores 234 related studies in 6 key areas (concrete, ceramics, glass, clay bricks, cement, and steel), compiles prediction models, evaluates them, analyzes model configurations and properties to gain insights into the field and identify optimal approaches. It points out model limitations, such as high computational costs, data-hungry, and suggests future research directions like practicality and promoting green initiatives through material circulation.

Md Hadisur Rahman, Md Rabiul Hasan, Nahian Ismail Chowdhury et al.

In an era of rapid technological advancement, the rise of Industry 4.0 has prompted industries to pursue innovative improvements in their processes. As we advance towards Industry 5.0, which focuses more on collaboration between humans and intelligent systems, there is a growing requirement for better sensing technologies for healthcare and safety purposes. Consequently, Motion Capture (MoCap) systems have emerged as critical enablers in this technological evolution by providing unmatched precision and versatility in various workplaces, including construction. As the construction workplace requires physically demanding tasks, leading to work-related musculoskeletal disorders (WMSDs) and health issues, the study explores the increasing relevance of MoCap systems within the concept of Industry 4.0 and 5.0. Despite the growing significance, there needs to be more comprehensive research, a scientometric review that quantitatively assesses the role of MoCap systems in construction. Our study combines bibliometric, scientometric, and systematic review approaches to address this gap, analyzing articles sourced from the Scopus database. A total of 52 papers were carefully selected from a pool of 962 papers for a quantitative study using a scientometric approach and a qualitative, indepth examination. Results showed that MoCap systems are employed to improve worker health and safety and reduce occupational hazards.The in-depth study also finds the most tested construction tasks are masonry, lifting, training, and climbing, with a clear preference for markerless systems.

Gleb Bazhenov, Oleg Platonov, Liudmila Prokhorenkova

Although data that can be naturally represented as graphs is widespread in real-world applications across diverse industries, popular graph ML benchmarks for node property prediction only cover a surprisingly narrow set of data domains, and graph neural networks (GNNs) are often evaluated on just a few academic citation networks. This issue is particularly pressing in light of the recent growing interest in designing graph foundation models. These models are supposed to be able to transfer to diverse graph datasets from different domains, and yet the proposed graph foundation models are often evaluated on a very limited set of datasets from narrow applications. To alleviate this issue, we introduce GraphLand: a benchmark of 14 diverse graph datasets for node property prediction from a range of different industrial applications. GraphLand allows evaluating graph ML models on a wide range of graphs with diverse sizes, structural characteristics, and feature sets, all in a unified setting. Further, GraphLand allows investigating such previously underexplored research questions as how realistic temporal distributional shifts under transductive and inductive settings influence graph ML model performance. To mimic realistic industrial settings, we use GraphLand to compare GNNs with gradient-boosted decision trees (GBDT) models that are popular in industrial applications and show that GBDTs provided with additional graph-based input features can sometimes be very strong baselines. Further, we evaluate currently available general-purpose graph foundation models and find that they fail to produce competitive results on our proposed datasets.

Suguru Otani

I investigate how explicit cartels, known as ``shipping conferences", in a global container shipping market facilitated the formation of one of the largest globally integrated markets through entry, exit, and shipbuilding investment of shipping firms. Using a novel data, I develop and construct a structural model and find that the cartels shifted shipping prices by 20-50\% and encouraged firms' entry and investment. In the counterfactual, I find that cartels would increase producer surplus while slightly decreasing consumer surplus, then may increase social welfare by encouraging firms' entry and shipbuilding investment. This would validate industry policies controlling prices and quantities in the early stage of the new industry, which may not be always harmful. Investigating hypothetical allocation rules supporting large or small firms, I find that the actual rule based on tonnage shares is the best to maximize social welfare.

Peter A. V. Gade, Trygve Skjøtskift, Henrik W. Bindner et al.

Energy-intensive industries can adapt to help balance the power grid. By using a real-world case study of a zinc galvanizing process in Denmark, we show how a modest investment in power control of the furnace enables the provision of various ancillary services. We consider two types of services, namely frequency containment reserve (FCR) and manual frequency restoration reserve (mFRR), and numerically conclude that the monetary value of both services is significant, such that the pay-back time of investment is potentially within a year. The FCR service provision is more preferable as its impact on the temperature of the zinc is negligible.

Matthew Munro, Fragkoulis Kanavaris, John Orr

Concrete and cement production account for approximately 9–10 % of global carbon dioxide emissions. Schemes are under development to establish shared definitions and rules for classifying and communicating the cradle-to-gate embodied carbon (EC) of concrete, which can be used as a basis for policies to decarbonise these industries. This paper critically reviews eight recently developed EC classification schemes for concrete, examining their specifics, limitations, and the potential for convergence upon a single, globally-applicable scheme. Important differences exist between them, regarding a) whether rating bands should remain static or be periodically updated to reflect the EC of concrete currently available in the market and b) whether baseline EC values should be established for each strength class, and/or based on other factors like design application, region, or production route (ready-mix versus precast). A recently proposed classification scheme for the UK addresses both issues by combining static rating bands applicable to all normal-weight concrete, with up-to-date industry data, thus communicating the EC of commercially available concrete. Industry EC data can then be segmented for variations in concrete production and use, enabling better like-with-like comparisons between concrete products considered for projects. Convergence upon a single, globally applicable classification scheme is proposed as it would facilitate easier comparisons between concrete products across different regions. This paper argues that achieving this goal is feasible provided the scheme establishes a shared LCA methodology, and variations of the scheme are developed to accommodate differences in concrete strength and carbon intensity measurement units typically used in different regions.

Jiatong Han, Qing Sun, Yanbo Jiang

In this paper, we select the China Carbon Market Price Index, which reflects the overall price changes in China’s carbon market (CCM), and employs the TVP-VAR-BK model to examine the risk spillover effects between the carbon market and high-carbon-emission industries in China from a frequency domain viewpoint. Employing the nonparametric quantile Granger causality test, it delves further into the effects of economic policy uncertainty (EPU) in China on the degree of risk spillovers between the carbon market and high-carbon-emission industries. There are significant risk spillover effects between the carbon market and high-carbon-emission industries. During the short term, the carbon market affects the cement industry more than the electric power and steel industries. However, the carbon market is affected by the volatility of the high-carbon-emission industries over the long term. In addition, the effect of EPU on the magnitude of risk spillovers between the carbon market and high-carbon-emission industries is nonsignificant at extreme quartiles and significant at the middle quartile level, which is typically asymmetric.

Jha Sushant Kumar, Kumar Saurabh

Concrete technology is widely favoured in construction for its affordability, performance, and effectiveness. However, there's a growing need for it to be more durable, energy-efficient, and environmentally friendly. Achieving these goals involves incorporating or substituting energy-efficient materials from industries, such as bottom ash, fly ash, and steel slag. Additionally, recyclable materials like Sugarcane Bagasse Ash (SBA) and Metakaolin (MK) can be utilized in concrete production. The combined effects of MK and SBA on concrete's mechanical properties were investigated through various destructive and non-destructive tests, including compressive strength, splitting tensile strength, flexural strength, Ultrasonic Pulse Velocity (UPV), and Digital Schmidt Rebound Hammer (RH) testing. Different ratios of MK and SBA, ranging from 5% to 20%, were used to partially replace cement by mass in the design of M-35 grade concrete, while M-sand replaced fine aggregate entirely by volume. Experimental results suggest that concrete incorporating industrial waste materials like SBA and MK outperforms conventional concrete, as evidenced by both non-destructive and destructive testing methods.

Jaures Syntyche Ndzila, Shuxin Liu, Xiaoli Wang et al.

The attractive properties of ye'elimite (C4A3$) are receiving particular attention in cement and repairing materials. The present paper aims to further explore the difference in hydration performance of different C4A3$ phases (o-C4A3$ and c-C4A3$) using the Ga-doping method. On this basis, C4A3-xGx$ specimens (noted as C4A3$, C4A2.3G0.7$, and C4A1.6G1.4$) were first synthesized and their hydration characteristics were systematically studied. Results showed that Ga-doping enhanced the hydration activity of ye'elimite. With increasing Ga3+ ions addition, the intensity of the initial peak gradually increased, but the duration of the induction period and the hydration heat development rate were gradually reduced. The conductivity and pore solution analysis also demonstrated a higher ion concentration precipitation at the early stage for cubic C4A1.6G1.4$ compared to pure C4A3$. The main hydration products of these specimens were AFt, AFm, and AH3 (gel). Moreover, the addition of Ga3+ ions improved the crystallinity of AFt and AFm with recorded a relatively higher decomposition temperature. Overall, this study demonstrated that the addition of Ga3+ ions can regulate the hydration characteristics of ye'elimite.

Mays Mahmoud Alsaed, Rafal Latif Al Mufti

Concrete is one of the most commonly used materials in civil engineering construction, and it continues to have increased production. This puts pressure on the consumption of its constituent materials, including Portland cement and aggregates. There are environmental consequences related to the increased emission of CO₂ that are associated with the production process of Portland cement. This has led to the development and use of alternative cementitious materials, mainly in the form of condensed silica fume, pulverised fuel ash, and ground granulated blast furnace slag. All of these are by-products of the silicon, electrical power generation, and iron production industries, respectively. In recent years, attention has turned to the possible use of sustainable bio-waste materials that might contribute to the replacement of Portland cement in concrete. This research investigates the effects of using rice husk ash as cement replacement material on the 1 to 28-day concrete properties, including the compressive strength, workability, and durability of concrete. The findings indicate that including rice husk ash in concrete can improve its strength at 3–28 days for percentage replacements of 5% to 20% (ranging from 2.4% to 18.7% increase) and improvements from 1 day for 20% replacement (with 11.1% increase). Any percentage replacement with rice husk ash also reduced the air permeability by 21.4% and therefore improved the durability, while there was a small reduction in the workability with increased replacement.

Uchenna D Ani, Jeremy M Watson, Nilufer Tuptuk et al.

This study explores the state-of-the-art, application, and maturity of socio-technical security models for industries and sectors dependent on CI and investigates the gap between academic research and industry practices concerning the modelling of both the social and technical aspects of security. Systematic study and critical analysis of literature show that a steady and growing on socio-technical security M&S approaches is emerging, possibly prompted by the growing recognition that digital systems and workplaces do not only comprise technologies, but also social (human) and sometimes physical elements.

Ayush Singh Rajput, Richa Gupta

The extension of legacy business process automation beyond the bounds of specific processes is known as hyperautomation. Hyperautomation provides automation for nearly any repetitive action performed by business users by combining AI tools with RPA. It automates complex IT business processes that a company's top brains might not be able to complete. This is an end-to-end automation of a standard business process deployment. It enables automation to perform task digitalization by combining a brain computer interface (BCI) with AI and RPA automation tools. BCI, in conjunction with automation tools, will advance the detection and generation of automation processes to the next level. It allows enterprises to combine business intelligence systems, address complex requirements, and enhance human expertise and automation experience. Hyperautomation and its importance in today's environment are briefly discussed in this paper. The article then goes on to discuss how BCI and sensors might aid Hyperautomation. The specific sectors of solicitations were examined using a variety of flexible technologies associated to this concept, as well as dedicated workflow techniques, which are also diagrammatically illustrated. Hyperautomation is being utilized to improve the efficiency, accuracy, and human enhancement of automated tasks dramatically. It incorporates a number of automated tools in its discovery, implementation, and automation phases. As a result, it's well-suited to integrating cutting-edge technologies and experimenting with new methods of working. Keywords- Hyperautomation, Brain computer Interface (BCI), Technology, Used case, Sensors, Industries.

Victor Brial, Hang Tran, Luca Sorelli et al.

Treating SPL by the low caustic leaching and liming process generates an inert nonhazardous residue called LCLL Ash and a fluorite byproduct Calcined LCLL Ash that is ground into a fine powder demonstrates pozzolanic behavior in cement. The effect of the calcination temperature and fluorite byproduct addition on the reactivity of LCLL Ash was studied by the compressive strength activity index, Frattini test and Rilem R3 tests followed by XRD analysis. At 800°C, the formation of nepheline causes alkali uptake, the LCLL Ash showed a slightly lower reactivity with 10% fluorite addition. At 1000°C, calcined LCLL Ash/CF showed a better amorphization of phases and increasing reactivity due to reactions between fluorite and sodium oxide. Unlike LCLL Ash, no delay in hydration or hydro reactivity was observed with calcined LCLL Ash/CF.