Kasra Ekrad, Bjarne Johansson, Inés Alvarez Vadillo
et al.
Interoperability across industrial automation systems is a cornerstone of Industry 4.0. To address this need, the OPC Unified Architecture (OPC UA) Publish-Subscribe (PubSub) model offers a promising mechanism for enabling efficient communication among heterogeneous devices. PubSub facilitates resource sharing and communication configuration between devices, but it lacks clear guidelines for mapping diverse industrial traffic types to appropriate PubSub configurations. This gap can lead to misconfigurations that degrade network performance and compromise real-time requirements. This paper proposes a set of guidelines for mapping industrial traffic types, based on their timing and quality-of-service specifications, to OPC UA PubSub configurations. The goal is to ensure predictable communication and support real-time performance in industrial networks. The proposed guidelines are evaluated through an industrial use case that demonstrates the impact of incorrect configuration on latency and throughput. The results underline the importance of traffic-aware PubSub configuration for achieving interoperability in Industry 4.0 systems.
Industry classification schemes are integral parts of public and corporate databases as they classify businesses based on economic activity. Due to the size of the company registers, manual annotation is costly, and fine-tuning models with every update in industry classification schemes requires significant data collection. We replicate the manual expert verification by using existing or easily retrievable multimodal resources for industry classification. We present MONETA, the first multimodal industry classification benchmark with text (Website, Wikipedia, Wikidata) and geospatial sources (OpenStreetMap and satellite imagery). Our dataset enlists 1,000 businesses in Europe with 20 economic activity labels according to EU guidelines (NACE). Our training-free baseline reaches 62.10% and 74.10% with open and closed-source Multimodal Large Language Models (MLLM). We observe an increase of up to 22.80% with the combination of multi-turn design, context enrichment, and classification explanations. We will release our dataset and the enhanced guidelines.
Additive Manufacturing (AM), widely known as 3D printing, has evolved from a prototyping tool to a transformative technology impacting aerospace, automotive, construction, and consumer goods industries. This review explores recent advancements in AM materials, processes, and applications that enhance its functionality and support sustainable manufacturing. Key innovations include high-performance composites such as carbon fiber-reinforced polymers, nanomaterials like graphene-based inks, and biodegradable polymers such as polylactic acid (PLA). In addition, the integration of multi-material and hybrid printing has expanded AM’s applicability in precision manufacturing. These developments enable AM to meet stringent requirements across critical industries, improving customization, production efficiency, and environmental impact. Despite its potential, AM faces challenges related to material durability, process consistency, standardization, scalability, and energy consumption. Addressing these issues demands ongoing research in advanced materials, process optimization, and sustainable practices, with an emphasis on integrating AM into Industry 4.0 and distributed manufacturing. This study concludes by identifying future research directions focused on AM’s role in driving mass customization, circular economy practices, and industrial-scale applications, establishing it as a foundational technology in modern manufacturing.
Depressurization combined with thermal stimulation based on injection-production well patterns is considered promising for gas hydrate development. Nevertheless, its direct application to Shenhu challenging hydrates may be problematic due to the presence of low reservoir permeability and permeable boundaries. The present study proposes to improve the development potential of Shenhu hydrate by reservoir reconstruction, including boundary sealing and reservoir fracturing, and numerically investigates the production performance. The results showed that water intrusion, hot loss, and gas leakage can be effectively addressed by boundary sealing. Nevertheless, it cannot enhance productivity as thermal decomposition gas accumulated around the injection well. Conversely, reservoir fracturing can significantly improve extraction efficiency as substantial amounts of hydrates dissociate along the fractures, and the gas can be well recovered through the fractures. However, reservoir fracturing was not conducive to water control and energy utilization as it induced more severe water flooding and gas leakage. Under the synergistic effect of the two, there was no methane leakage, and the gas production rate increased with increasing fracture conductivity, while the gas-to-water ratio and energy ratio presented the opposite trend. To obtain a favorable production performance, a fracture with a conductivity of 1–10 D·cm was recommended. Therefore, the combination of boundary sealing and reservoir fracturing makes it feasible for safe and efficient extraction of offshore challenging hydrate under the injection-production mode.
Ulan Abdullaev, Umetali Dzhusuev, Sakina Asanova
et al.
The study is devoted to the analysis of modern methods of production of energy-efficient building materials. A broad review of statistics on the level of emissions of harmful substances and the popularity of various energy-efficient materials was carried out, which made it possible to identify the environmental factor as the most vulnerable. Analysing the United Nations (UN) recommended measures to reduce CO2 emissions from the production of building materials and the amount of CO2 emissions in different countries, cement, steel and aluminium add 2.5 billion tonnes of CO2. In comparison, brick and glass production adds approximately 1.2 billion tonnes of CO2 annually. The problem of producing materials more adapted to regional peculiarities, such as insulation made from local natural materials, is considered in the example of Kyrgyzstan. Modern methods of building materials production in Ukraine include, for example, improving the quality of cement, the use of green energy and the production of Portland cement clinker. The use of waste in construction is an important step towards sustainability and environmental efficiency. This is especially true for ergonomic construction, where waste management reduces the negative impact on the environment and reduces natural resource consumption. Despite advances in construction technology and the introduction of alternative materials, bricks remain one of the most common materials. India is a leader in this area, although a study of its statistics revealed that due to the global prevalence of informal production practices, accurate statistics are difficult to collect. All sub-sectors of the ceramic industry are energy-intensive due to the need for drying and sintering at high temperatures (800-2000°C), which requires significant energy resources. Sustainability-oriented solutions and technology substitution are proving to be key to the decarbonisation of the ceramic industry. The combination of different technologies and approaches provides significant benefits in reducing CO2 emissions and energy consumption. The study results are relevant for the development of recommendations for the integration of environmentally friendly, innovative and ergonomic methods of production of energy-efficient building materials.
Orientation: Amid growing health and safety concerns in South African construction, leaders must foster and model safe behaviours to promote compliance and participation. Despite construction being inherently dangerous, psychological capital (PsyCap) offers a constructive alternative to enhance safety behaviour.
Research purpose: The study examined the relationship between authentic leadership and safety behaviour as mediated by PsyCap and its individual dimensions separately.
Motivation for the study: The construction industry must identify new ways to manage challenging work environments to reduce safety violations which impact employee wellness, accident track records and organisational performance. Viewing safety through a positive lens may help organisations identify novel approaches to improve safety behaviour in construction.
Research approach/design and method: This cross-sectional, quantitative study used online and paper-based surveys to investigate relationships between the constructs. Convenience sampling was employed to recruit workers across hierarchical levels at three locations within two South African construction firms.
Main findings: There is a positive relationship between authentic leadership and safety behaviour. Hope and efficacy fully mediated the relationship between authentic leadership and safety behaviour. Optimism partially mediates the relationship, while resilience has no impact.
Practical/managerial implications: Practitioners can apply these findings to support talent management and other workplace interventions to improve leadership development, foster PsyCap and improve safety behaviour.
Contributions/value-add: This research establishes the foundation for understanding how authentic leadership and PsyCap influence safety behaviour in the construction industry. It will help South African construction firms manage demanding environments and reduce occupational safety violations and related injuries.
Azmine Toushik Wasi, Enjamamul Haque Eram, Sabrina Afroz Mitu
et al.
Industry 5.0 marks a new phase in industrial evolution, emphasizing human-centricity, sustainability, and resilience through the integration of advanced technologies. Within this evolving landscape, Generative AI (GenAI) and autonomous systems are not only transforming industrial processes but also emerging as pivotal geopolitical instruments. We examine strategic implications of GenAI in Industry 5.0, arguing that these technologies have become national assets central to sovereignty, access, and global influence. As countries compete for AI supremacy, growing disparities in talent, computational infrastructure, and data access are reshaping global power hierarchies and accelerating the fragmentation of the digital economy. The human-centric ethos of Industry 5.0, anchored in collaboration between humans and intelligent systems, increasingly conflicts with the autonomy and opacity of GenAI, raising urgent governance challenges related to meaningful human control, dual-use risks, and accountability. We analyze how these dynamics influence defense strategies, industrial competitiveness, and supply chain resilience, including the geopolitical weaponization of export controls and the rise of data sovereignty. Our contribution synthesizes technological, economic, and ethical perspectives to propose a comprehensive framework for navigating the intersection of GenAI and geopolitics. We call for governance models that balance national autonomy with international coordination while safeguarding human-centric values in an increasingly AI-driven world.
A. R. Pina, Shams El-Adawy, H. J. Lewandowski
et al.
Quantum Information Science and Engineering (QISE) education and workforce development are top priorities at the national level in the US. This has included a push for academia to support the development of programs that will prepare students to enter the QISE workforce. As the field of QISE has grown rapidly in academia and industry, there is a need to better understand what quantum knowledge is needed for students to be ready for the workforce. We present preliminary findings on the level of quantum expertise and the specific quantum knowledge utilized across different roles, and in the execution of specific tasks in the QISE industry. Qualitative analysis of semi-structured interviews with industry professionals elucidates these aspects of the vital work functions related to the ongoing development of quantum technologies in industry. This work will provide insights into QISE curriculum development and changes needed to better support students transitioning into this growing industry.
Shams El-Adawy, A. R. Piña, Benjamin M. Zwickl
et al.
As the quantum information science and engineering (QISE) workforce grows, there is an anticipated need for professionals with bachelor's and master's degrees who can fill a wide range of roles in the quantum industry. This report identifies the experimental skills needed for individuals with bachelor's or master's degrees to succeed in quantum industry roles. Through semi-structured interviews with quantum industry employers, we gathered data on 22 distinct positions spanning hardware, software, and business functions. While employers describe varying expectations of quantum expertise, the unifying requirement across these roles is proficiency in experimental skills, which fall into four key categories: instrumentation, computation and data analysis, experimental and project design, as well as communication and collaboration. Positions open to bachelor's and master's graduates use all four skill areas, but the balance of experimental skill set needed differs. Bachelor's roles lean toward instrumentation, computation and data analysis, as well as experimental and project design skills. Individuals in these roles build, operate, and troubleshoot hardware, and they gather and interpret data to design and carry out experiments. Master's roles stand out with the communication and collaboration skills needed on top of the other three skill categories. Individuals in these roles oversee experiments, coordinate teams, and align efforts with company and client needs. By articulating experimental skills needed for bachelor's and master's roles in the quantum industry, this report provides actionable insights for educators developing QISE courses and programs.
The rapid development of Industry 4.0 technologies requires robust and comprehensive standardization to ensure interoperability, safety and efficiency in the Industry of the Future. This paper examines the fundamental role and functionality of standardization, with a particular focus on its importance in Europe's regulatory framework. Based on this, selected topics in context of standardization activities in context intelligent manufacturing and digital twins are highlighted and, by that, an overview of the Industry 4.0 standards framework is provided. This paper serves both as an informative guide to the existing standards in Industry 4.0 with respect to Artificial Intelligence and Digital Twins, and as a call to action for increased cooperation between standardization bodies and the research community. By fostering such collaboration, we aim to facilitate the continued development and implementation of standards that will drive innovation and progress in the manufacturing sector.
The pre-training paradigm plays a key role in the success of Large Language Models (LLMs), which have been recognized as one of the most significant advancements of AI recently. Building on these breakthroughs, code LLMs with advanced coding capabilities bring huge impacts on software engineering, showing the tendency to become an essential part of developers' daily routines. However, the current code LLMs still face serious challenges related to trustworthiness, as they can generate incorrect, insecure, or unreliable code. Recent exploratory studies find that it can be promising to detect such risky outputs by analyzing LLMs' internal states, akin to how the human brain unconsciously recognizes its own mistakes. Yet, most of these approaches are limited to narrow sub-domains of LLM operations and fall short of achieving industry-level scalability and practicability. To address these challenges, in this paper, we propose PtTrust, a two-stage risk assessment framework for code LLM based on internal state pre-training, designed to integrate seamlessly with the existing infrastructure of software companies. The core idea is that the risk assessment framework could also undergo a pre-training process similar to LLMs. Specifically, PtTrust first performs unsupervised pre-training on large-scale unlabeled source code to learn general representations of LLM states. Then, it uses a small, labeled dataset to train a risk predictor. We demonstrate the effectiveness of PtTrust through fine-grained, code line-level risk assessment and demonstrate that it generalizes across tasks and different programming languages. Further experiments also reveal that PtTrust provides highly intuitive and interpretable features, fostering greater user trust. We believe PtTrust makes a promising step toward scalable and trustworthy assurance for code LLMs.
The innovation of the study is that the deep learning method and sentiment analysis are integrated in traditional business model analysis and forecasting, and the research subject is TSMC for industry trend prediction of semiconductor industry in Taiwan. For the rapid market changes and development of wafer technologies of semiconductor industry, traditional data analysis methods not perform well in the high variety and time series data. Textual data and time series data were collected from seasonal reports of TSMC including financial information. Textual data through sentiment analysis by considering the event intervention both from internal events of the company and the external global events. Using the sentiment-enhanced time series data, the LSTM model was adopted for predicting industry trend of TSMC. The prediction results reveal significant development of wafer technology of TSMC and the potential threatens in the global market, and matches the product released news of TSMC and the international news. The contribution of the work performed accurately in industry trend prediction of the semiconductor industry by considering both the internal and external event intervention, and the prediction results provide valuable information of semiconductor industry both in research and business aspects.
Anita Staroń, Barbara Pucelik, Agata Barzowska
et al.
Modern production of vegetable oils has reached impressive levels, and the ever-growing quantities of waste cooking oil (WCO) provide a local source of raw materials for innovative materials. The WCO composite production process involves a series of reactions, including polymerisation, esterification, and transesterification, which lead to the hardening of composite materials. In light of the growing problem of bacterial and fungal diseases, materials with high strength properties and biocidal properties are being sought. Fungal infections of the skin are a widespread problem, and the number of cases is steadily increasing. This article presents a study of the antibacterial potential of WCO-based composites enriched with hops or sorrel root in the context of their application in the construction industry. The compressive and flexural strength of the oil composites, their absorbability and hydrophobicity, and their effects on Gram-positive (S. aureus and S. epidermidis) and Gram-negative (E. coli and P. aeruginosa) bacteria and fungi (A. niger, P. anomala) were investigated. Maximum split tensile strength (4.3 MPa) and flexural strength (5.1 MPa) were recorded for oil-hop composites. Oil composites enriched with curly sorrel and hops showed antibacterial activity against S. aureus at 27% and 25%. High biocidal activity (up to 70%) was recorded against E. coli and against S. epidermidis (up to 99%) due to the action of composites with curly sorrel. The antifungal activities of composites with hops was 15% and 19% for P. anomala and A. niger, respectively, while with curly sorrel they were 42% and 30%.
Fiber-reinforced mycelium (FRM) composites offer an innovative and sustainable approach to construction materials for architectural structures. Mycelium, the root structure of fungi, can be combined with various natural fibers (NF) to create a strong and lightweight material with environmental benefits. Incorporating NF like hemp, jute, or bamboo into the mycelium matrix enhances mechanical properties. This combination results in a composite that boasts enhanced strength, flexibility, and durability. Natural FRM composites offer sustainability through the utilization of agricultural waste, reducing the carbon footprint compared to conventional construction materials. Additionally, the lightweight yet strong nature of the resulting material makes it versatile for various construction applications, while its inherent insulation properties contribute to improved energy efficiency in buildings. Developing and adopting natural FRM composites showcases a promising step towards sustainable and eco-friendly construction materials. Ongoing research and collaboration between scientists, engineers, and the construction industry will likely lead to further improvements and expanded applications. This article provides a comprehensive analysis of the current research and applications of natural FRM composites for innovative and sustainable construction materials. Additionally, the paper reviews the mechanical properties and potential impacts of these natural FRM composites in the context of sustainable architectural construction practices. Recently, the applicability of mycelium-based materials has extended beyond their original domains of biology and mycology to architecture.
Chemicals: Manufacture, use, etc., Textile bleaching, dyeing, printing, etc.
CHANG Yu, WANG Gang, ZHU Peng, KONG Lingfei, HE Jingheng
The industry Internet security knowledge graph plays an important role in enriching the semantic relationships of security concepts, improving the quality of the security knowledge base, and enhancing the ability to visualize and analyze the security situation. It has become the key to recognize, trace and protect against the attacks targeting new energy industry control systems. However, compared with the construction of the general domain knowledge graph, there are still many problems in each stage of the construction of the industry Internet security knowledge graph, which affect its practical application effect. This paper introduces the concept and significance of the industry Internet security knowledge graph and its difference from the general knowledge graph, summarizes the related work and role of the ontology construction of industry Internet security knowledge graph. Under the background of industry Internet security, it focuses on the related work of the three important components of knowledge graph construction, respectively named entity recognition, relationship extraction and reference resolution. For each component, it detailedly reports on the development history and research status of this component in the domain, and deeply analyses the domain challenges in this component, such as non-continuous entity recognition, candidate word extraction, the lack of domain-quality datasets and so on. It predicts the future research directions of this component, provides reference and enlightenment to further improve the quality and usefulness of industry Internet security knowledge graph, so as to deal with emerging threats and attacks more effectively.
Wisdom Richard Mgomezulu, Paul Thangata, Daniel Njiwa
The impact of trade liberalization on Malawi’s economy has been a hotly debated topic. To shed light on the subject, a study was conducted using the PEP-1–1 CGE model and the latest Malawi’s Social Accounting Matrix (SAM) from 2019. The results were eye-opening, revealing the potential effects of the African Continental Free Trade Area (AfCFTA) on various sectors of the economy. The removal of trade tariffs is predicted to have a significant impact on prices, with a decrease of 26.31% in the agricultural sector alone, services (−7.88%), public administration (−9.92%), and manufacturing and industry (−11.23%) imposing hopes of improving food affordability and food security. However, it is expected to have adverse impacts on wage rates in the agricultural sector (−18.78%), manufacturing and construction (−19.01%), services (−2.79%) and public administration (−15.81%). Additionally, while exports are expected to increase, the country’s balance of payments may suffer as imports are likely to outweigh foreign earnings. This could also lead to a decrease in government revenue from taxes. To mitigate these effects, the study suggests implementing export restructuring strategies, particularly in industries like manufacturing and construction, and promoting diversification of local production to boost competitiveness and improve wage rates. With these measures in place, the government will not only offset potential losses but also tap into new sources of taxable income.
Cities. Urban geography, Urbanization. City and country
In the context of carbon peaking and carbon neutrality as well as the construction of a new energy system, the integrated development and intelligent regulation of fossil energy with new energy resources such as wind, solar, and geothermal energy has become a new pattern for the future energy system. The oil and gas industry is undergoing digital and intelligent transformation, and the development of smart oil and gas fields will reduce exploration and development costs and increase social and economic benefits. This study elucidates the concept and implications of smart oil and gas fields with multi-energy synergy and the critical role of new smart oil and gas fields in enhancing oil and gas reserves and production and in promoting the green, low-carbon, and intelligent transformation of the oil and gas industry. It reviews the current development status of the wind-solar-geothermal-energy storage multi-energy synergy system, the integration of oil and gas fields with the multi-energy synergy system, and the smart oil and gas fields. The study also identifies the challenges and key issues faced by the development of smart oil and gas fields in China. It summarizes future scenarios for smart oil and gas fields with multi-energy synergy: (1) utilization of green electricity, (2) new geothermal systems for thermal recovery of abandoned heavy oil reservoirs, (3) production optimization of oil well clusters based on wind/solar power microgrids, (4) in-situ conversion of natural gas and utilization of associated gas for power generation, (5) comprehensive energy management systems for efficient and low-carbon oil and gas production, and (6) intelligent collaboration and optimization of electricity, geothermal energy, and hydrogen energy storage. The core output of the construction of smart oil and gas fields with multi-energy synergy is low-carbon oil fields and super energy basins. Under the premise of ensuring oil and gas production, future development should focus on supplementing fundamental shortcomings, increasing technological advantages, strengthening application capabilities, and achieving independence, thereby achieving breakthroughs in key core technologies to form practical development solutions.
Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy
et al.
Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.
Time series forecasting in the air cargo industry presents unique challenges due to volatile market dynamics and the significant impact of accurate forecasts on generated revenue. This paper explores a comprehensive approach to demand forecasting at the origin-destination (O\&D) level, focusing on the development and implementation of machine learning models in decision-making for the air cargo industry. We leverage a mixture of experts framework, combining statistical and advanced deep learning models to provide reliable forecasts for cargo demand over a six-month horizon. The results demonstrate that our approach outperforms industry benchmarks, offering actionable insights for cargo capacity allocation and strategic decision-making in the air cargo industry. While this work is applied in the airline industry, the methodology is broadly applicable to any field where forecast-based decision-making in a volatile environment is crucial.