Hasil untuk "Industry"

Xiang Cai, Bangzhu Zhu, Haijing Zhang et al.

Faced with the dual constraints of resources and the environment, green technology innovation has become an important measure to solve the development challenges of heavily polluting industries. From the perspective of institutional regulation theory, this paper studies the impact of direct environmental regulation on green technology innovation in Chinese listed companies of heavily polluting industries by using the Panel Poisson fixed effect model. Besides, the heterogeneity of ownership and industry is discussed. The results indicate that direct environmental regulations exert a strong and significant incentive effect on green technology innovations in heavily polluting industries. Regarding the heterogeneity of enterprise ownership, direct environmental regulations were found to be more significant to the green technology innovations of state-owned listed companies in such industries. Considering industry heterogeneity, compared with labor-resource intensive industries, direct environmental regulation can effectively encourage green technology innovations in technology-capital intensive industries. This study provides a policy basis for promoting environmental governance and green technology innovation in China's heavily polluting industries.

R. C. Kuhad, Rishi Gupta, A. Singh

Microbial cellulases have shown their potential application in various industries including pulp and paper, textile, laundry, biofuel production, food and feed industry, brewing, and agriculture. Due to the complexity of enzyme system and immense industrial potential, cellulases have been a potential candidate for research by both the academic and industrial research groups. Nowadays, significant attentions have been devoted to the current knowledge of cellulase production and the challenges in cellulase research especially in the direction of improving the process economics of various industries. Scientific and technological developments and the future prospects for application of cellulases in different industries are discussed in this paper.

Mercedes Delgado, Michael E. Porter, Scott Stern

This paper examines the role of regional clusters in regional entrepreneurship. We focus on the distinct influences of convergence and agglomeration on growth in the number of start-up firms as well as in employment in these new firms in a given region-industry. While reversion to the mean and diminishing returns to entrepreneurship at the region-industry level can result in a convergence effect, the presence of complementary economic activity creates externalities that enhance incentives and reduce barriers for new business creation. Clusters are a particularly important way through which location-based complementarities are realized. The empirical analysis uses a novel panel dataset from the Longitudinal Business Database of the Census Bureau and the U.S. Cluster Mapping Project (Porter, 2003). Using this dataset, there is significant evidence of the positive impact of clusters on entrepreneurship. After controlling for convergence in start-up activity at the region-industry level, industries located in regions with strong clusters (i.e. a large presence of other related industries) experience higher growth in new business formation and start-up employment. Strong clusters are also associated with the formation of new establishments of existing firms, thus influencing the location decision of multiestablishment firms. Finally, strong clusters contribute to start-up firm survival.

Thomas L. Wheelen, J. Hunger

A. Poon

Tourism, the world s fastest growing industry, is now entering a more mature phase. During the 1970s and 1980s mass tourism, with its rigid, standardized packages, developed rapidly and provided many consumers with their first experiences of international travel. Today, a complex and multi-faceted industry, tourism faces growing pressures - consumer demand for more individually tailored holidays, an increasingly competitive operational environment, opportunities provided by new technology and growing environmental concerns. This book analyzes the major challenges facing tourism today. The author highlights the central role of information technology in creating mass tourism by the mid-1970 s, and how this technology and innovation is creating a new best practice of flexibility, market segmentation and diagonal integration within tourism. The book demonstrates how companies in the industry can enhance their competitiveness in the market place. Aimed at both academics and industry practitioners, this original and challenging work will attract a wide readership."

M. Greenstone

Stroke Therapy Academic Industry Roundtable

Andreas Rausch, Stefan Wittek

Different domains foster different architectural styles -- and thus different documentation practices (e.g., state-based models for behavioral control vs. ER-style models for information structures). Agentic AI systems exhibit another characteristic style: specialized agents collaborate by exchanging artifacts, invoking external tools, and coordinating via recurring interaction patterns and quality gates. As these systems evolve into long-lived industrial solutions, documentation must capture these style-defining concerns rather than relying on ad-hoc code sketches or pipeline drawings. This paper reports industrial experience from joint projects and derives a documentation systematics tailored to this style. Concretely, we provide (i) a style-oriented modeling vocabulary and a small set of views for agents, artifacts, tools, and their coordination patterns, (ii) a hierarchical description technique aligned with C4 to structure these views across abstraction levels, and (iii) industrial examples with lessons learned that demonstrate how the approach yields transparent, maintainable architecture documentation supporting sustained evolution.

Leonard Kleinberger, Michael Gundall, Hans D. Schotten

Industrial 5G deployments using Time Division Duplex (TDD) networks face a critical challenge: existing schedulers rely on static configuration of Uplink (UL) to Downlink (DL) resource ratios, failing to adapt to dynamic asymmetric traffic demands. This limitation is particularly problematic in Industry 4.0 scenarios where traffic patterns exhibit significant asymmetry between directions and heterogeneous Quality of Service (QoS) requirements. We present FLEX, a novel QoS-aware scheduler that dynamically adjusts the UL/DL ratio in flexible TDD slots while respecting diverse QoS requirements. FLEX introduces DL buffer state estimation to prevent starvation of high-priority DL traffic, exploiting the deterministic nature of industrial traffic patterns for accurate predictions. Through extensive simulations of industrial scenarios using 5G LENA and ns-3, we demonstrate that FLEX achieves similar throughput compared to established scheduling while correctly enforcing QoS priorities in both traffic directions. For deterministic traffic patterns, FLEX maintains minimal latency overhead (less than 1 slot duration), making it particularly suitable for industrial automation applications.

Chetan Arora, Fanyu Wang, Chakkrit Tantithamthavorn et al.

Requirements engineering (RE) in the space industry is inherently complex, demanding high precision, alignment with rigorous standards, and adaptability to mission-specific constraints. Smaller space organisations and new entrants often struggle to derive actionable requirements from extensive, unstructured documents such as mission briefs, interface specifications, and regulatory standards. In this innovation opportunity paper, we explore the potential of Retrieval-Augmented Generation (RAG) models to support and (semi-)automate requirements generation in the space domain. We present a modular, AI-driven approach that preprocesses raw space mission documents, classifies them into semantically meaningful categories, retrieves contextually relevant content from domain standards, and synthesises draft requirements using large language models (LLMs). We apply the approach to a real-world mission document from the space domain to demonstrate feasibility and assess early outcomes in collaboration with our industry partner, Starbound Space Solutions. Our preliminary results indicate that the approach can reduce manual effort, improve coverage of relevant requirements, and support lightweight compliance alignment. We outline a roadmap toward broader integration of AI in RE workflows, intending to lower barriers for smaller organisations to participate in large-scale, safety-critical missions.

Dan Parii, Evelyne Janssen, Guangzhi Tang et al.

Accurately predicting the lifespan of critical device components is essential for maintenance planning and production optimization, making it a topic of significant interest in both academia and industry. In this work, we investigate the use of survival analysis for predicting the lifespan of production printheads developed by Canon Production Printing. Specifically, we focus on the application of five techniques to estimate survival probabilities and failure rates: the Kaplan-Meier estimator, Cox proportional hazard model, Weibull accelerated failure time model, random survival forest, and gradient boosting. The resulting estimates are further refined using isotonic regression and subsequently aggregated to determine the expected number of failures. The predictions are then validated against real-world ground truth data across multiple time windows to assess model reliability. Our quantitative evaluation using three performance metrics demonstrates that survival analysis outperforms industry-standard baseline methods for printhead lifespan prediction.

Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song et al.

Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendation systems to generative tasks. Although scaling laws indicate that larger models generally yield better generalization and performance, their substantial computational requirements often render them impractical for many real-world scenarios at scale. In this paper, we present a comprehensive set of insights for training and deploying small language models (SLMs) that deliver high performance for a variety of industry use cases. We focus on two key techniques: (1) knowledge distillation and (2) model compression via structured pruning and quantization. These approaches enable SLMs to retain much of the quality of their larger counterparts while significantly reducing training/serving costs and latency. We detail the impact of these techniques on a variety of use cases in a large professional social network platform and share deployment lessons, including hardware optimization strategies that improve speed and throughput for both predictive and reasoning-based applications in Recommendation Systems.

Jiayue He, Yanhua Chen, Yanrong Hao et al.

Drought is a major environmental constraint that significantly affects seedling emergence, yield, and quality of Tartary buckwheat, thereby hindering the development of its industry. However, the molecular mechanisms underlying drought tolerance genes in Tartary buckwheat remain largely unexplored. Alcohol dehydrogenase (ADH), an essential plant protein, plays a crucial role in growth, development, and stress responses; however, its specific role in drought resistance remains unclear. This study identifies an ADH gene, FtADH1, using a membership function value of drought tolerance (MFVD) combined with a genome-wide association study (GWAS) and transcriptomic profiles that confer drought tolerance in Tartary buckwheat. Our findings demonstrated that the overexpression of FtADH1 in Arabidopsis and Tartary buckwheat hairy roots enhances drought tolerance by promoting root elongation and mitigating elevated levels of reactive oxygen species (ROS). Our findings demonstrate that FtADH1 can enhance drought tolerance in Tartary buckwheat and Arabidopsis. This study identifies FtADH1 as a new regulator of Tartary buckwheat’s ROS levels and stress responses, functioning by regulating protective enzyme activities at a high level to scavenge ROS and modulating root growth under drought stress. Further, it identifies proteins interacting with FtADH1 through a prokaryotic expression pull-down assay combined with mass spectrometry, revealing that FtADH1 interacts explicitly with the S-adenosyl-L-methionine (SAM) synthetase protein, FtSAMS1. Overexpression of FtSAMS1 enhances ADH enzymatic activity, leading to increased SAM content in overexpressing Tartary buckwheat hairy roots under water-deficit conditions. Additionally, overexpression of FtSAMS1 induces a drought-resistant phenotype in Arabidopsis and Tartary buckwheat hairy roots under drought stress, revealing the biological function of FtADH1. Evolutionary analysis indicates that ADH1 in Fagopyrum species has undergone significant evolutionary events, including duplication and purifying selection, which may contribute to functional diversification and adaptive advantages such as drought resistance in cultivated buckwheat. In summary, this study suggests that FtADH1 is a key contributor to drought tolerance, and its interaction with FtSAMS1 offers promising potential for developing drought-resistant varieties in Tartary buckwheat and its relative species.

Hadi Sumarto Rumsari, Widayanti Sri, Hendri Wijaya Junior et al.

Pulesari Tourism Village is one of the mainstay tourism villages in Sleman Regency, Yogyakarta. This tourist village had experienced a decrease in the number of tourists. Therefore, it is necessary to strive so that the number of tourists increases again through interaction from the government, industry, universities, the community, and the media. The research emphasizes tourist villages that promote natural tourism, distinguishing it from prior studies that concentrated on cultural tourism within the frameworks of penta helix and quintuple helix models. Research is significant with previous research that raised the theme of the helix. The purpose of this research is to conduct an analysis of Penta Helix, namely the government, universities, industry, community, and media in the development of Pulesari Tourism Village. The research method uses qualitative methods to reveal existing problems so that they can be used as solutions for the welfare of residents. Data collection techniques through observation, interviews, and documentation. Data analysis techniques are carried out through data reduction, data presentation, and conclusion drawn. The results of the study show that the helix of government, industry, universities, citizens, and the media, carry out their respective roles and interact together in an activity. This is in line with the penta helix theory regarding the role of interhelix and the mutual interaction between helixes. However, interaction has not been established in a sustainable manner. The role of society is more dominant than other helixes. Previous research has shown that the role of the community is not dominant.

Yu Chen^*+, Haoyi Liu^*+, Shiyu Liu^*+ et al.

Introduction Adolescent tobacco use has become a serious global public health problem, and effective tobacco control public service advertisements (PSAs) are crucial for reducing adolescent smoking rates. The study aims to employ a mixedmethods approach combining quantitative surveys and qualitative focus groups to evaluate the effectiveness of different types of tobacco control PSAs among Chinese adolescents, identify effective advertising characteristics and content elements, and provide empirical evidence for optimizing youth tobacco control communication strategies. Methods A total of 125 students aged 10–18 years were recruited from six primary and secondary schools in Beijing and Kunming from November 2020 to April 2021. Participants completed Likert-scale ratings measuring advertisement effectiveness after viewing eight tobacco control PSAs and participated in focus group interviews. Quantitative data were analyzed using independent samples t-tests, Spearman correlation analysis, and multivariable logistic regression analysis, while qualitative data were analyzed using thematic analysis. All statistical tests were two-tailed with significance set at p<0.05. Results Quantitative analysis revealed that PSAs employing ‘testimonials’ and ‘disease’ frameworks were most strongly associated with prevention intentions, while those using ‘celebrity endorsement’, ‘humor’ and ‘appearance damage’ frameworks showed the weakest associations. Kunming adolescents showed significantly higher advertisement acceptance scores than Beijing adolescents (mean difference=0.21; 95% CI: 0.04–0.38, p<0.05). The 10-item effectiveness scale demonstrated good internal consistency (Cronbach’s α=0.82). Qualitative analysis identified effective characteristics including presentation of specific health hazards, use of testimonials, and fear appeals; ineffective characteristics included non-specific harm presentation, use of humorous elements, and appearance damage content. Conclusions Tobacco control PSA design should consider strategies combining disease warnings with real-life testimonials, avoid humorous advertisements and industry-sponsored messaging, and consider regional cultural differences. Distribution through online and social media platforms frequently used by adolescents may enhance reach. Future longitudinal research with broader geographical sampling is needed to confirm these findings.

Preetaman Bajwa, Rupinder Saini, Sukhbir Singh et al.

Abstract Industrial hemp is receiving attention for its numerous benefits, particularly in the fiber industry. Weed competition is a primary concern for hemp cultivation, causing reduced yields and inferior‐quality fiber. However, little is known about herbicide application in hemp since a limited range of herbicides are available for hemp production. During 2023, this study evaluated the effect of different post‐emergence herbicides applied at early and late growth stages to optimize weed suppression and minimize crop injury in hemp under semiarid conditions. A randomized complete block design was used with six herbicide treatments, including early post (2 weeks after planting [WAP]) and late post (5 WAP) emergence applications of S‐metolachlor, clopyralid, and ethalfluralin. Hemp plant stand showed no significant difference among treatments. Early post herbicide application reduced 86% of weed biomass compared to untreated control at 7 WAP. By 10 WAP, weed biomass became comparable across treatments. At harvest, untreated control recorded comparatively higher weed biomass than early post treatments and late post ethalfluralin. Plant height remained nonsignificant among treatments until 10 WAP. At harvest, control showed no variation with late post treatments but recorded an average of 63% lower plant height than early post applications. Hemp biomass was insignificantly affected by treatments at 10 WAP. However, on average, early post S‐metolachlor and ethalfluralin applications demonstrated potential as effective herbicides, yielding four‐folds greater hemp biomass than untreated control at harvest. In conclusion, early post S‐metolachlor and ethalfluralin are promising tools for weed control, enhancing crop competitiveness and yield in hemp cultivation.

Artem Lykov, Miguel Altamirano Cabrera, Mikhail Konenkov et al.

This paper presents the concept of Industry 6.0, introducing the world's first fully automated production system that autonomously handles the entire product design and manufacturing process based on user-provided natural language descriptions. By leveraging generative AI, the system automates critical aspects of production, including product blueprint design, component manufacturing, logistics, and assembly. A heterogeneous swarm of robots, each equipped with individual AI through integration with Large Language Models (LLMs), orchestrates the production process. The robotic system includes manipulator arms, delivery drones, and 3D printers capable of generating assembly blueprints. The system was evaluated using commercial and open-source LLMs, functioning through APIs and local deployment. A user study demonstrated that the system reduces the average production time to 119.10 minutes, significantly outperforming a team of expert human developers, who averaged 528.64 minutes (an improvement factor of 4.4). Furthermore, in the product blueprinting stage, the system surpassed human CAD operators by an unprecedented factor of 47, completing the task in 0.5 minutes compared to 23.5 minutes. This breakthrough represents a major leap towards fully autonomous manufacturing.

Dayu Yang

Merger and Acquisition (M&A) activities play a vital role in market consolidation and restructuring. For acquiring companies, M&A serves as a key investment strategy, with one primary goal being to attain complementarities that enhance market power in competitive industries. In addition to intrinsic factors, a M&A behavior of a firm is influenced by the M&A activities of its peers, a phenomenon known as the "peer effect." However, existing research often fails to capture the rich interdependencies among M&A events within industry networks. An effective M&A predictive model should offer deal-level predictions without requiring ad-hoc feature engineering or data rebalancing. Such a model would predict the M&A behaviors of rival firms and provide specific recommendations for both bidder and target firms. However, most current models only predict one side of an M&A deal, lack firm-specific recommendations, and rely on arbitrary time intervals that impair predictive accuracy. Additionally, due to the sparsity of M&A events, existing models require data rebalancing, which introduces bias and limits their real-world applicability. To address these challenges, we propose a Temporal Dynamic Industry Network (TDIN) model, leveraging temporal point processes and deep learning to capture complex M&A interdependencies without ad-hoc data adjustments. The temporal point process framework inherently models event sparsity, eliminating the need for data rebalancing. Empirical evaluations on M&A data from January 1997 to December 2020 validate the effectiveness of our approach in predicting M&A events and offering actionable, deal-level recommendations.

Richard Tong, Haoyang Li, Joleen Liang et al.

The adoption of Artificial Intelligence in Education (AIED) holds the promise of revolutionizing educational practices by offering personalized learning experiences, automating administrative and pedagogical tasks, and reducing the cost of content creation. However, the lack of standardized practices in the development and deployment of AIED solutions has led to fragmented ecosystems, which presents challenges in interoperability, scalability, and ethical governance. This article aims to address the critical need to develop and implement industry standards in AIED, offering a comprehensive analysis of the current landscape, challenges, and strategic approaches to overcome these obstacles. We begin by examining the various applications of AIED in various educational settings and identify key areas lacking in standardization, including system interoperability, ontology mapping, data integration, evaluation, and ethical governance. Then, we propose a multi-tiered framework for establishing robust industry standards for AIED. In addition, we discuss methodologies for the iterative development and deployment of standards, incorporating feedback loops from real-world applications to refine and adapt standards over time. The paper also highlights the role of emerging technologies and pedagogical theories in shaping future standards for AIED. Finally, we outline a strategic roadmap for stakeholders to implement these standards, fostering a cohesive and ethical AIED ecosystem. By establishing comprehensive industry standards, such as those by IEEE Artificial Intelligence Standards Committee (AISC) and International Organization for Standardization (ISO), we can accelerate and scale AIED solutions to improve educational outcomes, ensuring that technological advances align with the principles of inclusivity, fairness, and educational excellence.

María Teresa Gómez-Villarino, María del Mar Barbero-Barrera, Ignacio Cañas et al.

The wine industry requires a considerable amount of energy, with an important fraction corresponding to the cooling and ventilation of above-ground aging warehouses. The large investments made in aging facilities can compromise the viability and competitiveness of wineries if their design is not optimized. The objective of this study was to provide guidance for the efficient design of new above-ground warehouses. To this end, multiple construction solutions (structure, envelopes, levels of integration, etc.) were characterized, and their costs and the resulting interior environments were analyzed. The results offer a comprehensive view of potential construction solutions and benchmark price ranges for viable and profitable designs. With a total cost of 300 EUR/m², an average damping of 98% per day can be achieved. Increasing the costs does not imply better effectiveness. A double enclosure with internal insulation—with or without an air chamber—can achieve excellent results. Greater integration as a result of several enclosures being in contact with other rooms and/or the terrain allows for a high effectiveness to be achieved without air conditioning. Perimeter glazing and ventilation holes can reduce the effectiveness of the construction, resulting in greater instability and a lower damping capacity.