Pei B. Ong, Yixiu Zhuge, Christopher Cheeseman
et al.
Amorphous precipitated silica (APS) produced by acid leaching of olivine has been characterised and assessed for use as a supplementary cementitious material (SCM). The APS was thermally treated between 400 and 1000°C to modify its pore structure, surface area, composition and reactivity. Pastes and mortars containing APS were cast with CEM I replacement levels from 0 to 30 wt.% and water-to-binder ratio of 0.5. TGA-MS, Q-XRD, FTIR and R3 tests show that APS has moderate to high pozzolanic reactivity. Mortars with 10 wt.% as-produced APS showed 30% increase in 28-day compressive strength compared to the control (50 MPa). Mortars with 20 wt.% replacement had comparable strengths to the control. Thermal treatment moderately reduced APS specific surface area and water demand, and improved mix workability, with mortars retaining comparable strengths to samples containing as-produced APS. The research demonstrates that silica derived from olivine has potential to be used as an SCM.
A growing number of publications address the best practices to use Large Language Models (LLMs) for software engineering in recent years. However, most of this work focuses on widely-used general purpose programming languages like Python due to their widespread usage training data. The utility of LLMs for software within the industrial process automation domain, with highly-specialized languages that are typically only used in proprietary contexts, remains underexplored. This research aims to utilize and integrate LLMs in the industrial development process, solving real-life programming tasks (e.g., generating a movement routine for a robotic arm) and accelerating the development cycles of manufacturing systems.
Keno Moenck, Adrian Philip Florea, Julian Koch
et al.
Autonomous vision applications in production, intralogistics, or manufacturing environments require perception capabilities beyond a small, fixed set of classes. Recent open-vocabulary methods, leveraging 2D Vision-Language Foundation Models (VLFMs), target this task but often rely on class-agnostic segmentation models pre-trained on non-industrial datasets (e.g., household scenes). In this work, we first demonstrate that such models fail to generalize, performing poorly on common industrial objects. Therefore, we propose a training-free, open-vocabulary 3D perception pipeline that overcomes this limitation. Instead of using a pre-trained model to generate instance proposals, our method simply generates masks by merging pre-computed superpoints based on their semantic features. Following, we evaluate the domain-adapted VLFM "IndustrialCLIP" on a representative 3D industrial workshop scene for open-vocabulary querying. Our qualitative results demonstrate successful segmentation of industrial objects.
Shams El-Adawy, A. R. Piña, Benjamin M. Zwickl
et al.
This report builds upon the Categorization of Roles in the Quantum Industry report by providing detailed profiles for 29 distinct roles across the quantum workforce. While the earlier report established a framework of four major role categories (hardware, software, bridging, and public facing and business) and their subcategories, the current report expands on this structural framework by characterizing what professionals in each role actually do, particularly by identifying the tasks, knowledge, skills, abilities (KSAs), and experience typically required for each role. Each role profile follows a standardized structure guided by the Occupational Information Network (O*NET) framework. By presenting a fine-grained view of day-to-day work and qualification expectations, this report serves as a practical resource for educators, students, industry professionals, and policymakers aiming to understand, educate, and support the evolving quantum workforce.
The Integrated Environmental Management System is a system that moves away from the traditional single-medium approach and adopts a medium-integrated approach to achieve a higher level of environmental protection. Korea has been implementing the Integrated Environmental Management System since 2017, and as of 2024, total of 19 industries have received integrated permits. In 2023, the cement manufacturing industry was added to the list of industries subject to the Integrated Environmental Management System. As a critical industry with significant emission of air pollutants and particulate matter, it requires systematic environmental management that prioritizes public health and sustainability. This study aims to compare and analyze the Best Available Technique Reference Documents (BREFs) for the cement manufacturing industry in Korea and the European Union (EU) to explore environmental management improvement strategies for Korea’s cement industry. The analysis revealed that the K-BREF provides strong practical guidelines and is more focused on industrial sites, while the EU-BREF is characterized by an integrated environmental management approach reflecting carbon neutrality strategies and resource circulation policies. Particularly in terms of waste fuel management, the EU-BREF provided specific details on waste handling and preparation processes, suitability assessment of waste fuels, lists of maximum allowable substance values, and actual application cases. Additionally, the BAT-AEL (Best Available Techniques Associate Emission Level) and monitoring intervals were also further specified. In contrast, the K-BREF reflects the early stage of implementing an integrated environmental management system, resulting in a lack of technical examples related to waste fuels and BAT-AEL standards being limited to a single criterion per pollutant. The Korean cement industry must expand its use of waste fuels to meet greenhouse gas reduction targets of 12% by 2030 and 53% by 2050. Accordingly, specific multi-media integrated control standards should be established. To effectively respond to increasingly stringent emission regulations and ensure sustainable environmental management, a multi-faceted approach including regulatory improvements, technological development, and policy support is essential.
Madhusmita Mishra, Ashirbad Mishra, Poonam Mangaraj
et al.
Heavy metals, particularly cadmium (Cd) and lead (Pb), pose a significant environmental challenge worldwide owing to their detrimental effects on ecosystem sustainability. India, the most populous country in the world, presently faces severe contamination by heavy metals. This study identifies and quantifies the Cd and Pb emissions from the principal industrial sources at the district level across India, using the IPCC bottom-up approach for 2019. The developed emission inventory includes various industries, notably coal-based power plants, captive power plants, cement production, iron and steel manufacturing, non-ferrous metal production, municipal and biomedical waste incineration, the glass industry, and fly ash generated. Annual emissions were reported to be approximately 2,016 tonnes/year (t/yr) for Cd and 19,258 t/yr for Pb, where coal combustion across different industries emitted approximately 93 t of Cd and 927 t of Pb, with the energy sector contributing about 66% and fly ash accounting for over 80% of total emissions. Among non-ferrous metals, copper production is solely responsible for 44 t and 77 t of Cd and Pb, respectively. The research also identifies regional hotspots for Cd and Pb emissions across India, highlighting areas where targeted remediation strategies can support sustainable environmental management.
ObjectiveThis study employs a probabilistic forecasting approach and robust optimization to address parameter uncertainty in portfolio optimization models within the Iranian capital market. The main focus is on enhancing portfolio performance by accounting for uncertainty and utilizing machine learning models to construct portfolios with maximum Sharpe ratios. MethodsTwo common approaches are applied to incorporate parameter uncertainty into the portfolio optimization model. The first approach is robust optimization, which defines an uncertainty set for each parameter and analyzes the problem in such a way that the solution remains optimal even under worst-case parameter realizations. The second approach involves an advanced machine learning model, Natural Gradient Boosting (NGBoost), whose outputs were employed within a probabilistic forecasting framework. The model inputs included five technical indicators: Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), Average True Range (ATR), Average Price Trading (ATP), and Momentum. Technical analysis is one of the main approaches in examining and forecasting financial market trends, which is based on the study and evaluation of historical price and trading volume data. This method assumes that all fundamental and psychological information of the market is reflected in prices, and that price movements form recognizable and repeatable patterns. The study is conducted across 10 industries, including basic metals, oil refining, banking and financial institutions, petrochemicals and chemicals, automotive, cement, pharmaceuticals, precious metals, rubber and plastics, and metallic minerals. The aforementioned industries are among the largest sectors of the Iranian capital market and, in terms of market value, constitute a substantial portion of the market. These industries encompass a wide range of production and service domains, each playing a fundamental role in the country’s economy and industrial development. Overall, the synergy of these industries strengthens economic diversification, foreign exchange earnings, employment, and sustainable development. After applying robust and probabilistic forecasting models in portfolio optimization, the results were compared against two benchmark portfolios—an equal-weight portfolio and the Markowitz mean-variance model—using the Sharpe ratio as the evaluation metric. Results"Using data from March 2022 to March 2024 for training the NGBoost model and estimating parameters for robust optimization, and 2024 data as the test set, portfolios were constructed for all ten industries. Their out-of-sample risk and return were then calculated. The comparison indicated that both proposed approaches significantly outperformed the benchmark portfolios, achieving higher Sharpe ratios at the 99% confidence level. ConclusionThe findings demonstrate that employing distributional rather than point forecasts, combined with smart beta strategies and robust parameter consideration in portfolio optimization, leads to portfolios with superior risk-return trade-offs. This enhanced performance is statistically significant at the 99% level. Furthermore, the results indicate that incorporating technical indicators as explanatory factors for returns can effectively improve return predictability. Leveraging these indicators in smart beta portfolio construction yields portfolios with superior performance.
Imran Riaz Hasrat, Eun-Young Kang, Christian Uldal Graulund
Safety and reliability are crucial in industrial drive systems, where hazardous failures can have severe consequences. Detecting and mitigating dangerous faults on time is challenging due to the stochastic and unpredictable nature of fault occurrences, which can lead to limited diagnostic efficiency and compromise safety. This paper optimizes the safety and diagnostic performance of a real-world industrial Basic Drive Module(BDM) using Uppaal Stratego. We model the functional safety architecture of the BDM with timed automata and formally verify its key functional and safety requirements through model checking to eliminate unwanted behaviors. Considering the formally verified correct model as a baseline, we leverage the reinforcement learning facility in Uppaal Stratego to optimize the safe failure fraction to the 90 % threshold, improving fault detection ability. The promising results highlight strong potential for broader safety applications in industrial automation.
Hsin-Chieh Kung, Chien-Hsing Wu, Bo-Wun Huang
et al.
Mercury's neurotoxic effects have prompted the development of advanced control and remediation methods to meet stringent measures for industries with high-mercury feedstocks. Industries with significant Hg emissions, including artisanal and small-scale gold mining (ASGM)-789.2 Mg year−1, coal combustion-564.1 Mg year−1, waste combustion-316.1 Mg year−1, cement production-224.5 Mg year−1, and non-ferrous metals smelting-204.1 Mg year−1, use oxidants and adsorbents capture Hg from waste streams. Oxidizing agents such as O3, Cl2, HCl, CaBr2, CaCl2, and NH4Cl oxidize Hg0 to Hg2+ for easier adsorption. To functionalize adsorbents, carbonaceous ones use S, SO2, and Na2S, metal-based adsorbents use dimercaprol, and polymer-based adsorbents are grafted with acrylonitrile and hydroxylamine hydrochloride. Adsorption capacities span 0.2–85.6 mg g−1 for carbonaceous, 0.5–14.8 mg g−1 for metal-based, and 168.1–1216 mg g−1 for polymer-based adsorbents. Assessing Hg contamination in soils and sediments uses bioindicators and stable isotopes. Remediation approaches include heat treatment, chemical stabilization and immobilization, and phytoremediation techniques when contamination exceeds thresholds. Achieving a substantially Hg-free ecosystem remains a formidable challenge, chiefly due to the ASGM industry, policy gaps, and Hg persistence. Nevertheless, improvements in adsorbent technologies hold potential.
Sotiris Michaelides, Stefan Lenz, Thomas Vogt
et al.
The industrial landscape is undergoing a significant transformation, moving away from traditional wired fieldbus networks to cutting-edge 5G mobile networks. This transition, extending from local applications to company-wide use and spanning multiple factories, is driven by the promise of low-latency communication and seamless connectivity for various devices in industrial settings. However, besides these tremendous benefits, the integration of 5G as the communication infrastructure in industrial networks introduces a new set of risks and threats to the security of industrial systems. The inherent complexity of 5G systems poses unique challenges for ensuring a secure integration, surpassing those encountered with any technology previously utilized in industrial networks. Most importantly, the distinct characteristics of industrial networks, such as real-time operation, required safety guarantees, and high availability requirements, further complicate this task. As the industrial transition from wired to wireless networks is a relatively new concept, a lack of guidance and recommendations on securely integrating 5G renders many industrial systems vulnerable and exposed to threats associated with 5G. To address this situation, in this paper, we summarize the state-of-the-art and derive a set of recommendations for the secure integration of 5G into industrial networks based on a thorough analysis of the research landscape. Furthermore, we identify opportunities to utilize 5G to enhance security and indicate remaining challenges, identifying future academic directions.
Francesco Sanfedino, Paolo Iannelli, Daniel Alazard
et al.
To overcome the innovation gap of the Guidance, Navigation and Control (GNC) design process between research and industrial practice a benchmark of industrial relevance has been developed and is presented. This initiative is driven as well by the necessity to train future GNC engineers and the GNC space community on a set of identified complex problems. It allows to demonstrate the relevance of state-of-the-art modeling, control and analysis algorithms for future industrial adoption. The modeling philosophy for robust control synthesis, analysis including the control architecture that enables the simulation of the mission, i.e. the acquisition of a high pointing space mission, are provided.
This paper discusses the importance of reflective and socially conscious education in engineering schools, particularly within the EE/CS sector. While most engineering disciplines have historically aligned themselves with the demands of the technology industry, the lack of critical examination of industry practices and their impact on justice, equality, and sustainability is self-evident. Today, the for-profit engineering/technology companies, some of which are among the largest in the world, also shape the narrative of engineering education and research in universities. As engineering graduates form the largest cohorts within STEM disciplines in Western countries, they become future professionals who will work, lead, or even establish companies in this industry. Unfortunately, the curriculum within engineering education often lacks a deep understanding of social realities, an essential component of a comprehensive university education. Here we establish this unusual connection with the industry that has driven engineering higher education for several decades and its obvious negative impacts to society. We analyse this nexus and highlight the need for engineering schools to hold a more critical viewpoint. Given the wealth and power of modern technology companies, particularly in the ICT domain, questioning their techno-solutionism narrative is essential within the institutes of higher education.
The Industrial Internet of Things (IIoT) enables industries to build large interconnected systems utilizing various technologies that require high data rates. Terahertz (THz) communication is envisioned as a candidate technology for achieving data rates of several terabits-per-second (Tbps). Despite this, establishing a reliable communication link at THz frequencies remains a challenge due to high pathloss and molecular absorption. To overcome these limitations, this paper proposes using intelligent reconfigurable surfaces (IRSs) with THz communications to enable future smart factories for the IIoT. In this paper, we formulate the power allocation and joint IIoT device and IRS association (JIIA) problem, which is a mixed-integer nonlinear programming (MINLP) problem. {Furthermore, the JIIA problem aims to maximize the sum rate with imperfect channel state information (CSI).} To address this non-deterministic polynomial-time hard (NP-hard) problem, we decompose the problem into multiple sub-problems, which we solve iteratively. Specifically, we propose a Gale-Shapley algorithm-based JIIA solution to obtain stable matching between uplink and downlink IRSs. {We validate the proposed solution by comparing the Gale-Shapley-based JIIA algorithm with exhaustive search (ES), greedy search (GS), and random association (RA) with imperfect CSI.} The complexity analysis shows that our algorithm is more efficient than the ES.
The current landscape of massive production industries is undergoing significant transformations driven by emerging customer trends and new smart manufacturing technologies. One such change is the imperative to implement mass customization, wherein products are tailored to individual customer specifications while still ensuring cost efficiency through large-scale production processes. These shifts can profoundly impact various facets of the industry. This study focuses on the necessary adaptations in shop-floor production planning. Specifically, it proposes the use of efficient evolutionary algorithms to tackle the flowshop with missing operations, considering different optimization objectives: makespan, weighted total tardiness, and total completion time. An extensive computational experimentation is conducted across a range of realistic instances, encompassing varying numbers of jobs, operations, and probabilities of missing operations. The findings demonstrate the competitiveness of the proposed approach and enable the identification of the most suitable evolutionary algorithms for addressing this problem. Additionally, the impact of the probability of missing operations on optimization objectives is discussed.
Edafetanure-Ibeh Faith, Evah Patrick Tamarauefiye, Mark Uwuoruya Uyi
The aim of attending an educational institution is learning, which in turn is sought after for the reason of independence of thoughts, ideologies as well as physical and material independence. This physical and material independence is gotten from working in the industry, that is, being a part of the independent working population of the country. There needs to be a way by which students upon graduation can easily adapt to the real world with necessary skills and knowledge required. This problem has been a challenge in some computer science departments, which after effects known after the student begins to work in an industry. The objectives of this project include: Designing a web based chat application for the industry and computer science department, Develop a web based chat application for the industry and computer science and Evaluate the web based chat application for the industry and computer science department. Waterfall system development lifecycle is used in establishing a system project plan, because it gives an overall list of processes and sub-processes required in developing a system. The descriptive research method applied in this project is documentary analysis of previous articles. The result of the project is the design, software a web-based chat application that aids communication between the industry and the computer science department and the evaluation of the system. The application is able to store this information which can be decided to be used later. Awareness of the software to companies and universities, implementation of the suggestions made by the industry in the computer science curriculum, use of this software in universities across Nigeria and use of this not just in the computer science field but in other field of study
Sabine Upnere, Iveta Novakova, Normunds Jekabsons
et al.
Concrete is a widely used material in various industries, including hazardous waste management. At the same time, its production creates a significant carbon footprint. Therefore, intensive research is being conducted to create more eco-friendly concrete, for example, partially replacing cement with by-products such as oil shale ash (OSA) or improving properties by adding dispersed fibers such as basalt fibers (BFs). The article consists of experimental testing of nine types of concrete and the modeling of crack propagation in bending. The basic trends of crack propagation in samples of concrete with OSA and BFs are simulated using a two-dimensional Finite Element (FE) model considering only material degradation on the opening crack surface and experimental data of three- and four-point bending tests. Crack propagation is modeled using the bridging law approach. A surrogate model for predicting the peak loading as a function of tensile strength and fracture work was created. An examination of the results of the FE model shows that the bilinear and nonlinear bridging law functions best describe the crack growth in the analyzed material. A comparison of experimental and modeled results showed that the length of the composite BF strongly affects the accuracy of the numerical model.
Thomas Decker, Ralf Gross, Alexander Koebler
et al.
In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current state of academic XAI research. Our findings are based on an extensive series of interviews regarding the role and applicability of XAI along the Machine Learning (ML) lifecycle in current industrial practice and its expected relevance in the future. The interviews were conducted among a great variety of roles and key stakeholders from different industry sectors. On top of that, we outline the state of XAI research by providing a concise review of the relevant literature. This enables us to provide an encompassing overview covering the opinions of the surveyed persons as well as the current state of academic research. By comparing our interview results with the current research approaches we reveal several discrepancies. While a multitude of different XAI approaches exists, most of them are centered around the model evaluation phase and data scientists. Their versatile capabilities for other stages are currently either not sufficiently explored or not popular among practitioners. In line with existing work, our findings also confirm that more efforts are needed to enable also non-expert users' interpretation and understanding of opaque AI models with existing methods and frameworks.
J. P. Edelen, M. J. Henderson, J. Einstein-Curtis
et al.
Industrial particle accelerators inherently operate in much dirtier environments than typical research accelerators. This leads to an increase in noise both in the RF system and in other electronic systems. Combined with the fact that industrial accelerators are mass produced, there is less attention given to optimizing the performance of an individual system. As a result, industrial systems tend to under perform considering their hardware hardware capabilities. With the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging, improving the signal processing of these machines will increase the margin for the deployment of these systems. Our work is focusing on using machine learning techniques to reduce the noise of RF signals used for pulse-to-pulse feedback in industrial accelerators. We will review our algorithms, simulation results, and results working with measured data. We will then discuss next steps for deployment and testing on an industrial system.