Hasil untuk "Industry"

K. Shahzad, S. Bajwa, Shahid A. Zia

Weijan Shan, Gordon Walker, B. Kogut

J. DiMasi, R. Hansen, H. Grabowski et al.

E. H. Kim, Vijay Singal

Donald E. Hatfield, J. Liebeskind, T. Opler

Alphaeus Dmonte, Vidhi Gupta, Daniel J Perry et al.

Fine-tuning a task-specific multilingual large language model (LLM) involves training the model on a multilingual dataset with examples in all the required languages. Updating one or more supported languages with additional data or adding support for a new language involves retraining the model, which can be computationally inefficient and creates a severe maintenance bottleneck. Recent research on merging multilingual multitask models has shown promise in terms of improved quality, but its computational and maintenance efficiency remains unstudied. In this work, we provide the first focused analysis of this merging strategy from an efficiency perspective, evaluating it across three independent tasks. We demonstrate significant efficiency gains while maintaining parity in terms of quality: this merging approach reduces the initial training time by up to 50\%. We also demonstrate that updating an individual language and re-merging as part of model maintenance reduces training costs by more than 60\%, compared to re-training the full multilingual model. We show this on both public and proprietary industry datasets confirming that the approach works well for industrial use cases in addition to academic settings already studied in previous work.

L. Viikari, A. Kantelinen, J. Sundquist et al.

D. Irwin, Peter J. Klenow

Mengling Zhang, Mengling Zhang, Yunlei Li et al.

Although studies have investigated Solanum nigrum L. (SNL) in mice, its effects on broilers remain unclear. This study examined how dietary SNL influences growth performance, antioxidant capacity, ileal transcriptome, and gut microbiota in broilers. A total of 200 one-day-old healthy Wuhua yellow-feathered chickens were randomly divided into four groups of five replicates (10 birds each). The groups received: a basal diet (CON), a basal diet with 500 mg/kg amoxicillin (AMO), a basal diet with 1000 mg/kg SNL grass meal (0.1% SNL), and a basal diet with 2000 mg/kg SNL grass meal (0.2% SNL). The experiment lasted 35 days. SNL supplementation modestly improved feed efficiency and jejunal villus height (p = 0.019). It also altered cecal microbiota by increasing Bacteroidetes, Bacteroides, and Faecalibacterium, while decreasing Firmicutes and Oscillibacter. Ileal transcriptomics identified multiple differentially expressed genes (DEGs) across comparisons, which were enriched in intestinal immune network pathways for IgA production. Correlation analysis linked cecal microbiota changes to ileal gene expression. In conclusion, SNL exhibits the potential as an alternative to antibiotics in chickens, and this study provides empirical support for its broader adoption in poultry industry.

Nan Sun, Nan Sun, Yuxin Wang et al.

IntroductionThis study adopted the intercropping pattern opepper (Capsicum annuum L.) and Chinese chives (Allium tuberosum), combined with high-throughput sequencing and microbial network analysis, to systematically reveal the mechanisms of intercropping on the structural regulation and functional synergy of the crop rhizosphere microbiome and root-stem endophyte communities.MethodsThree treatments were set up: blank control, solo cultivation, and intercropping.Combined with high-throughput sequencing and network analysis, the reorganization patterns of rhizosphere and endophyte communities were systematically analyzed.ResultsIntercropping induced differential responses of microbial communities in the two crops: it significantly increased the bacterial α-diversity in Chinese chives leaves, and the Shannon index of pepper roots also showed an upward trend, while the microbial diversity in pepper rhizosphere soil was inhibited. In contrast, among roots, the “pepper intercropped with Chinese chives” group had the highest total number of OTUs and the largest number of unique OTUs. Microbial communities exhibited cross-host transfer characteristics: the migration rate of microbial communities from pepper roots to Chinese chives rhizosphere reached 46.57%, and 69.54% of the microbial communities in Chinese chives roots originated from pepper roots. Specifically, Aureimonas and Sphingomonadaceae were significantly enriched in pepper leaves, the relative abundance of Pantoea in Chinese chives leaves increased by 11.5 times, and the abundance of Flavobacterium in pepper rhizosphere increased by 94%. Microbial co-occurrence network analysis confirmed the optimization of functional synergy: the proportion of positive interactions in pepper leaves increased to 90.45%, and the negative interactions of Bradyrhizobium decreased by 97%, the proportion of positive interactions of functional bacteria in Chinese chives rhizosphere reached 88.96%, and Bacillus enhanced positive connections while maintaining an abundance of 10.23%–20.87%, the number of positive interactions of Streptomyces in pepper rhizosphere doubled. Network stability showed spatial variation: the robustness of stem microbial networks was significantly improved, while the vulnerability of rhizosphere microbial networks increased.DiscussionThis study provides microbial theoretical support for the intercropping system to optimize nitrogen utilization by driving pepper to enrich the growth-promoting bacteria Sphingomonadaceae, and to enhance disease resistance by promoting Chinese chives to recruit the biocontrol bacteria Bacillus, thereby forming a microecological regulation mechanism with functional complementarity.

Dongyi Yi, Guibo Zhu, Chenglin Ding et al.

With the rapid advancement of Multimodal Large Language Models (MLLMs), numerous evaluation benchmarks have emerged. However, comprehensive assessments of their performance across diverse industrial applications remain limited. In this paper, we introduce MME-Industry, a novel benchmark designed specifically for evaluating MLLMs in industrial settings.The benchmark encompasses 21 distinct domain, comprising 1050 question-answer pairs with 50 questions per domain. To ensure data integrity and prevent potential leakage from public datasets, all question-answer pairs were manually crafted and validated by domain experts. Besides, the benchmark's complexity is effectively enhanced by incorporating non-OCR questions that can be answered directly, along with tasks requiring specialized domain knowledge. Moreover, we provide both Chinese and English versions of the benchmark, enabling comparative analysis of MLLMs' capabilities across these languages. Our findings contribute valuable insights into MLLMs' practical industrial applications and illuminate promising directions for future model optimization research.

Abdikarim Mohamed Ibrahim, Rosdiadee Nordin, Yahya S. M. Khamayseh et al.

The evolution from Industry 4.0 to Industry 5.0 introduces stringent requirements for ultra reliable low latency communication (URLLC) to support human centric, intelligent, and resilient industrial systems. Sixth-generation (6G) wireless networks aim to meet these requirements through sub-millisecond end-to-end delays, microsecond level jitter, and near perfect reliability, enabled by advances such as terahertz (THz) communication, reconfigurable intelligent surfaces (RIS), multi-access edge computing (MEC), and AI driven cross layer optimization. This paper presents a comprehensive review of URLLC solutions for 6G enabled industry 5.0, organized into a structured taxonomy including application domains, key technical enablers, design challenges, and performance enhancements. The survey examines emerging approaches, including digital twin integration, AI/ML based resource orchestration, Network Function Virtualization (NFV) enabled service function chaining, and cross domain networking, while mapping them to critical industrial scenarios such as smart manufacturing, connected healthcare, autonomous mobility, remote control, and next-generation mobile networks. Performance trade-offs between latency, reliability, scalability, and energy efficiency are analyzed in the context of representative state-of-the-art studies. Finally, the paper identifies open challenges and outlines future research directions to realize deterministic, secure, and sustainable URLLC architectures for Industry 5.0.

Felix Glawe, Laura Kremer, Luisa Vervier et al.

Collaborative robots (cobots) are a core technology of Industry 4.0. Industry 4.0 uses cyber-physical systems, IoT and smart automation to improve efficiency and data-driven decision-making. Cobots, as cyber-physical systems, enable the introduction of lightweight automation to smaller companies through their flexibility, low cost and ability to work alongside humans, while keeping humans and their skills in the loop. Industry 5.0, the evolution of Industry 4.0, places the worker at the centre of its principles: The physical and mental well-being of the worker is the main goal of new technology design, not just productivity, efficiency and safety standards. Within this concept, human trust in cobots and human autonomy are important. While trust is essential for effective and smooth interaction, the workers' perception of autonomy is key to intrinsic motivation and overall well-being. As failures are an inevitable part of technological systems, this study aims to answer the question of how system failures affect trust in cobots as well as human autonomy, and how they can be recovered afterwards. Therefore, a VR experiment (n = 39) was set up to investigate the influence of a cobot failure and its severity on human autonomy and trust in the cobot. Furthermore, the influence of transparent communication about the failure and next steps was investigated. The results show that both trust and autonomy suffer after cobot failures, with the severity of the failure having a stronger negative impact on trust, but not on autonomy. Both trust and autonomy can be partially restored by transparent communication.

Antoine Houssard

In the field of artificial intelligence (AI) research, there seems to be a rapprochement between academics and industrial forces. The aim of this study is to assess whether and to what extent industrial domination in the field as well as the ever more frequent switch between academia and industry resulted in the adoption of industrial norms and practices by academics. Using bibliometric information and data on scientific code, we aimed to understand academic and industrial researchers' practices, the way of choosing, investing, and succeeding across multiple and concurrent artifacts. Our results show that, although both actors write papers and code, their practices and the norms guiding them differ greatly. Nevertheless, it appears that the presence of industrials in academic studies leads to practices leaning toward the industrial side, but also to greater success in both artifacts, suggesting that if convergence is, then it is passing through those mixed teams rather than through pure academic or industrial studies.

G. Walker, B. Kogut

Alexander Windmann, Philipp Wittenberg, Marvin Schieseck et al.

In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.

Lukas Walter, Clemens Sauerwein, Daniel W. Woods

Security conferences are important venues for information sharing, where academics and practitioners share knowledge about new attacks and state-of-the-art defenses. Despite their importance, researchers have not systematically examined who shares information and which security topics are discussed. To address this gap, our paper characterizes the speakers, sponsors, and topics presented at prestigious academic and industry security conferences. We compile a longitudinal dataset containing 9,728 abstracts and 1,686 sponsors across four academic and six industry conferences. Our findings show limited information sharing between industry and academia. Conferences vary significantly in how equitably talks and authorship are distributed across individuals. The topics of academic and industry abstracts display consistent coverage of techniques within the MITRE ATT&CK framework. Top-tier academic conferences, as well as DEFCON and Black Hat, address the governance, response, and recovery functions of the NIST Cybersecurity Framework inconsistently. Commercial information security and insurance conferences (RSA, Gartner, Advisen and NetDiligence) more consistently cover the framework. Prevention and detection were the most common topics in the sample period, with no clear temporal trends.

M. Sarvary

Gongjin Lan, Qi Hao

This paper aims to provide a quick review of the methods including the technologies in detail that are currently reported in industry and academia. Specifically, this paper reviews the end-to-end planning, including Tesla FSD V12, Momenta 2023, Horizon Robotics 2023, Motional RoboTaxi 2022, Woven Planet (Toyota): Urban Driver, and Nvidia. In addition, we review the state-of-the-art academic studies that investigate end-to-end planning of autonomous driving. This paper provides readers with a concise structure and fast learning of state-of-the-art end-to-end planning for 2022-2023. This article provides a meaningful overview as introductory material for beginners to follow the state-of-the-art end-to-end planning of autonomous driving in industry and academia, as well as supplementary material for advanced researchers.

Abdullahi Saka, Ridwan Taiwo, Nurudeen Saka et al.

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.