Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas
et al.
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.
Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.
Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.
Artificial intelligence (AI) has undergone transformative advances since 2022, particularly through generative AI, large language models (LLMs), and diffusion models, fundamentally reshaping the creative industries. However, existing reviews have not comprehensively addressed these recent breakthroughs and their integrated impact across the creative production pipeline. This paper addresses this gap by providing a systematic review of AI technologies that have emerged or matured since our 2022 review, examining their applications across content creation, information analysis, post-production enhancement, compression, and quality assessment. We document how transformers, LLMs, diffusion models, and implicit neural representations have established new capabilities in text-to-image/video generation, real-time 3D reconstruction, and unified multi-task frameworks-shifting AI from support tool to core creative technology. Beyond technological advances, we analyze the trend toward unified AI frameworks that integrate multiple creative tasks, replacing task-specific solutions. We critically examine the evolving role of human-AI collaboration, where human oversight remains essential for creative direction and mitigating AI hallucinations. Finally, we identify emerging challenges including copyright concerns, bias mitigation, computational demands, and the need for robust regulatory frameworks. This review provides researchers and practitioners with a comprehensive understanding of current AI capabilities, limitations, and future trajectories in creative applications.
Mehdi Mohammad pour, Majid Zanjirdar, Peyman Ghafari Ashtiani
The expansion of communications between active industries and companies in different industry groups on the Tehran Stock Exchange has caused that, in the event of volatility in an industry index, this volatility can spread like a domino to other industry groups and also to other economic sectors, creating systemic risk. Therefore, it is necessary to identify the index of volatile industries, calculate and evaluate the contribution of each of them to the occurrence of systemic risk, the amount of spillover, and the amount of their influence and impact on each other. The purpose of this research is to prioritize the volatility of time series data of 30 industry indices of Tehran Stock Exchange, from 2008 to 2024 using 6 entropy methods, calculate the systemic risk of the growth of each industry index using the conditional value at risk measure ΔCoVaR, and also evaluate the amount of volatility spillover using the TVP-VAR auto-regressive model to predict and prevent the destructive effects of volatility. The research findings show: The highest volatility is related to 8 indices: other mines, communication equipment, agriculture, leather products, coal, petroleum products, chemicals and cement. Also, the highest contagion is to companies active in the coal industry. In addition, the chemical and cement industries can begin to be a systemic risk to the Iranian capital market. Also, a net examination of the spillover effect shows that the growth of the chemical, cement, and communication equipment industries is injecting spillovers into other industries.
This study analyzes the EU Carbon Border Adjustment Mechanism (CBAM)’s implications for China’s trade, GDP, and carbon emissions under evolving global climate governance frameworks. Combining climate club theory with recent policies from the U.S., U.K., Japan, and Canada, it proposes a potential multi-climate club coexistence model. Using the GTAP-E model, the research quantifies CBAM’s effects under two scenarios: a single EU-led climate club and a multi-club system. Key findings reveal that while China’s short-term export reductions to the EU remain marginal (<1% across industries), long-term trade diversion intensifies, with 30% of cement exports projected to shift to non-EU markets by 2034. Multi-club cooperation exacerbates GDP growth challenges for China compared to a single-club scenario but amplifies carbon reduction incentives, particularly if CBAM expands to cover indirect emissions. High-energy sectors (e.g., cement, chemicals) emerge as most vulnerable. The study underscores CBAM’s dual role as both a trade barrier and a catalyst for industrial decarbonization. Recommendations emphasize strengthening China’s carbon pricing mechanisms, proactive engagement in multilateral climate negotiations, and targeted support for energy-intensive industries. These insights highlight the urgency of adaptive strategies to reconcile economic resilience with climate obligations amid fragmented global governance.
In industry, the networking and automation of machines through the Internet of Things (IoT) continues to increase, leading to greater digitalization of production processes. Traditionally, business and production processes are controlled, optimized and monitored using business process management methods that require process discovery. However, these methods cannot be fully applied to industrial production processes. Nevertheless, processes in the industry must also be monitored and discovered for this purpose. The aim of this paper is to develop an approach for process discovery methods and to adapt existing process discovery methods for application to industrial processes. The adaptations of classic discovery methods are presented as universally applicable guidelines specifically for the Industrial Internet of Things (IIoT). In order to create an optimal process model based on process evaluation, different methods are combined into a standardized discovery approach that is both efficient and cost-effective.
Javier Penuela, Cecile Ben, Stepan Boldyrev
et al.
Demand response (DR) programs currently cover about 2\% of the average annual global demand, which is far from contributing to the International Energy Agency's ``Net Zero by 2050'' roadmap's 20\% target. While aggregation of many small flexible loads such as individual households can help reaching this target, increasing the participation of industries that are major electricity consumers is certainly a way forward. The indoor agriculture sector currently experiences a significant growth to partake in the sustainable production of high-quality food world-wide. As energy-related costs, up to 40\% of the total expenses, may preclude full maturity of this industry, DR participation can result in a win-win situation. Indeed, the agriculture system must transform and become a sustainable source of food for an increasing number of people worldwide under the constraints of preservation of soils and water, carbon footprint, and energy efficiency. We considered the case of the Russian Federation where indoor farming is burgeoning and already represents a load of several thousand megawatts. To show the viability of the indoor farming industry participation in implicit and explicit DR programs, we built a physical model of a vertical farm inside a phytotron with complete control of environmental parameters including ambient temperature, relative humidity, CO$_2$ concentration, and photosynthetic photon flux density. This phytotron was used as a model greenhouse. We grew different varieties of leafy plants under simulated DR conditions and control conditions on the same setup. Our results show that the indoor farming dedicated to greens can participate in DR without adversely affecting plant production and that this presents an economic advantage.
Decarbonising the industrial sector is vital to reach net zero targets. The deployment of industrial decarbonisation technologies is expected to increase industrial electricity demand in many countries and this may require upgrades to the existing electricity network or new network investment. While the infrastructure requirements to support the introduction of new fuels and technologies in industry, such as hydrogen and carbon capture, utilisation and storage are often discussed, the need for investment to increase the capacity of the electricity network to meet increasing industrial electricity demands is often overlooked in the literature. This paper addresses this gap by quantifying the requirements for additional electricity network capacity to support the decarbonisation of industrial sectors across Great Britain (GB). The Net Zero Industrial Pathways model is used to predict the future electricity demand from industrial sites to 2050 which is then compared spatially to the available headroom across the distribution network in GB. The results show that network headroom is sufficient to meet extra capacity demands from industrial sites over the period to 2030 in nearly all GB regions and network scenarios. However, as electricity demand rises due to increased electrification across all sectors and industrial decarbonisation accelerates towards 2050, the network will need significant new capacity (71 GW + by 2050) particularly in the central, south, and north-west regions of England, and Wales. Without solving these network constraints, around 65% of industrial sites that are large point sources of emissions would be constrained in terms of electric capacity by 2040. These sites are responsible for 69% of industrial point source emissions.
As Machine Learning (ML) becomes more prevalent in Industry 4.0, there is a growing need to understand how systematic approaches to bringing ML into production can be practically implemented in industrial environments. Here, MLOps comes into play. MLOps refers to the processes, tools, and organizational structures used to develop, test, deploy, and manage ML models reliably and efficiently. However, there is currently a lack of information on the practical implementation of MLOps in industrial enterprises. To address this issue, we conducted a multiple case study on MLOps in three large companies with dedicated MLOps teams, using established tools and well-defined model deployment processes in the Industry 4.0 environment. This study describes four of the companies' Industry 4.0 scenarios and provides relevant insights into their implementation and the challenges they faced in numerous projects. Further, we discuss MLOps processes, procedures, technologies, as well as contextual variations among companies.
Amulie Jarjusey, Kimitoshi Hayano, Alula Araya Kassa
et al.
This study proposes a novel method for managing surplus soil in urban construction projects by investigating the use of banana leaf powder (BLP) as an eco-friendly and efficient mud-soil stabilizer. This study is pioneering in its focus on the use of an agricultural waste product, BLP, as an alternative to conventional methods that frequently rely on cement or lime, which pose environmental concerns owing to their high alkalinity and carbon emissions. BLP, derived from dried and pulverized banana leaves, traditionally used in various industries, was evaluated for its suitability as a stabilizer against orange peel biopolymer (OBP) and fly ash (FA). The findings of this study are groundbreaking. BLP's water absorption capacity, Wab, although lower than that of OBP, was significantly higher than that of FA, demonstrating its potential as a mud soil stabilizer. BLP increased the compaction and strength of the treated clay, whereas OBP made it more challenging to compact the clay owing to gelatinization. The ease of compaction with BLP is attributed to the absorption of free water into the pores of the particles. These results suggest that differences in the stabilizer's water absorption mechanism affect the effectiveness of the post-treatment compaction process on the treated soil. An analysis of the impact of water absorption capacity on the cone index, qc, revealed a material-independent relationship between the parameter β (=Wab × A, where A denotes stabilizer addition content) and qc for BLP and FA. This is because, in contrast to OBP, BLP and FA did not gelatinize the free water that was not absorbed by the stabilizer and remained in the same form within the treated clay. This indicates that understanding the water absorption capacity and mechanism of organic- or inorganic-based stabilizers is important for predicting the strength of treated soil. The same relationship between β and qc holds true for the hybrid-treated clay using both BLP and FA, with β considered to be the sum of β for both BLP and FA. Furthermore, this study innovatively addresses the concern of BLP decay in treated soil by creating a hybrid stabilizer with FA and conducting experiments to accelerate BLP decay using fungal mycelia. These experiments revealed that the hybrid-treated clay exhibited a more gradual decrease in carbon content and stabilized pH levels, implying that FA could inhibit decay by fungal mycelia, improving the BLP-treated soil's long-term durability. These findings suggest that agricultural wastes with high organic content can be effectively used for soil stabilization and ground improvement in civil engineering construction and maintenance projects by combining inorganic materials and taking advantage of their properties, such as high water absorbency. This can address the issues (such as high alkalinity and high carbon dioxide emissions) associated with cement- and lime-based stabilizers, which were previously widely used.
Corrosion of steel strand embedded in deficient grout has been associated with elevated concentrations of sulfate ions stemming from grout segregation and the adverse influences of excess mix water and grout prehydration. There have been discussions about appropriate ways to assess sulfate ion levels in the grout pore water. Various test methodologies can include varying material conditioning procedures, including heating, drying, and chemical reactions that can influence the level of sulfate ion aggregation in the test leachate from the initial bleed water from the bulk material. In this study, the sulfate content was measured by leaching and alternative methods such as XRF and bleed water testing. Six leaching methods were employed to assess the effect of leaching heating, heating time, leaching volume, grout sample mass, and drying temperature. Leaching of larger grout sample mass can yield higher leachate sulfate concentrations, but the concentrations were not commensurate with the larger grout mass. Leaching of a larger grout sample mass with a mass-to-water ratio of 1:10 was not shown to be efficient in the dissolution of sulfate ions. Larger mass-to-water ratio (1:40) yielded higher sulfate concentrations in the leachate and normalized grout mass. Pre-drying of grout samples to 100 °C for 24 h was shown to incur losses in sulfate content. Recommendations of test methods to assess the sulfate ion content from segregated and hardened grout were made.
Avinash Dass, Amit Kumar Mishra, Gustavo André de Araújo Santos
et al.
Carbon dioxide (CO2) in the Earth's atmosphere is a significant greenhouse gas and plays a pivotal role in shaping the carbon dynamics of specific regions. Here, we have examined the spatio-temporal variations of atmospheric CO2 in the Bihar region of India and provided critical information regarding climate change mitigation. NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite retrieved dry air column average atmospheric CO2 concentrations (XCO2) datasets (2015-2021) are used to analyse hot/cold spots, anomalies, hot/cold moments, and their relationships with Bihar's climate and vegetation indices. The highest CO2 concentration (416 ± 1.5 ppm, hot moments) is found in April and May (summer season) and the lowest concentration (406 ± 1.6 ppm, cold moments) is seen in monsoon season. The results reveal that seasonal variations of XCO2 are instrumental in comprehending Bihar's annual carbon dynamics, impacting factors such as plant growth and crop yields. Anomalies and hotspots analyses identify Kaimur, Munger, and Paschim Champaran as significant hotspots, which are house of major industries, power stations, cement factories and mining sites. Moreover, the study shows significant negative correlations (p < 0.001) between XCO2 and various parameters, including Sun Induced Chlorophyll Fluorescence (SIF) at wavelengths 740 and 757, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and relative humidity. Particularly, NDVI and EVI changes explain XCO2 variability by 26 % (p < 0.001) and 24 % (p < 0.001), respectively. Additionally, relative humidity accounted for 37 % (p < 0.001) of the variance in XCO2. Our results indicate that the socio-economic condition of the study area has not been changed significantly during the study period. Further, the increase in afforestation activities has counterbalanced the small increase in CO2 due to developmental process over limited locations. These findings contribute significantly to our understanding of regional carbon dynamics of Bihar and their implications for climate change mitigation efforts.
Abdelmageed Atef Abdelmageed Shehata, Alex Otieno Owino, Md. Yachin Islam
et al.
Abstract In the global construction industry, areas characterized by weak and expansive soils are on the rise, necessitating effective solutions for strength enhancement. Addressing this concern, sustainable soil amendments have gained attention, with rice husk ash (RHA) from rice milling industries being a notable focus. Our experimental study aimed to assess the shear strength of this innovative construction material, introducing a unique approach that considers subgrade layers with minimal cement dosage, including upper, bottom, and double layers a novel contribution yet unexplored in existing literature. In addition to conventional mechanical testing, we employed SEM (Scanning Electron Microscopy) and EDS (Energy-Dispersive X-ray Spectroscopy) analyses to comprehensively explore the treated soils' microstructural and elemental composition aspects. Examining sixteen specimen combinations of weak expansive soil-RHA-cement, varying proportions of RHA (2%, 4%, 6%) and cement (2%, 4%, 6%) were mixed to understand their effects on shear strength parameters. Our findings revealed significant shear strength improvement in each subgrade layer, with specimen 6%RHA6%C in the lower subgrade layer exhibiting the highest cohesive strength at 143 kN/m2. Notably, the double layer configuration, specimen 2%RHA6%C, achieved maximum deviatoric stresses of 383 kN/m2. This novel construction material contributes to effective waste management and presents an innovative engineering solution for sustainable ground improvement, offering promising prospects for future geotechnical advancements.
Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more. RSs are indeed key to the success of many popular APPs, such as YouTube, Tik Tok, Xiaohongshu, Bilibili, and others. This paper explores the methodology for improving modern industrial RSs. It is written for experienced RS engineers who are diligently working to improve their key performance indicators, such as retention and duration. The experiences shared in this paper have been tested in some real industrial RSs and are likely to be generalized to other RSs as well. Most contents in this paper are industry experience without publicly available references.
Vinzenz Klapper, Giovanni Luzi, Benedict Prah
et al.
Cyclones are devices used in various industries to remove particulate matter from gases and liquids. Commonly used in the power generation, cement, and mining industries, cyclones improve the efficiency and longevity of equipment by removing dust and other small particles that can cause wear and damage. Among centrifugal separation, reverse-flow cyclones are primarily used for particle separation, which can reach heights of several meters on an industrial scale and therefore, are difficult to access for maintenance. A uniflow centrifugal segregation system avoids these drawbacks of reverse-flow cyclones since their accessibility is good and their height usually does not exceed their diameter. The efficiency is a critical aspect of separating systems. This study systematically examines the collection efficiency for particles ranging from 1 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">μ</mi><mi mathvariant="normal">m</mi></mrow></semantics></math></inline-formula> to 29 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">μ</mi><mi mathvariant="normal">m</mi></mrow></semantics></math></inline-formula> in diameter based on varying vane angles of the swirl inducer at flow rates ranging from 130 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="normal">L</mi></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi mathvariant="normal">s</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> to 236 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="normal">L</mi></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi mathvariant="normal">s</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>.