Hasil untuk "Construction industry"

Dawn M Torrisi, Magnus R Campler, Janice Y Park et al.

Currently, the swine industry is lacking an efficient method for large-scale emergency depopulation. Class A water-based foam (WBF) has been demonstrated as a viable option for large-scale depopulation of pigs in all stages of development. However, these studies exclusively used the PHOS-CHEK WD881 (WD881) Class A foam concentrate based on previously demonstrated efficacy for depopulation. This study investigated the suitability of 15 other commercially available WBF concentrates for depopulation based on foaming performance, physiological effects, and efficacy. The performance of each product was evaluated and compared to WD881 at 0.5, 1.0 and 3.0% water-foam concentrations for low- and high-pressure pump systems. The time to fill an 11.5 m3 construction container and decay rate over a 10-minute dwell time were assessed for each WBF. Top-performing foams were further evaluated for behavior and short-term physiological changes and gross lesions during a 15-minute exposure test on piglets. Finally, the top-performing foams were tested for their suitability to depopulate adult swine during large-scale field conditions. Subcutaneous dataloggers recorded swine activity which was used to estimate the time to cessation of movement (COM), an approximate analog for loss of consciousness. Four WBF concentrates (FireIce Polar EcoFoam, Buckeye Platinum, National Foams Knockdown and BioFor N) were shortlisted based on performance at 1% concentration. These products had a mean (±SD) fill time of 62.4s (± 14.9) and decay rate of 0.5 (± 0.66) cm/min compared to WD881 with 50.0s (± 3.5) and 0.2 (± 0.1) cm/min, respectively. No differences between treatment groups were observed during the exposure testing and subsequent necropsy. For the large-scale field trials, the mean (±SE) time to COM was 151.5 s (±10.5). All foams achieved 100% mortality of swine. This study identified four additional WBFs suitable for swine depopulation which are commercially available on the U.S market. These additional WBF options may facilitate large-scale swine depopulation during widespread infectious disease outbreaks by mitigating potential bottlenecks resulting from product availability.

Zhi-Xin Cai, Zhi-Heng Zeng, Wen-Zhi Chen et al.

Abstract Agaricus bisporus is a widely cultivated edible fungus globally. However, the mechanisms underlying the differences in flavor and nutritional traits between wild-type (W) and cultivated-type (C) strains remain unclear, which hinders the artificial breeding of high-quality varieties. This study systematically revealed, for the first time, the molecular and metabolic basis of flavor divergence between wild and cultivated A. bisporus by integrating transcriptomics and metabolomics. A total of 43 strains (23 wild-type and 20 cultivated-type) were analyzed using high-throughput sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to dissect differences in gene expression and metabolite profiles. Results showed that although total protein and amino acid contents exhibited no significant differences, transcriptomic analysis identified significant upregulation of AGABI2DRAFT_188981 and AGABI2DRAFT_191000 (genes associated with high-affinity methionine permease MUP1) in cultivated strains, suggesting their indirect regulation of flavor formation via methionine metabolism. Metabolomic analysis further revealed a marked increase in uridine levels in cultivated strains (3.2-fold higher than wild-type, p < 0.01), indicating potential medicinal value, while wild strains were enriched with flavor precursors such as fumaric acid and isoleucine (fold change ≥ 2.5). In contrast, cultivated strains accumulated metabolites like 2-hydroxybutyric acid and α-ketoglutarate (VIP > 1.5). This study pioneered the construction of a gene-metabolite correlation network, identifying a strong positive correlation between AGABI2DRAFT_191352 (6-phosphofructokinase) and 2-hydroxybutyric acid (r = 0.82), highlighting the regulatory role of glycolytic flux in flavor metabolism. These findings not only elucidate the impact of artificial cultivation on metabolic reprogramming in A. bisporus but also provide critical molecular targets for targeted breeding of strains with enhanced flavor and nutritional value, offering practical significance for advancing the edible fungi industry.

Georg Rottenwalter, Marcel Tilly, Victor Owolabi

Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. Additionally, many industrial machines lack comprehensive sensor technology, making data acquisition incomplete and challenging. Explainable Artificial Intelligence offers a solution by providing insights into model decision-making and identifying the most relevant features for classification. In this paper, we investigate the impact of feature reduction using XAI techniques on the quality classification of injection-molded parts. We apply SHAP, Grad-CAM, and LIME to analyze feature importance in a Long Short-Term Memory model trained on real production data. By reducing the original 19 input features to 9 and 6, we evaluate the trade-off between model accuracy, inference speed, and interpretability. Our results show that reducing features can improve generalization while maintaining high classification performance, with an small increase in inference speed. This approach enhances the feasibility of AI-driven quality control, particularly for industrial settings with limited sensor capabilities, and paves the way for more efficient and interpretable machine learning applications in manufacturing.

Ningyu He, Shangtong Cao, Haoyu Wang et al.

As JavaScript has been criticized for performance and security issues in web applications, WebAssembly (Wasm) was proposed in 2017 and is regarded as the complementation for JavaScript. Due to its advantages like compact-size, native-like speed, and portability, Wasm binaries are gradually used as the compilation target for industrial projects in other high-level programming languages and are responsible for computation-intensive tasks in browsers, e.g., 3D graphic rendering and video decoding. Intuitively, characterizing in-the-wild adopted Wasm binaries from different perspectives, like their metadata, relation with source programming language, existence of security threats, and practical purpose, is the prerequisite before delving deeper into the Wasm ecosystem and beneficial to its roadmap selection. However, currently, there is no work that conducts a large-scale measurement study on in-the-wild adopted Wasm binaries. To fill this gap, we collect the largest-ever dataset to the best of our knowledge, and characterize the status quo of them from industry perspectives. According to the different roles of people engaging in the community, i.e., web developers, Wasm maintainers, and researchers, we reorganized our findings to suggestions and best practices for them accordingly. We believe this work can shed light on the future direction of the web and Wasm.

Chaozheng Wang, Zezhou Yang, Shuzheng Gao et al.

Code completion, a crucial practice in industrial settings, helps developers improve programming efficiency by automatically suggesting code snippets during development. With the emergence of Large Code Models (LCMs), this field has witnessed significant advancements. Due to the natural differences between open-source and industrial codebases, such as coding patterns and unique internal dependencies, it is a common practice for developers to conduct domain adaptation when adopting LCMs in industry. There exist multiple adaptation approaches, among which retrieval-augmented generation (RAG) and fine-tuning are the two most popular paradigms. However, no prior research has explored the trade-off of the two approaches in industrial scenarios. To mitigate the gap, we comprehensively compare the two paradigms including Retrieval-Augmented Generation (RAG) and Fine-tuning (FT), for industrial code completion in this paper. In collaboration with Tencent's WXG department, we collect over 160,000 internal C++ files as our codebase. We then compare the two types of adaptation approaches from three dimensions that are concerned by industrial practitioners, including effectiveness, efficiency, and parameter sensitivity, using six LCMs. Our findings reveal that RAG, when implemented with appropriate embedding models that map code snippets into dense vector representations, can achieve higher accuracy than fine-tuning alone. Specifically, BM25 presents superior retrieval effectiveness and efficiency among studied RAG methods. Moreover, RAG and fine-tuning are orthogonal and their combination leads to further improvement. We also observe that RAG demonstrates better scalability than FT, showing more sustained performance gains with larger scales of codebase.

Anastasia Zhukova, Jonas Lührs, Christian E. Lobmüller et al.

Recent trends in NLP utilize knowledge graphs (KGs) to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific terminology or relationships between documents that might otherwise be overlooked. This paper explores how SciNCL, a graph-aware neighborhood contrastive learning methodology originally designed for scientific publications, can be applied to the process industry domain, where text logs contain crucial information about daily operations and are often structured as sparse KGs. Our experiments demonstrate that language models fine-tuned with triplets derived from graph embeddings (GE) outperform a state-of-the-art mE5-large text encoder by 9.8-14.3% (5.45-7.96p) on the proprietary process industry text embedding benchmark (PITEB) while having 3 times fewer parameters.

Marcos Lima Romero, Ricardo Suyama

The recent development of Agentic AI systems, empowered by autonomous large language models (LLMs) agents with planning and tool-usage capabilities, enables new possibilities for the evolution of industrial automation and reduces the complexity introduced by Industry 4.0. This work proposes a conceptual framework that integrates Agentic AI with the intent-based paradigm, originally developed in network research, to simplify human-machine interaction (HMI) and better align automation systems with the human-centric, sustainable, and resilient principles of Industry 5.0. Based on the intent-based processing, the framework allows human operators to express high-level business or operational goals in natural language, which are decomposed into actionable components. These intents are broken into expectations, conditions, targets, context, and information that guide sub-agents equipped with specialized tools to execute domain-specific tasks. A proof of concept was implemented using the CMAPSS dataset and Google Agent Developer Kit (ADK), demonstrating the feasibility of intent decomposition, agent orchestration, and autonomous decision-making in predictive maintenance scenarios. The results confirm the potential of this approach to reduce technical barriers and enable scalable, intent-driven automation, despite data quality and explainability concerns.

Suman Kumar Das

India's growing population and economy have significantly increased the demand and consumption of natural resources. As a result, the potential benefits of transitioning to a circular economic model have been extensively discussed and debated among various Indian stakeholders, including policymakers, industry leaders, and environmental advocates. Despite the numerous initiatives, policies, and transnational strategic partnerships of the Indian government, most small and medium enterprises in India face significant challenges in implementing circular economy practices. This is due to the lack of a clear pathway to measure the current state of the circular economy in Indian industries and the absence of a framework to address these challenges. This paper examines the circularity of the 93-textile industry in India using the C-Readiness Tool. The analysis comprehensively identified 9 categories with 34 barriers to adopting circular economy principles in the textile sector through a narrative literature review. The identified barriers were further compared against the findings from a C-readiness tool assessment, which revealed prominent challenges related to supply chain coordination, consumer engagement, and regulatory compliance within the industry's circularity efforts. In response to these challenges, the article proposes a strategic roadmap that leverages digital technologies to drive the textile industry towards a more sustainable and resilient industrial model.

Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi et al.

Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and speed. Yet, organizations in regulated industries face strict constraints on which LLMs they can use - many cannot utilize commercial models due to data privacy regulations and compliance requirements, creating a significant challenge for achieving high-quality code optimization while maintaining cost-effectiveness. We address this by implementing a Mixture-of-Agents (MoA) approach that directly synthesizes code from multiple specialized LLMs, comparing it against TurinTech AI's vanilla Genetic Algorithm (GA)-based ensemble system and individual LLM optimizers using real-world industrial codebases. Our key contributions include: (1) First MoA application to industrial code optimization using real-world codebases; (2) Empirical evidence that MoA excels with open-source models, achieving 14.3% to 22.2% cost savings and 28.6% to 32.2% faster optimization times for regulated environments; (3) Deployment guidelines demonstrating GA's advantage with commercial models while both ensembles outperform individual LLMs; and (4) Real-world validation across 50 code snippets and seven LLM combinations, generating over 8,700 variants, addresses gaps in industrial LLM ensemble evaluation. This provides actionable guidance for organizations balancing regulatory compliance with optimization performance in production environments.

Jingzhi Gong, Rafail Giavrimis, Paul Brookes et al.

There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM systems in production environments. We introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates high-quality, task-specific prompts across diverse LLMs while maintaining industrial efficiency requirements. MPCO leverages metaprompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts. It is an essential part of the ARTEMIS code optimization platform for automated validation and scaling. Our comprehensive evaluation on five real-world codebases with 366 hours of runtime benchmarking demonstrates MPCO's effectiveness: it achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods. Analysis shows that 96% of the top-performing optimizations stem from meaningful edits. Through systematic ablation studies and meta-prompter sensitivity analysis, we identify that comprehensive context integration is essential for effective meta-prompting and that major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.

Qinwen Chen, Wenbiao Tao, Zhiwei Zhu et al.

Community Question Answering (CQA) platforms can be deemed as important knowledge bases in community, but effectively leveraging historical interactions and domain knowledge in real-time remains a challenge. Existing methods often underutilize external knowledge, fail to incorporate dynamic historical QA context, or lack memory mechanisms suited for industrial deployment. We propose ComRAG, a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism designed for retrieval, generation, and efficient storage. Evaluated on three industrial CQA datasets, ComRAG consistently outperforms all baselines--achieving up to 25.9% improvement in vector similarity, reducing latency by 8.7% to 23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations.

Angelica Cardoza, Henry A. Colorado

This study shows an alkaline activated cement (AAC), also known as geopolymer, made from red brick waste with partial addition of Ordinary Portland Cement (OPC). This is a sustainable material since incorporates waste from the brick industry to make cements, therefore increasing the materials circularity and this reducing pollution. The material was cured at room temperature. The brick residue was activated with sodium hydroxide and sodium silicate in aqueous solution to form the hybrid cement. Several mixtures were made with different amounts of waste and proportions of alkaline activator. The mechanical properties of the materials were studied to determine their feasibility to be used in the construction sector. Three contents of OPC were used: 10, 20, and 30 wt%, which were added to improve the mechanical behavior and post-curing time. The activated hybrid cement was analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), compression, and flexural tests. The main results show that the addition of OPC to the brick derived AAC produces an increased compressive strength of 106 MPa when 30 wt% OPC was added, a very significant result since the control sample found was 33 MPa in compression strength, an improvement for more than 3 times. The data were corroborated by statistical analysis.

Eric Guiffo Kaigom

As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide considerable advantages for smart robotized applications in the industry.Workflow efficiency, collective decision enrichment even for executives, as well as a natural, resilient, and sustainable robotized assistance for the workforce are potential advantages. Hence, the Metaverse could consolidate the connection between Industry 4.0 and Industry 5.0. This paper identifies and puts forward potential advantages of the Metaverse for robotized applications and highlights how these advantages support goals pursued by the Industry 4.0 and Industry 5.0 visions. Keywords: Robotics, Metaverse, Digital Twin, VR/AR, AI/ML, Foundation Model;

Davide Frizzo, Francesco Borsatti, Alessio Arcudi et al.

Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest (EIF) AD method. ExIFFI is tested on four industrial datasets, demonstrating superior explanation effectiveness, computational efficiency and improved raw anomaly detection performances. ExIFFI reaches over then 90\% of average precision on all the benchmarks considered in the study and overperforms state-of-the-art Explainable Artificial Intelligence (XAI) approaches in terms of the feature selection proxy task metric which was specifically introduced to quantitatively evaluate model explanations.

Khushi Jaiswal, Ievgeniia Kuzminykh, Sanjay Modgil

As Artificial Intelligence (AI) changes how businesses work, there is a growing need for people who can work in this sector. This paper investigates how well universities in United Kingdom offering courses in AI, prepare students for jobs in the real world. To gain insight into the differences between university curricula and industry demands we review the contents of taught courses and job advertisement portals. By using custom data scraping tools to gather information from job advertisements and university curricula, and frequency and Naive Bayes classifier analysis, this study will show exactly what skills industry is looking for. In this study we identified 12 skill categories that were used for mapping. The study showed that the university curriculum in the AI domain is well balanced in most technical skills, including Programming and Machine learning subjects, but have a gap in Data Science and Maths and Statistics skill categories.

Shengye Wan, Joshua Saxe, Craig Gomes et al.

Recent research advances in Artificial Intelligence (AI) have yielded promising results for automated software vulnerability management. AI-based models are reported to greatly outperform traditional static analysis tools, indicating a substantial workload relief for security engineers. However, the industry remains very cautious and selective about integrating AI-based techniques into their security vulnerability management workflow. To understand the reasons, we conducted a discussion-based study, anchored in the authors' extensive industrial experience and keen observations, to uncover the gap between research and practice in this field. We empirically identified three main barriers preventing the industry from adopting academic models, namely, complicated requirements of scalability and prioritization, limited customization flexibility, and unclear financial implications. Meanwhile, research works are significantly impacted by the lack of extensive real-world security data and expertise. We proposed a set of future directions to help better understand industry expectations, improve the practical usability of AI-based security vulnerability research, and drive a synergistic relationship between industry and academia.

Syed Bustan Fatima Warsi, Biranchi Panda, Pankaj Biswas

3D Concrete Printing (3DCP) is a growing field of sustainable technology that offers an avenue for rapid industrialisation of the construction industry. Over the years, many non-structural elements have been fabricated by 3D printing, thus demonstrating significant advantages over traditional casting process, including freedom from the need of mold, realisation of complex geometries and reduction of material use etc. However, examples of 3D printed structural parts are still limited with addition of fibres into concrete mixes. The fibres can be incorporated in 3D printed concrete either before mixing or printing; or during the printing. The present study adopted systematic review methodology to recommend effective fibre addition method in terms of mechanical properties of the printed concrete composite. In this review work, an attempt has been made to summarise the effect of different fibres including their type, dosage, entrainment methods, while highlighting the technical challenges and approaches adopted in the past for increasing the fibre dosages, controlling their distribution and orientation. This may save a significant amount of time and resources for the industrial sector for 3D printing high strength fibre reinforced concrete structures.

Hua Liu, Chengjian Yang, Zhaorong Chen

Promoting carbon reduction in the construction sector is crucial to achieving China’s ‘double carbon’ target. However, due to the interaction of multiple factors, the carbon emission efficiency of Chinese construction industry (CEECI) varies from province to province, and the path to efficient CEECI is not uniform. This study aims to analyze the combined effects of multiple factors on CEECI and to explore the underlying logic behind the formation of efficient CEECI in the province, which measures the CEECI for 2018 and 2019 for 30 provinces, autonomous regions, and municipalities directly under the Central Government of China using the super-slack-based measure (Super-SBM), which includes non-desired outputs. From a group perspective, the qualitative comparative analysis method is applied to analyze the common mechanism of the regional economic development level, energy consumption structure, business management level, market openness, science, and technology innovation level on CEECI. The results show that the regional construction industry has three equivalent low-carbon development paths: “low energy management”, “scale management”, and “scale market opening”. Finally, according to the differences in regional resource endowments, differentiated paths suitable for the low-carbon development of the construction industry in different regions are proposed.

Ji-Qiang Li, Yan Chen, Yong Biao Ma et al.

With the development of the hydrogen fuel cell automobile industry, higher requirements are put forward for the construction of hydrogen energy infrastructure, the matching of parameters and the control strategy of hydrogen filling rate in the hydrogenation process of hydrogenation station. Fuel for hydrogen fuel cell vehicles comes from hydrogen refueling stations. At present, the technological difficulty of hydrogenation is mainly reflected in the balanced treatment of reducing the temperature rise of hydrogen and shortening the filling time during the fast filling process. The Joule-Thomson (JT) effect occurs when high-pressure hydrogen gas passes through the valve assembly, which may lead to an increase in hydrogen temperature. The JT effect is generally reflected by the JT coefficient. According to the high pressure hydrogen in the pressure reducing valve, the corresponding JT coefficients were calculated by using the VDW equation, RK equation, SRK equation and PR equation, and the expression of JT effect temperature rise was deduced, which revealed the hydrogen temperature variation law in the process of reducing pressure. Make clear the relationship between charging parameters and temperature rise in the process of decompression; the flow and thermal characteristics of hydrogen in the process of decompression are revealed. This study provides basic support for experts to achieve throttling optimization of related pressure control system in hydrogen industry.

Ci-Jyun Liang, Thai-Hoa Le, Youngjib Ham et al.

Artificial intelligence (AI) and robotics research and implementation emerged in the architecture, engineering, and construction (AEC) industry to positively impact project efficiency and effectiveness concerns such as safety, productivity, and quality. This shift, however, warrants the need for ethical considerations of AI and robotics adoption due to its potential negative impacts on aspects such as job security, safety, and privacy. Nevertheless, this did not receive sufficient attention, particularly within the academic community. This research systematically reviews AI and robotics research through the lens of ethics in the AEC community for the past five years. It identifies nine key ethical issues namely job loss, data privacy, data security, data transparency, decision-making conflict, acceptance and trust, reliability and safety, fear of surveillance, and liability, by summarizing existing literature and filtering it further based on its AEC relevance. Furthermore, thirteen research topics along the process were identified based on existing AEC studies that had direct relevance to the theme of ethics in general and their parallels are further discussed. Finally, the current challenges and knowledge gaps are discussed and seven specific future research directions are recommended. This study not only signifies more stakeholder awareness of this important topic but also provides imminent steps towards safer and more efficient realization.