We propose two distinct approaches to the measurement of industry upstreamness (or average distance from final use) and show that they yield an equivalent measure. Furthermore, we provide two additional interpretations of this measure, one of them related to the concept of forward linkages in Input-Output analysis. On the empirical side, we construct this measure for 426 industries using the 2002 US Input-Output Tables. We also verify the stability of upstreamness across countries in the OECD STAN database, albeit with a more aggregated industry classification. Finally, we present an application that explores the determinants of the average upstreamness of exports at the country level using trade flows for 2002.
Static analysis tools (SATs) are widely adopted in both academia and industry for improving software quality, yet their practical use is often hindered by high false positive rates, especially in large-scale enterprise systems. These false alarms demand substantial manual inspection, creating severe inefficiencies in industrial code review. While recent work has demonstrated the potential of large language models (LLMs) for false alarm reduction on open-source benchmarks, their effectiveness in real-world enterprise settings remains unclear. To bridge this gap, we conduct the first comprehensive empirical study of diverse LLM-based false alarm reduction techniques in an industrial context at Tencent, one of the largest IT companies in China. Using data from Tencent's enterprise-customized SAT on its large-scale Advertising and Marketing Services software, we construct a dataset of 433 alarms (328 false positives, 105 true positives) covering three common bug types. Through interviewing developers and analyzing the data, our results highlight the prevalence of false positives, which wastes substantial manual effort (e.g., 10-20 minutes of manual inspection per alarm). Meanwhile, our results show the huge potential of LLMs for reducing false alarms in industrial settings (e.g., hybrid techniques of LLM and static analysis eliminate 94-98% of false positives with high recall). Furthermore, LLM-based techniques are cost-effective, with per-alarm costs as low as 2.1-109.5 seconds and $0.0011-$0.12, representing orders-of-magnitude savings compared to manual review. Finally, our case analysis further identifies key limitations of LLM-based false alarm reduction in industrial settings.
Plastic industry workers are often exposed to repetitive tasks and awkward postures due to mass production, increasing their risk of ergonomic-related injuries. This study aimed to assess these risks in a real plastic syringe injection molding factory, focusing on the injection molding workstation as a case study. Various ergonomic assessment tools were used, including the Cornell Musculoskeletal Disorder Questionnaire, Ovako Working Posture Analysis System (OWAS), Rapid Upper Limb Assessment (RULA), and Rapid Entire Body Assessment (REBA), and Jack Siemens software for posture simulation. The results revealed that workers are commonly experienced pain in the lower back, neck, and shoulders. After implementing ergonomic modifications to the workstation, the assessment tools indicated a significant reduction in risk levels: compression forces on the lower back decreased by 30%, shear forces by 49%, and the percentage of time spent in risky joint angles per work cycle decreased by 3% for the neck, 23% for the back, 3% for the shoulder, 5% for the wrist, and 9% for the elbow. These findings highlight the importance of ergonomic redesign in improving worker health and reducing occupational hazards.
Sathish Krishna Anumula, Sivaramkumar Ponnarangan, Faizal Nujumudeen
et al.
A mix of intelligent systems and robotics is making engineering industries much more efficient, precise and able to adapt. How artificial intelligence (AI), machine learning (ML) and autonomous robotic technologies are changing manufacturing, civil, electrical and mechanical engineering is discussed in this paper. Based on recent findings and a suggested way to evaluate intelligent robotic systems in industry, we give an overview of how their use impacts productivity, safety and operational costs. Experience and case studies confirm the benefits this area brings and the problems that have yet to be solved. The findings indicate that intelligent robotics involves more than a technology change; it introduces important new methods in engineering.
Domain-adaptive continual pretraining (DAPT) is a state-of-the-art technique that further trains a language model (LM) on its pretraining task, e.g., masked language modeling (MLM), when common domain adaptation via LM fine-tuning is not possible due to a lack of labeled task data. Although popular, MLM requires a significant corpus of domain-related data, which is difficult to obtain for specific domains in languages other than English, such as the process industry in the German language. This paper introduces an efficient approach called ICL-augmented pretraining or ICL-APT that leverages in-context learning (ICL) and k-nearest neighbors (kNN) to augment target data with domain-related and in-domain texts, significantly reducing GPU time while maintaining strong model performance. Our results show that the best configuration of ICL-APT performed better than the state-of-the-art DAPT by 28.7% (7.87 points) and requires almost 4 times less GPU-computing time, providing a cost-effective solution for industries with limited computational capacity. The findings highlight the broader applicability of this framework to other low-resource industries, making NLP-based solutions more accessible and feasible in production environments.
Anastasia Zhukova, Thomas Walton, Christian E. Lobmüller
et al.
In the era of graph-based retrieval-augmented generation (RAG), link prediction is a significant preprocessing step for improving the quality of fragmented or incomplete domain-specific data for the graph retrieval. Knowledge management in the process industry uses RAG-based applications to optimize operations, ensure safety, and facilitate continuous improvement by effectively leveraging operational data and past insights. A key challenge in this domain is the fragmented nature of event logs in shift books, where related records are often kept separate, even though they belong to a single event or process. This fragmentation hinders the recommendation of previously implemented solutions to users, which is crucial in the timely problem-solving at live production sites. To address this problem, we develop a record linking model, which we define as a cross-document coreference resolution (CDCR) task. Record linking adapts the task definition of CDCR and combines two state-of-the-art CDCR models with the principles of natural language inference (NLI) and semantic text similarity (STS) to perform link prediction. The evaluation shows that our record linking model outperformed the best versions of our baselines, i.e., NLP and STS, by 28% (11.43 p) and 27.4% (11.21 p), respectively. Our work demonstrates that common NLP tasks can be combined and adapted to a domain-specific setting of the German process industry, improving data quality and connectivity in shift logs.
This study integrates resource dependence theory and social network theory to explore how two key interlocking network positions-central network position and structural holes-drive corporate green innovation. Using empirical data from listed manufacturing firms in Japan between 2013 and 2019, a country renowned for its environmental leadership, the analysis reveals that both central network positions and structural holes significantly enhance the development of green technologies. Furthermore, absorptive capacity and connectivity with industry leaders are identified as moderating factors influencing the relationship between these network positions and green innovation. Distinct from prior research, this study highlights the pivotal role of network positions in fostering corporate green innovation. The insights provided are valuable for executives looking to enhance their firms’ green innovation performance and for policymakers committed to advancing eco-innovation.
The global patent application count has steadily increased, achieving eight consecutive years of growth.The global patent industry has shown a general trend of expansion. This is attributed to the increasing innovation activities, particularly in the fields of technology, healthcare, and biotechnology. Some emerging market countries, such as China and India, have experienced significant growth in the patent domain, becoming important participants in global patent activities.