Hasil untuk "Industry"

Nick Bloom, J. V. Reenen

R. Florida

N. Bontis, William Chua, Chong Keow et al.

D. Miracle

T. Bresnahan

R. Parrino

A. Klevorick, R. Levin, R. Nelson et al.

M. Nagamachi

Yukun Zheng, Yachao Zhao, Xuanbo Liu et al.

High-strength oriented PET products were prepared via the confined drawing process. The aggregation structure changes and orientation behavior of PET with different initial crystallinities (before and after drawing) were investigated by TMDSC, WAXD, and SAXS. Combined with the three-phase structure model, the generation and evolution process of highly-ordered rigid amorphous fraction (RAF) were further confirmed. Meanwhile, the effect of drawing temperature on the structure and properties of oriented PET was systematically explored. Consequently, below the cold crystallization temperature, limited mobility of crystalline regions results in two key evolutionary behaviors of PET during drawing: the transformation of the amorphous phase into highly-ordered RAF, and stress-induced crystallization under tensile force. Additionally, the annealing temperature is another key factor influencing the microstructural evolution of semi-crystalline PET. The temperature at which the crystalline phase forms, it directly determines the mobility of the crystalline regions. The characterization results from TMDSC and X-ray techniques quantitatively analyzed the composition and variation law of the aggregation structure, providing guidance for the structure-property regulation of high-strength oriented PET.

Sander De Coninck, Emilio Gamba, Bart Van Doninck et al.

The adoption of AI-powered computer vision in industry is often constrained by the need to balance operational utility with worker privacy. Building on our previously proposed privacy-preserving framework, this paper presents its first comprehensive validation on real-world data collected directly by industrial partners in active production environments. We evaluate the framework across three representative use cases: woodworking production monitoring, human-aware AGV navigation, and multi-camera ergonomic risk assessment. The approach employs learned visual transformations that obscure sensitive or task-irrelevant information while retaining features essential for task performance. Through both quantitative evaluation of the privacy-utility trade-off and qualitative feedback from industrial partners, we assess the framework's effectiveness, deployment feasibility, and trust implications. Results demonstrate that task-specific obfuscation enables effective monitoring with reduced privacy risks, establishing the framework's readiness for real-world adoption and providing cross-domain recommendations for responsible, human-centric AI deployment in industry.

Louise McCormack, Diletta Huyskes, Dave Lewis et al.

The EU Artificial Intelligence (AI) Act directs businesses to assess their AI systems to ensure they are developed in a way that is human-centered and trustworthy. The rapid adoption of AI in the industry has outpaced ethical evaluation frameworks, leading to significant challenges in accountability, governance, data quality, human oversight, technological robustness, and environmental and societal impacts. Through structured interviews with fifteen industry professionals, paired with a literature review conducted on each of the key interview findings, this paper investigates practical approaches and challenges in the development and assessment of Trustworthy AI (TAI). The findings from participants in our study, and the subsequent literature reviews, reveal complications in risk management, compliance and accountability, which are exacerbated by a lack of transparency, unclear regulatory requirements and a rushed implementation of AI. Participants reported concerns that technological robustness and safety could be compromised by model inaccuracies, security vulnerabilities, and an overreliance on AI without proper safeguards in place. Additionally, the negative environmental and societal impacts of AI, including high energy consumption, political radicalisation, loss of culture and reinforcement of social inequalities, are areas of concern. There is a pressing need not just for risk mitigation and TAI evaluation within AI systems but for a wider approach to developing an AI landscape that aligns with the social and cultural values of the countries adopting those technologies.

Rinieri Lorenzo, Gori Giacomo, Melis Andrea et al.

The OPC UA protocol is becoming the de facto standard for Industry 4.0 machine-to-machine communication. It stands out as one of the few industrial protocols that provide robust security features designed to prevent attackers from manipulating and damaging critical infrastructures. However, prior works showed that significant challenges still exists to set up secure OPC UA deployments in practice, mainly caused by the complexity of certificate management in industrial scenarios and the inconsistent implementation of security features across industrial OPC UA devices. In this paper, we present Pk-IOTA, an automated solution designed to secure OPC UA communications by integrating programmable data plane switches for in-network certificate validation and leveraging the IOTA Tangle for decen- tralized certificate distribution. Our evaluation is performed on a physical testbed representing a real-world industrial scenario and shows that Pk-IOTA introduces a minimal overhead while providing a scalable and tamper-proof mechanism for OPC UA certificate management.

Michelle H. Lee, Eveline Febriana, Maybritte Lim et al.

Summary: Background: Postprandial glucose concentration 1-h (1 h-PG) after an oral glucose tolerance test (OGTT) has similar or superior performance to 2 h-PG in predicting type-2 diabetes mellitus (T2DM) in several populations, and is simpler to obtain in clinical practice. However, studies in Asians are scarce. We investigated the utility of elevated baseline 1 h-PG in predicting T2DM incidence within three years, and its relationship with β-cell function in 1250 non-diabetic Asian participants. Methods: Participants underwent an OGTT, an intravenous glucose challenge and a hyperinsulinemic-euglycemic clamp to determine glucose tolerance, acute insulin response (AIR) and insulin sensitivity at baseline. OGTTs were repeated every six months until study completion to monitor T2DM conversion. Findings: The area under the receiver operating characteristic curve of 1 h-PG was not significantly different from 2 h-PG (AUC1h-PG = 0.883 vs. AUC2h-PG = 0.907; ΔAUC = −0.024, P = 0.124) and the optimal 1 h-PG cut-off was ≥10.7 mmol/L. When groups of high/low 1 h-PG and 2 h-PG at baseline were compared, AIR and disposition index were significantly lower in groups with high 1 h-PG, and both had a stronger correlation with 1 h-PG, indicating that impaired β-cell function was more strongly associated with elevated 1 h-PG than 2 h-PG. Interpretation: The ability of 1 h-PG to detect Asians at risk of developing T2DM within three years is on par with 2 h-PG and the optimal cut-off is 10.7 mmol/L. Elevated 1 h-PG is associated with β-cell dysfunction. We conclude that 1 h-PG can be considered as a primary OGTT time point to identify Asians at risk for T2DM, allowing for screening at a reduced time and cost, and with lower patient burden. Funding: National Medical Research Council (NMRC), Ministry of Health (MOH; Singapore) Industry Alignment Fund [NMRC/MOHIAFCat1/0048/2016] and Janssen Pharmaceuticals Inc. (USA).

Zhiyu Xiong, Yuan Yuan, Bopeng Qiu et al.

Abstract Background Sleep quality decreased can result in a major health issue in older people with age. While not all sleep changes are pathological in older people’s life, severe disturbances may lead to depression, cognitive impairments, deterioration of quality of life, significant stresses for careers and increased healthcare costs. Despite the known benefits of exercise for improving sleep quality, it is necessary to identify the optimal exercise type and dose. Objective This systematic review and network meta-analysis (NMA) combined to examine evaluated the existing evidence on the effectiveness of different exercises, and to examine the dose and response relationship between overall and specific types with improving sleep quality in older people. Methods PubMed, Cochrane Central, Web of Science, and Embase were systematically searched for this review, including studies up to April 2025. Only randomized controlled trials were included. Studies involved at least one type of exercise intervention and reported changes in sleep quality assessments. To address the limitations of relying solely on statistical significance, we also calculated the minimal clinically important difference (MCID) to determine the smallest meaningful improvement in sleep quality among older people, both overall and across different exercise doses. Data analysis and visualization were conducted using the “meta”, “netmeta”, “MBNMA”, and “ggplot2” packages in the R environment. Results A total of 62 RCTs involving 5005 older adults were included. Overall, exercise significantly improved sleep quality, with clinically meaningful improvements achieved from as early as 5 weeks of intervention. The optimal exercise type was combined aerobic and resistance training, followed by aerobic exercise, resistance training, walking, and yoga. The estimated optimal exercise dose was around 660 to 990 METs*min/week, with longer durations at 15 weeks producing the greatest benefits. Improvements were more pronounced among participants with poorer baseline sleep quality. Conclusion If older people receive the most appropriate exercise intervention, they can obtain clinically meaningful benefits of improving sleep in the elderly within the WHO guidelines for exercise doses. The results support the WHO recommendation that combine aerobic exercise and resistance training should be an important part of interventions for the older people. Protocol registration PROSPERO registration number: CRD42024566751. Graphical Abstract

Sreelatha Aihloor Subramanyam, Sina Ghaemi, Hessam Golmohamadi et al.

Flexibility is advocated as an effective solution to address the growing need to alleviate grid congestion, necessitating efficient energy management strategies for industrial operations. This paper presents a mixed-integer linear programming (MILP)-based optimization framework for a flexible asset in an industrial setting, aiming to minimize operational costs and enhance energy efficiency. The method integrates dynamic pricing and real-time grid analysis, alongside a state estimation model using Extended Kalman Filtering (EKF) that improves the accuracy of system state predictions. Model Predictive Control (MPC) is employed for real-time adjustments. A real-world case studies from aquaculture industries and industrial power grids in Denmark demonstrates the approach. By leveraging dynamic pricing and grid signals, the system enables adaptive pump scheduling, achieving a 27% reduction in energy costs while maintaining voltage stability within 0.95–1.05 p.u. and ensuring operational safety. These results confirm the effectiveness of grid-aware, flexible control in reducing costs and enhancing stability, supporting the transition toward smarter, sustainable industrial energy systems.

Bo Zhang, Heye Huang, Chunyang Liu et al.

End-to-end autonomous driving, with its holistic optimization capabilities, has gained increasing traction in academia and industry. Vectorized representations, which preserve instance-level topological information while reducing computational overhead, have emerged as promising paradigms. However, existing vectorized query-based frameworks often overlook the inherent spatial correlations among intra-instance points, resulting in geometrically inconsistent outputs (e.g., fragmented HD map elements or oscillatory trajectories). To address these limitations, we propose intra-instance vectorized driving transformer (InVDriver), a novel vectorized query-based system that systematically models intra-instance spatial dependencies through masked self-attention layers, thereby enhancing planning accuracy and trajectory smoothness. Across all core modules, i.e., perception, prediction, and planning, InVDriver incorporates masked self-attention mechanisms that restrict attention to intra-instance point interactions, enabling coordinated refinement of structural elements while suppressing irrelevant inter-instance noise. The experimental results on the nuScenes benchmark demonstrate that InVDriver achieves state-of-the-art performance, surpassing prior methods in both accuracy and safety, while maintaining high computational efficiency.

Naomi Hull, Anusha Bradley, Monique Boatwright et al.

ABSTRACT In 2024, the New Zealand (NZ) government made a rare departure from the joint food standards programme with Australia, administered by Food Standards Australia New Zealand (FSANZ). This paper presents a timely case study of how transnational dairy and baby food corporations lobbied the NZ government to reject updated infant formula standards, despite strong evidence and support across Australia for reform. Globally, transnational corporations dominate commercial milk formula industry, and industry and utilise lobbying strategies to delay and limit regulation. Drawing on original data from official information act requests, we examine the political dynamics surrounding infant formula regulation and the implications for breastfeeding protection and health governance in the region. Despite FSANZ's evidence‐based decisions to improve labelling, restrict health claims, and enhance consumer protection, NZ bowed to the lobbying pressure of key companies who had cited risks to exports, jobs and future product development. Lobbying by these companies targeted the Prime Minister and key ministers, demonstrating a remarkable level of access and influence. This case exposes the weaknesses in NZ's political transparency laws, where no mandatory lobbying registers and reporting requirements exist. We conclude that it is crucial for governments to make policy decisions without the influence of the baby food industry and provide a strong argument for better regulation of corporate lobbying. Infant and young child health must be prioritised over profit.

Saddaf Razzaq, Aayesha Riaz, Naila Siddique et al.

Abstract Newcastle disease virus (NDV) remains a major threat to the poultry industry worldwide. Recombinant DNA vaccine against NDV offers a promising solution to current Newcastle disease (ND) challenges. Present study describes the development of a DNA vaccine (rDNA-NDV-F) using the fusion (F) gene from NDV genotype VII strain isolated from Rawalpindi, Pakistan. While conventional NDV vaccines reduce mortality in commercial poultry, they do not provide complete protection or prevent viral shedding. To address this issue, genotype-matched vaccines have been proposed. Here, we developed and evaluated the efficacy of the rDNA-NDV-F vaccine against genotype VII challenge. NDV was isolated from a field strain and propagated in embryonated chicken eggs (ECE). Virus activity was confirmed using Hemagglutination assay (HA), HA inhibition (HAI), and Mean Death Time (MDT) assay. Polymerase Chain Reaction (PCR) and sequencing confirmed the genotype VII.2 strain. The DNA vaccine was constructed using the fusion (F) protein gene cloned into the expression plasmid pcDNA3.1. Gene insertion was verified by PCR and restriction digestion, while protein expression was confirmed via immunofluorescence assay. To assess vaccine efficacy, 120 chickens (14 days old) were divided into four groups: G1 (rDNA-NDV-F), G2 (empty vector), G3 (PBS control), and G4 (non-vaccinated, non-challenged control). Serological responses were measured using ELISA on days 0, 7, 14, 21, and 28. Birds were challenged with NDV genotype VII (105 EID50). Virus shedding from tracheal and cloacal swabs was analyzed on days 3, 7, and 10 post-challenge. Clinical signs and mortality rates were also recorded. The rDNA-NDV-F vaccine induced strong immune responses, with peak ELISA (6180) titers at 28 days. Virus shedding was detected in three birds on day 3 but was absent by day 10. No virus shedding was observed in cloacal swabs, indicating restriction in the digestive system. Vaccinated birds showed mild clinical signs in only two cases, with no neurological symptoms or mortality. In contrast, negative and vector control groups exhibited severe clinical signs and 90–100% mortality. Statistical analysis confirmed significant differences (P < 0.05). This study highlights the effectiveness of genotype-matched recombinant NDV vaccines in providing effective protection for poultry.

Hambur Wang

This study investigates the impact of industrial agglomeration on land use intensification in the Yangtze River Delta (YRD) urban agglomeration. Utilizing spatial econometric models, we conduct an empirical analysis of the clustering phenomena in manufacturing and producer services. By employing the Location Quotient (LQ) and the Relative Diversification Index (RDI), we assess the degree of industrial specialization and diversification in the YRD. Additionally, Global Moran's I and Local Moran's I scatter plots are used to reveal the spatial distribution characteristics of land use intensification. Our findings indicate that industrial agglomeration has complex effects on land use intensification, showing positive, negative, and inverted U-shaped impacts. These synergistic effects exhibit significant regional variations across the YRD. The study provides both theoretical foundations and empirical support for the formulation of land management and industrial development policies. In conclusion, we propose policy recommendations aimed at optimizing industrial structures and enhancing land use efficiency to foster sustainable development in the YRD region.

Zhipeng Ma, Bo Nørregaard Jørgensen, Michelle Levesque et al.

Digitalization is challenging in heavy industrial sectors, and many pi-lot projects facing difficulties to be replicated and scaled. Case studies are strong pedagogical vehicles for learning and sharing experience & knowledge, but rarely available in the literature. Therefore, this paper conducts a survey to gather a diverse set of nine industry cases, which are subsequently subjected to analysis using the business model canvas (BMC). The cases are summarized and compared based on nine BMC components, and a Value of Business Model (VBM) evaluation index is proposed to assess the business potential of industrial digital solutions. The results show that the main partners are industry stakeholders, IT companies and academic institutes. Their key activities for digital solutions include big-data analysis, machine learning algorithms, digital twins, and internet of things developments. The value propositions of most cases are improving energy efficiency and enabling energy flexibility. Moreover, the technology readiness levels of six industrial digital solutions are under level 7, indicating that they need further validation in real-world environments. Building upon these insights, this paper proposes six recommendations for future industrial digital solution development: fostering cross-sector collaboration, prioritizing comprehensive testing and validation, extending value propositions, enhancing product adaptability, providing user-friendly platforms, and adopting transparent recommendations.