Abstract In the context of Industry 4.0 and the rapid development of intelligent construction technologies, advanced digital monitoring methods such as Digital Image Correlation (DIC) and IoT-based sensing are increasingly used to support refined structural performance evaluation. Against this technological background, this study investigates the seismic performance of an X-shaped braced composite wall panel designed within an integrated prefabricated construction framework and with an emphasis on green building materials. Quasi-static tests were conducted to examine the deformation characteristics, failure mechanisms, and load-transfer behavior resulting from the coordinated action of the prefabricated inner and outer frames with the X-shaped steel brace. DIC and IoT sensors were used as auxiliary tools to enhance the observation of global and local deformation, although the primary focus of the research remained on the mechanical behavior of the proposed wall panel. The comparison between DIC measurements, IoT sensor data, and experimentally observed failure patterns confirmed the rationality of the internal force flow and the progressive damage process. The results indicate that the composite wall panel exhibits improved Displacement capacity, deformation capability, and seismic energy dissipation, demonstrating its potential for application in resilient and sustainable prefabricated building systems.
Andrea Arcuri, Alexander Poth, Olsi Rrjolli
et al.
REST APIs are widely used in industry, in all different kinds of domains. An example is Volkswagen AG, a German automobile manufacturer. Established testing approaches for REST APIs are time consuming, and require expertise from professional test engineers. Due to its cost and importance, in the scientific literature several approaches have been proposed to automatically test REST APIs. The open-source, search-based fuzzer EvoMaster is one of such tools proposed in the academic literature. However, how academic prototypes can be integrated in industry and have real impact to software engineering practice requires more investigation. In this paper, we report on our experience in using EvoMaster at Volkswagen AG, as an EvoMaster user from 2023 to 2026. We share our learnt lessons, and discuss several features needed to be implemented in EvoMaster to make its use in an industrial context successful. Feedback about value in industrial setups of EvoMaster was given from Volkswagen AG about 4 APIs. Additionally, a user study was conducted involving 11 testing specialists from 4 different companies. We further identify several real-world research challenges that still need to be solved.
Workplace injuries are a significant issue in the construction industry. The efficiency and effectiveness of the OHS system can be analysed using safety indicators. This paper presents a methodological procedure for the selection and ranking of indicators to improve the OHS system. The procedure consists of two phases: preparatory and execution. In the preparatory phase, a hierarchical structure was proposed, consisting of three aspects, four factors, and thirty-nine indicators. Additionally, experts were selected. In the execution phase, experts applied the weighted Borda method to select five key indicators for each factor. The selected indicators were then compared and ranked using the group fuzzy analytic hierarchy process (FAHP). The influence of experts in the selection and ranking of indicators was determined according to their previous experience. The procedure was applied to analyse OHS systems in small window- and door-fitting companies. Among the indicators, experts highlighted the efficiency of OHS resource management, the frequency of coordinating activities with contractors, the communication capacity of the workers, the analysis of the results of external OHS controls, the number of external funds for OHS system improvements as well as the number and assessment of incidents. Furthermore, ten scenarios were analysed with varying values of factor weight values. The scenarios demonstrated that the highlighted indicators were consistently ranked among the top ten indicators.
Jaroslav Pokorný, Radek Ševčík, Lucie Zárybnická
et al.
Biomass residues from the agricultural industry, logging and wood processing activities have become a valuable fuel source. If processed under pyrolysis combustion, several products are generated. Bio-oil and gases are essential alternatives to fossil coal-based fuels for energy and electricity production, whose need is constantly growing. Biochar, the porous carbon-based lightweight product, often ends up as a soil fertilizer. However, it can be applied in other industrial sectors, e.g., in plastics production or in modifying cementitious materials intended for construction needs. This work dealt with the application of small amounts of softwood-based biochar up to 2.0 wt.% on hydration kinetics and a wide range of physical and mechanical properties, such as water transport characteristics and flexural and compressive strengths of modified cement pastes. In the comparison with reference specimens, the biochar incorporation into cement pastes brought benefits like the reduction of open porosity, improvement of strength properties, and decreased capillary water absorption of 7-day and 28-day-cured cement pastes. Moreover, biochar-dosed cement pastes showed an increase in heat evolution during the hydration process, accompanied by higher consumption of clinker minerals. Considering all examined characteristics, the optimal dosage of softwood-derived biochar of 1.0 wt.% of Portland cement can be recommended.
Sathish Krishna Anumula, SVSV Prasad Sanaboina, Ravi Kumar Nagula
et al.
The growing need to automate processes in industrial settings has led to tremendous growth in the robotic systems and especially the robotic arms. The paper assumes the design, modeling and control of a robotic arm to suit industrial purpose like assembly, welding and material handling. A six-degree-of-freedom (DOF) robotic manipulator was designed based on servo motors and a microcontroller interface with Mechanical links were also fabricated. Kinematic and dynamic analyses have been done in order to provide precise positioning and effective loads. Inverse Kinematics algorithm and Proportional-Integral-Derivative (PID) controller were also applied to improve the precision of control. The ability of the system to carry out tasks with high accuracy and repeatability is confirmed by simulation and experimental testing. The suggested robotic arm is an affordable, expandable, and dependable method of automation of numerous mundane procedures in the manufacturing industry.
Designing effective debt collection systems is crucial for improving operational efficiency and reducing costs in the financial industry. However, the challenges of maintaining script diversity, contextual relevance, and coherence make this task particularly difficult. This paper presents a debt collection system based on real debtor-collector data from a major commercial bank. We construct a script library from real-world debt collection conversations, and propose a two-stage retrieval based response system for contextual relevance. Experimental results show that our system improves script diversity, enhances response relevance, and achieves practical deployment efficiency through knowledge distillation. This work offers a scalable and automated solution, providing valuable insights for advancing debt collection practices in real-world applications.
The rapid evolution of the transportation cybersecurity ecosystem, encompassing cybersecurity, automotive, and transportation and logistics sectors, will lead to the formation of distinct spatial clusters and visitor flow patterns across the US. This study examines the spatiotemporal dynamics of visitor flows, analyzing how socioeconomic factors shape industry clustering and workforce distribution within these evolving sectors. To model and predict visitor flow patterns, we develop a BiTransGCN framework, integrating an attention-based Transformer architecture with a Graph Convolutional Network backbone. By integrating AI-enabled forecasting techniques with spatial analysis, this study improves our ability to track, interpret, and anticipate changes in industry clustering and mobility trends, thereby supporting strategic planning for a secure and resilient transportation network. It offers a data-driven foundation for economic planning, workforce development, and targeted investments in the transportation cybersecurity ecosystem.
Deep learning solutions for vulnerability detection proposed in academic research are not always accessible to developers, and their applicability in industrial settings is rarely addressed. Transferring such technologies from academia to industry presents challenges related to trustworthiness, legacy systems, limited digital literacy, and the gap between academic and industrial expertise. For deep learning in particular, performance and integration into existing workflows are additional concerns. In this work, we first evaluate the performance of CodeBERT for detecting vulnerable functions in industrial and open-source software. We analyse its cross-domain generalisation when fine-tuned on open-source data and tested on industrial data, and vice versa, also exploring strategies for handling class imbalance. Based on these results, we develop AI-DO(Automating vulnerability detection Integration for Developers' Operations), a Continuous Integration-Continuous Deployment (CI/CD)-integrated recommender system that uses fine-tuned CodeBERT to detect and localise vulnerabilities during code review without disrupting workflows. Finally, we assess the tool's perceived usefulness through a survey with the company's IT professionals. Our results show that models trained on industrial data detect vulnerabilities accurately within the same domain but lose performance on open-source code, while a deep learner fine-tuned on open data, with appropriate undersampling techniques, improves the detection of vulnerabilities.
Purpose: Today, the efficient management of supply chains plays a fundamental role in the market and economy. The supply chain is a network of facilities working together to make and move products from upstream to downstream to provide customers with highly qualified products and services. Nowadays, construction has become a growing and huge industry sector worldwide. One of the supply chains that needs proper management is related to the construction industry. The purpose of this article is to optimize this type of supply chain by minimizing its total costs.
Design/methodology/approach: An attempt has been made to develop an optimization model for the construction supply chain, considering all the important elements involved in the construction process, i.e. contractors, designers, suppliers of materials and construction materials, as well as three important and basic flows in the construction industry, i.e. the flow of manpower, the flow of equipment and machinery, and the flow of materials. All indices, parameters, decision variables, objective functions and constraints have been introduced and presented in the proposed model.
Findings: The model proposed by GAMS optimization software was solved and the obtained results included the lowest construction cost as well as the optimal amount of construction materials and materials, labour, equipment, and machinery based on the required construction size.
Research limitations/implications: The application of the supply chain in the construction industry is a relatively new topic. In the classic supply chain, the flow of materials and output at the end of the chain includes the manufactured product, while in the construction supply chain, the final output includes a building or a structure. Individuals, industries and even countries incur a lot of construction costs to meet their needs in the field of construction. The current study was influenced by limitations such as access to real data and the impossibility of handling a real case study, because the problem of designing the construction supply chain has wide dimensions and requires access to all dimensions of the construction industry chain, from upstream to downstream.
Practical implications: With the definition and expansion of the concept of supply chain and the use of supply chain management in manufacturing industries and the positive results it brought in various manufacturing industries, supply chain management emerged in the construction industry. Meanwhile, researchers, major contractors, and large construction companies are trying to find methods to take advantage of the supply chain management approach. Also, the stakeholders of the construction industry can enable active decision-making and agile responses to market fluctuations by continuously monitoring and updating the results of cost sensitivity analysis.
Social implications: Optimizing the construction supply chain can lead to reduced costs, improved project timelines, and enhanced sustainability. However, it may also impact local communities through job displacement, environmental concerns, and social inequality. Balancing efficiency with social responsibility is crucial to ensure equitable outcomes in construction projects.
Originality/value: By now, there has been no reference available in the literature in the field of construction supply chain considering the designer, the flow of manpower and the flow of drawings and technical documents. The proposed model is comprehensive and includes the construction chain, considering all aspects such as the flow of required materials and materials, the flow of labour, the flow of required equipment and machinery, the flow of plans and documents, and designers and contractors.
With recent advancements in the telecommunication industry and the deployment of 5G networks, radio propagation modeling is considered a fundamental task in planning and optimization. Accurate and efficient models of radio propagation enable the estimation of Path Loss (PL) or Received Signal Strength (RSS), which is used in a variety of practical applications including the construction of radio coverage maps and localization. Traditional PL models use fundamental physics laws and regression-based models, which can be guided with measurements. In general, these methods have small computational complexity and have been highly successful in attaining accurate models for settings with trivial environmental complexity (e.g., clear weather or no clutter). However, attaining high accuracy in radio propagation modeling at complex settings (e.g., an urban setting with many buildings and obstacles) has required ray tracing, which computationally complex. Recently, the wireless community has been studying Machine Learning (ML)-based modeling algorithms to find a middle-ground. ML algorithms have become faster to execute and, more importantly, more radio data measurements have become available with the increased deployment of wireless devices. In this survey, we explore the recent advancements in the use of ML for modeling and predicting radio coverage and PL.
Telecommunication, Transportation and communications
The complexity and uncertainty of construction projects contribute to low efficiency in the construction industry. This research applied the Takt-time planning method to optimize the construction working process, and proposed a risk control framework based on Value at Risk (VaR) and Conditional Value at Risk (CVaR) approaches to explore and predict a project schedule and cost performance under different scenarios. This research selected a high-rise residential building project for a case study and collected 1672 productivity data samples. Arena Simulation models were established based on 90 combinations of labor assignments to assess Takt-time planning strategies’ impact on project performance in four scenarios. The VaR and CVaR evaluations at 75% and 90% confidence levels were compared to balance project benefits and risks. Without any overtime or additional workers, this research found a Takt-time planning method that can reduce the project duration by 20.2% and labor costs by 2.1% at the same time, using a labor assignment of 12 bar placers, 12 carpenters, and 5 pipefitters. The findings can assist construction managers to achieve a shorter duration, reduced cost, and safer work environment, which will be very effective and beneficial to improve project overall performance.
Mega Dewi Ashari Putri, Yatnanta Padma Devia, M. Ruslin Anwar
Population growth led to massive construction development, with increasing demand every year. One of those demands is housing construction. The high demand for this house has led to the emergence of new contractors on a local scale, with products and services of unstable quality. Hence, the contractors need to be more competitive in the industry. This research aims to identify and analyze the important factors influencing quality assurance (QA) and the competitiveness of local contractors in housing construction projects. The analysis is grounded in three primary frameworks: ISO 9001:2015 (Quality Management Systems), PMBOK 6th Edition (Project Management Body of Knowledge), and Ministry of Public Works and Public Housing Regulation No. 4 of 2009. The research design used statistical analysis, Relative Importance Index (RII), and Analytical Hierarchy Process (AHP) to identify key factors. Research findings indicate that leadership commitment, project resources, and quality documentation are the three most critical factors. Regardless of the identified critical factors, all factors contribute to a significant influence on successful performance. The proposed strategic approach involves sequentially improving the "quality of work output," followed by "customer retention" and "project cycle efficiency." These findings can serve as a guideline for contractors in making decisions to enhance their competitiveness.
The construction industry is an important material production sector of the national economy, and trade in goods and services between different industrial sectors in different regions may result in the transfer of embodied carbon emissions from the construction industry. A systematic identification of the relationships and structural characteristics of the embodied carbon transfer in the construction industry is crucial for rationally defining the responsibility for emission reduction and scientifically formulating emission reduction policies to promote the effective promotion of China’s carbon emission reduction actions. Based on the calculation of input-output theory, this study constructs a multi-regional input-output (MRIO) model of 31 provinces in China containing 28 industries to estimate the carbon emissions of the construction industry in 2017, it also combines the complex network theory to construct the industrial and regional embodied carbon transfer network of China’s construction industry, and calculates the network structure indexes to deeply explore the spatial transfer network structure characteristics of the embodied carbon transfer between regions of China’s construction industry in 2017. The results show that the construction, energy and building materials manufacturing sectors are at the core of the sectoral carbon transfer network structure, with strong network control. The embodied carbon transfer network between regions in the construction industry has a small-world character, more than 40% of all relevant regions have carbon transfer relationships with other regions, significant carbon emissions are transferred from the resource-rich, industrially well-endowed central-western and north-eastern provinces to the economically developed south-eastern coastal provinces. According to the results of the study, differentiated carbon emission reduction plans are formulated, and policy suggestions for optimizing the carbon emission reduction plan of the construction industry are put forward.
Across the technology industry, many companies have expressed their commitments to AI ethics and created dedicated roles responsible for translating high-level ethics principles into product. Yet it is unclear how effective this has been in leading to meaningful product changes. Through semi-structured interviews with 26 professionals working on AI ethics in industry, we uncover challenges and strategies of institutionalizing ethics work along with translation into product impact. We ultimately find that AI ethics professionals are highly agile and opportunistic, as they attempt to create standardized and reusable processes and tools in a corporate environment in which they have little traditional power. In negotiations with product teams, they face challenges rooted in their lack of authority and ownership over product, but can push forward ethics work by leveraging narratives of regulatory response and ethics as product quality assurance. However, this strategy leaves us with a minimum viable ethics, a narrowly scoped industry AI ethics that is limited in its capacity to address normative issues separate from compliance or product quality. Potential future regulation may help bridge this gap.
Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera
et al.
New technologies such as Machine Learning (ML) gave great potential for evaluating industry workflows and automatically generating key performance indicators (KPIs). However, despite established standards for measuring the efficiency of industrial machinery, there is no precise equivalent for workers' productivity, which would be highly desirable given the lack of a skilled workforce for the next generation of industry workflows. Therefore, an ML solution combining data from manufacturing processes and workers' performance for that goal is required. Additionally, in recent times intense effort has been devoted to explainable ML approaches that can automatically explain their decisions to a human operator, thus increasing their trustworthiness. We propose to apply explainable ML solutions to differentiate between expert and inexpert workers in industrial workflows, which we validate at a quality assessment industrial workstation. Regarding the methodology used, input data are captured by a manufacturing machine and stored in a NoSQL database. Data are processed to engineer features used in automatic classification and to compute workers' KPIs to predict their level of expertise (with all classification metrics exceeding 90 %). These KPIs, and the relevant features in the decisions are textually explained by natural language expansion on an explainability dashboard. These automatic explanations made it possible to infer knowledge from expert workers for inexpert workers. The latter illustrates the interest of research in self-explainable ML for automatically generating insights to improve productivity in industrial workflows.
Agile methodologies have gained significant traction in the software development industry, promising increased flexibility and responsiveness to changing requirements. However, their applicability to safety-critical systems, particularly in the automotive sector, remains a topic of debate. This paper examines the benefits and challenges of implementing agile methods in the automotive industry through a comprehensive review of relevant literature and case studies. Our findings highlight the potential advantages of agile approaches, such as improved collaboration and faster time-to-market, as well as the inherent challenges, including safety compliance and cultural resistance. By synthesizing existing research and practical insights, this paper aims to provide an understanding of the role of agile methods in shaping the future of automotive software development.
Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative experiments are performed based on a laboratory batch
This study provides a comprehensive analysis of artificial intelligence (AI) contribution to research in the translation industry (ACTI), synthesizing it over forty-five years from 1980-2024. 13220 articles were retrieved from three sources, namely WoS, Scopus, and Lens; 9836 were unique records, which were used for the analysis. We provided two types of analysis, viz., scientometric and thematic, focusing on Cluster, Subject categories, Keywords, Bursts, Centrality and Research Centers as for the former. For the latter, we provided a thematic review for 18 articles, selected purposefully from the articles involved, centering on purpose, approach, findings, and contribution to ACTI future directions. This study is significant for its valuable contribution to ACTI knowledge production over 45 years, emphasizing several trending issues and hotspots including Machine translation, Statistical machine translation, Low-resource language, Large language model, Arabic dialects, Translation quality, and Neural machine translation. The findings reveal that the more AI develops, the more it contributes to translation industry, as Neural Networking Algorithms have been incorporated and Deep Language Learning Models like ChatGPT have been launched. However, much rigorous research is still needed to overcome several problems encountering translation industry, specifically concerning low-resource, multi-dialectical and free word order languages, and cultural and religious registers.