Hasil untuk "Construction industry"

Monica Marconi Sciarroni, Emanuele Storti

Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink for real-time processing, while SPARQL and SWRL-based reasoning provide context-dependent stream discovery. Experimental evaluations demonstrate the effectiveness of combining semantic models, context-aware reasoning and distributed stream processing to enable interoperable data workflows for Industry 5.0 environments.

Xuanming Zhang

Industrial carbon emissions are a major driver of climate change, yet modeling these emissions is challenging due to multicollinearity among factors and complex interdependencies across sectors and time. We propose a novel graph-based deep learning framework DGL to analyze and forecast industrial CO_2 emissions, addressing high feature correlation and capturing industrial-temporal interdependencies. Unlike traditional regression or clustering methods, our approach leverages a Graph Neural Network (GNN) with attention mechanisms to model relationships between industries (or regions) and a temporal transformer to learn long-range patterns. We evaluate our framework on public global industry emissions dataset derived from EDGAR v8.0, spanning multiple countries and sectors. The proposed model achieves superior predictive performance - reducing error by over 15% compared to baseline deep models - while maintaining interpretability via attention weights and causal analysis. We believe that we are the first Graph-Temporal architecture that resolves multicollinearity by structurally encoding feature relationships, along with integration of causal inference to identify true drivers of emissions, improving transparency and fairness. We also stand a demonstration of policy relevance, showing how model insights can guide sector-specific decarbonization strategies aligned with sustainable development goals. Based on the above, we show high-emission "hotspots" and suggest equitable intervention plans, illustrating the potential of state-of-the-art AI graph learning to advance climate action, offering a powerful tool for policymakers and industry stakeholders to achieve carbon reduction targets.

Jorge A. Arroyo

I study how first sizable industry entries reshape local and neighboring labor markets in Puerto Rico. Using over a decade of quarterly municipality--industry data (2014Q1--2025Q1), I identify ``first sizable entries'' as large, persistent jumps in establishments, covered employment, and wage bill, and treat these as shocks to local industry presence at the municipio--industry level. Methodologically, I combine staggered-adoption difference-in-differences estimators that are robust to heterogeneous treatment timing with an imputation-based event-study approach, and I use a doubly robust difference-in-differences framework that explicitly allows for interference through pre-specified exposure mappings on a contiguity graph. The estimates show large and persistent direct gains in covered employment and wage bill in the treated municipality--industry cells over 0--16 quarters. Same-industry neighbors experience sizable short-run gains that reverse over the medium run, while within-municipality cross-industry and neighbor all-industries spillovers are small and imprecisely estimated. Once these spillovers are taken into account and spatially robust inference and sensitivity checks are applied, the net regional 0--16 quarter effect on covered employment is positive but modest in magnitude and estimated with considerable uncertainty. The results imply that first sizable entries generate substantial local gains where they occur, but much smaller and less precisely measured net employment gains for the broader regional economy, highlighting the importance of accounting for spatial spillovers when evaluating place-based policies.

Zhongsheng Wang, Sijie Wang, Jia Wang et al.

In the design of customer relationship management (CRM) systems, accurately identifying customer types and offering personalized services are key to enhancing customer satisfaction and loyalty. However, this process faces the challenge of discerning customer voices and intentions, and general pre-trained automatic speech recognition (ASR) models make it difficult to effectively address industry-specific speech recognition tasks. To address this issue, we innovatively proposed a solution for fine-tuning industry-specific ASR models, which significantly improved the performance of the fine-tuned ASR models in industry applications. Experimental results show that our method substantially improves the crucial auxiliary role of the ASR model in industry CRM systems, and this approach has also been adopted in actual industrial applications.

Seonwu Kim, Yohan Na, Kihun Kim et al.

The emergence of open-source large language models (LLMs) has expanded opportunities for enterprise applications; however, many organizations still lack the infrastructure to deploy and maintain large-scale models. As a result, small LLMs (sLLMs) have become a practical alternative despite inherent performance limitations. While Domain Adaptive Continual Pretraining (DACP) has been explored for domain adaptation, its utility in commercial settings remains under-examined. In this study, we validate the effectiveness of a DACP-based recipe across diverse foundation models and service domains, producing DACP-applied sLLMs (ixi-GEN). Through extensive experiments and real-world evaluations, we demonstrate that ixi-GEN models achieve substantial gains in target-domain performance while preserving general capabilities, offering a cost-efficient and scalable solution for enterprise-level deployment.

Ashley Hourigan, Ridewaan Hanslo

The demand for rapid software delivery in the Information Technology (IT) industry has significantly intensified, emphasising the need for faster software products and service releases with enhanced features to meet customer expectations. Agile methodologies are replacing traditional approaches such as Waterfall, where flexibility, iterative development and adaptation to change are favoured over rigid planning and execution. DevOps, a subsequent evolution from Agile, emphasises collaborative efforts in development and operations teams, focusing on continuous integration and deployment to deliver resilient and high-quality software products and services. This study aims to critically assess both Agile and DevOps practices in the IT industry to identify the feasibility and applicability of Agile methods in DevOps practices. Eleven semi-structured interviews were conducted with Agile and DevOps practitioners in varying capacities across several sectors within the IT industry. Through thematic analysis, 51 unique codes were extracted and synthesised into 19 themes that reported on each phase of the DevOps lifecycle, specifically regarding the integration and implementation of Agile methods into DevOps practices. Based on the findings, a new understanding detailing the interrelationship of Agile methods in DevOps practices was discussed that met the research objectives.

Yaling Wang, Zhongxiao Zhang, Xinru Chen et al.

The genus Distylium (Hamamelidaceae) is highly valued for its applications in ornamental horticulture, industry, and construction. Although plastid genomes (plastomes) of multiple Distylium species have been characterized, no mitochondrial genomes (mitogenomes) have been reported for this genus. In this study, we assembled and annotated the complete mitogenome of Distylium racemosum using HiFi sequencing data. The mitogenome comprises a longer circular chromosome and a shorter linear chromosome (904,264 bp in total length), revealing a structurally complex conformation. We annotated 67 genes, including 43 protein-coding genes (PCGs), 21 tRNA genes, and three rRNA genes. Analyses identified exceptionally high repetitive sequence content, with 304 simple sequence repeats, 1,508 dispersed repeats, and 50 tandem repeats, representing the highest repeat content among Saxifragales mitogenomes to date. Additionally, 49 mitochondrial plastid DNA sequences were detected, including only one complete plastid-derived gene (trnC-GCA) transferred to the mitogenome. We predicted 697 RNA editing sites across 42 PCGs, further underscoring the genome’s dynamic evolution. Phylogenetic reconstruction based on mitogenomes and plastomes from 18 species indicated D. racemosum occupied a basal position within Saxifragales, which is consistent with the APG IV classification system. This study provides the first comprehensive mitogenomic resource for the Distylium genus, offering valuable insights for molecular classification, species identification, and germplasm conservation of Distylium plants.

Bingquan LIU, Liyun CAI, Ming QI et al.

ObjectiveIn China, there is a significant geographical mismatch between methanol production and demand. Production is concentrated in the coal-rich regions of Northwest China, while demand is primarily in the economic belts of Central and Eastern China, such as east and south China. This mismatch has resulted in extensive cross-regional transportation of methanol from west to east and north to south. Currently, methanol transportation through highway is dominant in China, facing significant cost challenges and carbon emission pressure. The traditional methanol transportation system struggles to meet the needs of industrial development. MethodsThe economic efficiency of multimodal methanol transportation was systematically analyzed, and a low-cost, low-energy transportation solution was developed to provide theoretical support and practical pathways for the efficient circulation of the methanol industry under the new energy system. Using the data on production capacity, output and consumption of China’s methanol industry from 2020 to 2024, along with pipeline transportation price and enterprise transportation cost survey data disclosed by PipeChina, the cost breakdown structure method was adopted to quantitatively analyze the costs of 4 transportation modes: highway, railway, pipeline, and waterway. A unit transportation cost calculation model was established to compare the economic efficiency and carbon emissions of different transportation modes. In response to the difficulties in methanol transportation and considering the dispersed and small-scale production and demand of methanol, a multimodal transportation scheme featuring “agglomeration” at both ends was proposed, and its feasibility was verified through cost sharing and transportation volume calculation. ResultsPipeline is most economical for methanol transportation, with unit costs for newly built pipelines ranging from RMB 0.18 to 0.28/(t·km). Co-transportation using existing refined oil pipelines can further reduce costs to RMB 0.16−0.25/(t·km). The multimodal transportation model featuring “agglomeration” at both ends enables long-distance, low-cost transport between methanol production and consumption areas, reduces energy consumption, and supports the green transformation of the economy and environmentally-friendly development. ConclusionThe implementation of an optimized transportation system, centered on pipeline transportation with agglomeration at both ends, should focus on promoting: ① the construction of dedicated methanol loading and unloading facilities and cross-regional pipeline networks to address the challenges posed by decentralized production and demand; ② breakthroughs in batch transportation technology for refined oil pipelines to improve purity control during co-transportation; and ③ the establishment of a multimodal transport coordination mechanism at the policy level to minimize institutional costs in the transshipment process.

Gandel Radoslav, Jerabek Jan, Cmiel Petr et al.

The use of machine learning and neural networks in predicting the compressive strength of concrete promises to significantly improve the accuracy and reliability of models for the design and optimization of concrete mixtures. With rapid advances in this field, computational models will be able to handle even larger amounts of experimental data, increasing their ability to capture the complex relationships between input parameters and the mechanical properties of concrete. With the development of new neural network architectures and machine learning algorithms, it will be possible to create highly adaptive predictive models that can better respond to variability in concrete composition and production conditions, leading to more efficient and sustainable design in the construction industry. The submitted paper deals with the design of concrete mixtures and prediction of their compressive strength based on the compressive strength results of mixtures of known composition from other experiments using machine learning. Practical validation of the developed regression model will be carried out by testing the machine-designed mixtures for compressive strength after 28 days.

Ping Ye, Binglin Guo, Huyong Qin et al.

Abstract Considerable carbon emissions from the cement industry pose a notable challenge to achieving long-term sustainable development and creating an enriched social environment. Biochar (BC) obtained from biomass pyrolysis can be used as a carbon-negative material, and it plays a crucial role in the reduction of global carbon emissions. The development of more efficient and cost-effective technologies to fully realize this potential and reduce the environmental impact of BC production and use remains a formidable challenge. The utilization of BC to prepare sustainable cementitious composites with economically value-added benefits has recently attracted much research interest. Therefore, this review analyzes factors influencing the physicochemical properties of BC and their optimization methods, as well as the impact of BC addition on various cement composites and their potential applications. Besides, recent advances in machine learning for predicting the properties of composites and the environmental-economic implications of material are reviewed. The progress and challenges of BC–cement composites are discussed and potential directions for exploration are provided. Therefore, it is recommended to explore commercialization pathways tailored to local conditions and to develop machine learning models for performance prediction and life-cycle analysis, thereby promoting the widespread application of BC in industry and construction. Graphical Abstract

Dan Chong, Siyu Liao, Mingjie Xu et al.

<b>Background</b>: The construction industry faces significant safety hazards, frequent accidents, and inadequate management. Studies identify unsafe worker behaviors as the primary cause of construction accidents. However, most research overlooks the psychological state, particularly emotions, of construction workers. <b>Methods</b>: This study designed a behavioral experiment integrating social cognitive neuroscience, collecting real-time EEG data to classify and recognize fear, anger, and neutral emotions. Variance analysis explored differences in safety hazard identification and risk assessment under these emotional states. A total of 22 male participants were involved, with data collection lasting three days. The role of psychological capital in mediating the effects of emotions on unsafe behaviors was also examined. <b>Results</b>: Emotional classification using EEG signals achieved 79% accuracy by combining frequency domain and nonlinear feature extraction. Fear significantly enhanced safety hazard identification accuracy compared to neutral and anger emotions (F = 0.027, <i>p</i> = 0.03). Risk assessment values under fear and anger were higher than under neutral emotion (F = 0.121, <i>p</i> = 0.023). Psychological capital interacted significantly with emotions in hazard identification accuracy (F = 0.68, <i>p</i> = 0.034), response time (F = 2.562, <i>p</i> = 0.003), and risk assessment response time (F = 1.415, <i>p</i> = 0.026). Safety hazard identification correlated with the number of safety trainings (<i>p</i> = 0.002) and safety knowledge lectures attended (<i>p</i> = 0.025). Risk assessment was significantly associated with smoking (<i>p</i> = 0.023), alcohol consumption (<i>p</i> = 0.004), sleep duration (<i>p</i> = 0.017), and safety training (<i>p</i> = 0.024). <b>Conclusions</b>: The findings provide insights into how emotions affect safety hazard identification and risk assessment, offering a foundation for improving emotional regulation, reducing accidents, and enhancing safety management in construction.

Olajide Julius Faremi, Habeeb Abiodun Arowolo

The ongoing digital transformation of the construction industry, known as Construction 4.0, requires students in built-environment programs to gain adequate exposure to emerging technologies. Despite this need, many faces significant barriers to meaningful engagement. This study explores the factors limiting students’ learning experiences related to Construction 4.0 in Nigerian universities and polytechnics. Using a stratified random sampling method, 154 students from one university and one polytechnic were surveyed through a structured questionnaire. Data were analyzed using frequency distribution, relative importance index, and the Mann-Whitney test. Results identify key obstacles such as limited peer collaboration, insufficient funding and resources, and weak infrastructure. There were no statistically significant differences in these barriers between university and polytechnic respondents. The study concludes that addressing the current limitations requires institutional reforms, including infrastructure investment, curriculum development, faculty training, and support for student collaboration, particularly in polytechnic settings.

Jakub Jakubowski, Natalia Wojak-Strzelecka, Rita P. Ribeiro et al.

Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be performed using Artificial Intelligence (AI) methods, which often use data generated from industrial sensors. The steel industry, which is an important branch of the global economy, is one of the potential beneficiaries of this trend, given its large environmental footprint, the globalized nature of the market, and the demanding working conditions. This survey synthesizes the current state of knowledge in the field of AI-based PdM within the steel industry and is addressed to researchers and practitioners. We identified 219 articles related to this topic and formulated five research questions, allowing us to gain a global perspective on current trends and the main research gaps. We examined equipment and facilities subjected to PdM, determined common PdM approaches, and identified trends in the AI methods used to develop these solutions. We explored the characteristics of the data used in the surveyed articles and assessed the practical implications of the research presented there. Most of the research focuses on the blast furnace or hot rolling, using data from industrial sensors. Current trends show increasing interest in the domain, especially in the use of deep learning. The main challenges include implementing the proposed methods in a production environment, incorporating them into maintenance plans, and enhancing the accessibility and reproducibility of the research.

Roman Bögli, Leandro Lerena, Christos Tsigkanos et al.

TLA+ is a formal specification language used for designing, modeling, documenting, and verifying systems through model checking. Despite significant interest from the research community, knowledge about usage of the TLA+ ecosystem in practice remains scarce. Industry reports suggest that software engineers could benefit from insights, innovations, and solutions to the practical challenges of TLA+. This paper explores this development by conducting a systematic literature review of TLA+'s industrial usage over the past decade. We analyze the trend in industrial application, characterize its use, examine whether its promised benefits resonate with practitioners, and identify challenges that may hinder further adoption.

Shivam Somani

The console gaming industry, a dominant force in the global entertainment sector, has witnessed a wave of consolidation in recent years, epitomized by Microsoft's high-profile acquisitions of Activision Blizzard and Zenimax. This study investigates the repercussions of such mergers on consumer welfare and innovation within the gaming landscape, focusing on product quality as a key metric. Through a comprehensive analysis employing a difference-in-difference model, the research evaluates the effects of acquisition on game review ratings, drawing from a dataset comprising over 16,000 console games released between 2000 and 2023. The research addresses key assumptions underlying the difference-in-difference methodology, including parallel trends and spillover effects, to ensure the robustness of the findings. The DID results suggest a positive and statistically significant impact of acquisition on game review ratings, when controlling for genre and release year. The study contributes to the literature by offering empirical evidence on the direct consequences of industry consolidation on consumer welfare and competition dynamics within the gaming sector.

Daisuke Mashima, Muhammad M. Roomi, Bennet Ng et al.

Assurance of cybersecurity is crucial to ensure dependability and resilience of smart power grid systems. In order to evaluate the impact of potential cyber attacks, to assess deployability and effectiveness of cybersecurity measures, and to enable hands-on exercise and training of personals, an interactive, virtual environment that emulates the behaviour of a smart grid system, namely smart grid cyber range, has been demanded by industry players as well as academia. A smart grid cyber range is typically implemented as a combination of cyber system emulation, which allows interactivity, and physical system (i.e., power grid) simulation that are tightly coupled for consistent cyber and physical behaviours. However, its design and implementation require intensive expertise and efforts in cyber and physical aspects of smart power systems as well as software/system engineering. While many industry players, including power grid operators, device vendors, research and education sectors are interested, availability of the smart grid cyber range is limited to a small number of research labs. To address this challenge, we have developed a framework for modelling a smart grid cyber range using an XML-based language, called SG-ML, and for "compiling" the model into an operational cyber range with minimal engineering efforts. The modelling language includes standardized schema from IEC 61850 and IEC 61131, which allows industry players to utilize their existing configurations. The SG-ML framework aims at making a smart grid cyber range available to broader user bases to facilitate cybersecurity R\&D and hands-on exercises.

Gabriele Valvano, Antonino Agostino, Giovanni De Magistris et al.

Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtain in industrial environments. Generative AI offers opportunities to enlarge small industrial datasets artificially, thus enabling the usage of state-of-the-art supervised approaches in the industry. Unfortunately, also good generative models need a lot of data to train, while industrial datasets are often tiny. Here, we propose a new approach for reusing general-purpose pre-trained generative models on industrial data, ultimately allowing the generation of self-labelled defective images. First, we let the model learn the new concept, entailing the novel data distribution. Then, we force it to learn to condition the generative process, producing industrial images that satisfy well-defined topological characteristics and show defects with a given geometry and location. To highlight the advantage of our approach, we use the synthetic dataset to optimise a crack segmentor for a real industrial use case. When the available data is small, we observe considerable performance increase under several metrics, showing the method's potential in production environments.

Monika Steidl, Benedikt Dornauer, Michael Felderer et al.

Deviations from expected behavior during runtime, known as anomalies, have become more common due to the systems' complexity, especially for microservices. Consequently, analyzing runtime monitoring data, such as logs, traces for microservices, and metrics, is challenging due to the large volume of data collected. Developing effective rules or AI algorithms requires a deep understanding of this data to reliably detect unforeseen anomalies. This paper seeks to comprehend anomalies and current anomaly detection approaches across diverse industrial sectors. Additionally, it aims to pinpoint the parameters necessary for identifying anomalies via runtime monitoring data. Therefore, we conducted semi-structured interviews with fifteen industry participants who rely on anomaly detection during runtime. Additionally, to supplement information from the interviews, we performed a literature review focusing on anomaly detection approaches applied to industrial real-life datasets. Our paper (1) demonstrates the diversity of interpretations and examples of software anomalies during runtime and (2) explores the reasons behind choosing rule-based approaches in the industry over self-developed AI approaches. AI-based approaches have become prominent in published industry-related papers in the last three years. Furthermore, we (3) identified key monitoring parameters collected during runtime (logs, traces, and metrics) that assist practitioners in detecting anomalies during runtime without introducing bias in their anomaly detection approach due to inconclusive parameters.

Seunghyun Son, Youngju Na, Bumjin Han

In the construction industry, despite the development of technology and the efforts of companies, safety accidents are frequent, and the types of accidents are also diversified. In particular, when looking at the accident rates of the construction industry, the number of deaths from fall accidents accounts for a very high proportion. To resolve this, various measures to prevent fall, such as installation of safety railings and safety nets, have been proposed at the national level, but the effect is very insignificant. Therefore, it is necessary to establish measures for safety management and to propose prevention techniques by in-depth analysis of the causes of fall accidents through actual accident cases at the construction sites. The purpose of this study is to assess the risk of the cause of fall accidents for sustainable safety management at construction sites. To this end, data collection of fall accident cases at domestic construction sites, risk assessment by cause, and fall accident prevention techniques are conducted in order. This study was conducted on fall accident cases that occurred at a height of more than 2m. The results of this study will contribute to substantially reducing fall accidents at construction sites in South Korea. Additionally, it is used as basic data for improving Korea's construction safety management system.

Suguru Otani, Takuma Matsuda

We construct a novel unified merger list in the global container shipping industry between 1966 (the beginning of the industry) and 2022. Combining the list with proprietary data, we construct a structural matching model to describe the historical transition of the importance of a firm's age, size, and geographical proximity on merger decisions. We find that, as a positive factor, a firm's size is more important than a firm's age by 9.858 times as a merger incentive between 1991 and 2005. However, between 2006 and 2022, as a negative factor, a firm's size is more important than a firm's age by 2.013 times, that is, a firm's size works as a disincentive. We also find that the distance between buyer and seller firms works as a disincentive for the whole period, but the importance has dwindled to economic insignificance in recent years. In counterfactual simulations, we observe that the prohibition of mergers between firms in the same country would affect the merger configuration of not only the firms involved in prohibited mergers but also those involved in permitted mergers. Finally, we present interview-based evidence of the consistency between our merger lists, estimations, and counterfactual simulations with the industry experts' historical experiences.