Hasil untuk "Industrial relations"

D. Sanders, Z. Smith, R. Guo et al.

Abstract Over the past three decades, polymeric gas separation membranes have become widely used for a variety of industrial gas separations applications. This review presents the fundamental scientific principles underpinning the operation of polymers for gas separations, including the solution-diffusion model and various structure/property relations, describes membrane fabrication technology, describes polymers believed to be used commercially for gas separations, and discusses some challenges associated with membrane materials development. A description of new classes of polymers being considered for gas separations, largely to overcome existing challenges or access applications that are not yet practiced commercially, is also provided. Some classes of polymers discussed in this review that have been the focus of much recent work include thermally rearranged (TR) polymers, polymers of intrinsic microporosity (PIMs), room-temperature ionic liquids (RTILs), perfluoropolymers, and high-performance polyimides.

A. Gandini

What are the distinctive traits that characterize work(ing) through (and for) a digital platform? In the burgeoning debate on the ‘gig economy’, a critical examination that comprehensively addresses this issue beyond specific examples or case studies is currently missing. This article uses labour process theory – an important Marxist approach in the study of relations of production in industrial capitalism – to address this gap. Supported by empirical illustrations from existing research, the article discusses the notions of ‘point of production’, emotional labour and control in the gig economy to argue that labour process theory offers a unique set of tools to expand our understanding of the way in which labour power comes to be transformed into a commodity in a context where the encounter between supply and demand of work is mediated by a digital platform, and where feedback, ranking and rating systems serve purposes of managerialization and monitoring of workers.

G. Tzeng, Cheng-Hsin Chiang, Chung-Wei Li

K. Legge

C. O'Reilly, D. Caldwell, W. P. Barnett

R. Jackall

M. Storper

L. James, J. Brett

R. Gibbons

Chuen-Chien Lee

E. Kossek, Brenda A. Lautsch, S. Eaton

T. Judge, D. Cable

J. Barceló, C. Poschenrieder

B. Palier, K. Thelen

C. Burke, Kevin C. Stagl, E. Salas et al.

R. Pond

Mengyang Zhao, Teng Fu, Haiyang Yu et al.

Few-Shot Industrial Anomaly Detection (FS-IAD) has important applications in automating industrial quality inspection. Recently, some FS-IAD methods based on Large Vision-Language Models (LVLMs) have been proposed with some achievements through prompt learning or fine-tuning. However, existing LVLMs focus on general tasks but lack basic industrial knowledge and reasoning capabilities related to FS-IAD, making these methods far from specialized human quality inspectors. To address these challenges, we propose a unified framework, IADGPT, designed to perform FS-IAD in a human-like manner, while also handling associated localization and reasoning tasks, even for diverse and novel industrial products. To this end, we introduce a three-stage progressive training strategy inspired by humans. Specifically, the first two stages gradually guide IADGPT in acquiring fundamental industrial knowledge and discrepancy awareness. In the third stage, we design an in-context learning-based training paradigm, enabling IADGPT to leverage a few-shot image as the exemplars for improved generalization to novel products. In addition, we design a strategy that enables IADGPT to output image-level and pixel-level anomaly scores using the logits output and the attention map, respectively, in conjunction with the language output to accomplish anomaly reasoning. To support our training, we present a new dataset comprising 100K images across 400 diverse industrial product categories with extensive attribute-level textual annotations. Experiments indicate IADGPT achieves considerable performance gains in anomaly detection and demonstrates competitiveness in anomaly localization and reasoning. We will release our dataset in camera-ready.

S. Shen, Z. Lin, W. Liu et al.

Industrial dashboards, commonly deployed by organizations such as enterprises and governments, are increasingly crucial in data communication and decision-making support across various domains. Designing an industrial dashboard prototype is particularly challenging due to its visual complexity, which can include data visualization, layout configuration, embellishments, and animations. Additionally, in real-world industrial settings, designers often encounter numerous constraints. For instance, when companies negotiate collaborations with clients and determine design plans, they typically need to demo design prototypes and iterate on them based on mock data quickly. Such a task is very common and crucial during the ideation stage, as it not only helps save developmental costs but also avoids data-related issues such as lengthy data handover periods. However, existing authoring tools of dashboards are mostly not tailored to such prototyping needs, and motivated by these gaps, we propose DashChat, an interactive system that leverages large language models (LLMs) to generate industrial dashboard design prototypes from natural language. We collaborated closely with designers from the industry and derived the requirements based on their practical experience. First, by analyzing 114 high-quality industrial dashboards, we summarized their common design patterns and inject the identified ones into LLMs as reference. Next, we built a multi-agent pipeline powered by LLMs to understand textual requirements from users and generate practical, aesthetic prototypes. Besides, functionally distinct, parallel-operating agents are created to enable efficient generation. Then, we developed a user-friendly interface that supports text-based interaction for generating and modifying prototypes. Two user studies demonstrated that our system is both effective and efficient in supporting design prototyping.

Solar Jin

Accelerating the deep transformation and upgrading of industrial structure and forming new quality productive forces are essential components for China to achieve the great rejuvenation of the Chinese Dream. After more than 40 years of rapid development, China has entered the "new normal" of development, making the advancement of new quality productive forces an urgent task. This paper reviews the evolution of China's industrial structure, argues the necessity for a new round of deep industrial transformation, and explores the impact of industrial structure transformation and upgrading on the level of new quality productive forces using various methods. The research findings are as follows:(1)The deep transformation and upgrading of the industrial structure can significantly promote the development of new quality productive forces, but there are obvious regional differences.(2)The core indicator of the improvement in the level of new quality productive forces is the enhancement of total factor productivity. Furthermore, this paper summarizes past industrial development processes and the challenges faced, and analyzes and discusses the potential challenges that may arise in promoting the development of new quality productive forces through deep industrial structure transformation, based on empirical research results.

Marcos Soto

In industrial environments, data acquisition accuracy is crucial for process control and optimization. Wireless telemetry has proven to be a valuable tool for improving efficiency in well-testing operations, enabling bidirectional communication and real-time control of downhole tools. However, high sampling frequencies present challenges in telemetry, including data storage, transmission, computational resource consumption, and battery life of wireless devices. This study explores how optimizing data acquisition strategies can reduce aliasing effects and systematic errors while improving sampling rates without compromising measurement accuracy. A reduction of 80% in sampling frequency was achieved without degrading measurement quality, demonstrating the potential for resource optimization in industrial environments.