Hasil untuk "Industry"

Birger Wernerfelt, Cynthia A. Montgomery

A. Spence

T. C. Powell

Glenn R Carroll

R. Reger, A. Huff

J. Stopford, C. Baden-Fuller

Joanna E. Cohen

W. H. Thompson, Phillip B. Leege

B. Kogut, Sea-Jin Chang

A. Akintoye, Malcolm J. Macleod

E. Berman, J. Bound, Z. Griliches

V. Maksimovic, G. Phillips

A. Fernandes

Anisha Joseph, S Deepa, Nivya Mariam Paul et al.

Graphene oxide (GO)–incorporated tin oxide (SnO _2 ) nanocomposites have attracted significant attention due to their tunable structural, optical, and biological properties. In this work, pristine and GO-integrated SnO _2 nanocomposites were synthesized via a co-precipitation method with varying GO concentrations to investigate their physicochemical characteristics and antibacterial performance. X-ray diffraction analysis confirmed the formation of a tetragonal cassiterite SnO _2 structure with an average crystallite size of approximately 26 nm. FESEM and TEM studies revealed well-distributed GO–SnO _2 nanocomposites, while HRTEM analysis confirmed their crystalline nature with d-spacing values consistent with XRD results. Raman spectroscopy identified characteristic vibrational modes (A _1 g, B _2 g, and Eg), indicating structural integrity and defect-related features, whereas FTIR spectra exhibited prominent Sn–O stretching vibrations at 604 and 462 cm ^−1 . TGA/DTA analysis demonstrated good thermal stability with minimal weight loss beyond 400 °C. UV–Visible spectroscopy and Tauc analysis revealed a reduction in band gap upon GO incorporation, attributed to the formation of interstitial energy levels and enhanced electronic interaction. EDS analysis showed increased carbon content and notable variations in the Sn–O ratio with increasing GO concentration, suggesting modified surface reactivity. Antibacterial activity evaluated against Staphylococcus aureus , Escherichia coli , Klebsiella sp., and Pseudomonas sp. showed a significant enhancement for GO-integrated SnO _2 samples, with a maximum increase of approximately 66.7% against Pseudomonas sp and 37.5% against E.coli , calculated from zone of inhibition measurements. This enhancement is attributed to increased reactive oxygen species generation and effective bacterial membrane disruption induced by GO incorporation. The study highlights the potential of GO–SnO _2 nanocomposites as advanced antibacterial materials, contributing to Sustainable Development Goals SDG 3 (Good Health and Well-being) and SDG 9 (Industry, Innovation, and Infrastructure).

Wei Luo, Haiming Yao, Wenyong Yu

Industrial anomaly detection plays a crucial role in ensuring product quality control. Therefore, proposing an effective anomaly detection model is of great significance. While existing feature-reconstruction methods have demonstrated excellent performance, they face challenges with shortcut learning, which can lead to undesirable reconstruction of anomalous features. To address this concern, we present a novel feature-reconstruction model called the \textbf{T}emplate-based \textbf{F}eature \textbf{A}ggregation \textbf{Net}work (TFA-Net) for anomaly detection via template-based feature aggregation. Specifically, TFA-Net first extracts multiple hierarchical features from a pre-trained convolutional neural network for a fixed template image and an input image. Instead of directly reconstructing input features, TFA-Net aggregates them onto the template features, effectively filtering out anomalous features that exhibit low similarity to normal template features. Next, TFA-Net utilizes the template features that have already fused normal features in the input features to refine feature details and obtain the reconstructed feature map. Finally, the defective regions can be located by comparing the differences between the input and reconstructed features. Additionally, a random masking strategy for input features is employed to enhance the overall inspection performance of the model. Our template-based feature aggregation schema yields a nontrivial and meaningful feature reconstruction task. The simple, yet efficient, TFA-Net exhibits state-of-the-art detection performance on various real-world industrial datasets. Additionally, it fulfills the real-time demands of industrial scenarios, rendering it highly suitable for practical applications in the industry. Code is available at https://github.com/luow23/TFA-Net.

Hongfang Lu, Lijun Guo, Mohammadamin Azimi et al.

Abstract Recently, with the development of “Industry 4.0”, “Oil and Gas 4.0” has also been put on the agenda in the past two years. Some companies and experts believe that “Oil and Gas 4.0” can completely change the status quo of the oil and gas industry, which can bring huge benefits because it accelerates the digitization and intelligentization of the oil and gas industry. However, the “Oil and Gas 4.0” is still in its infancy. Therefore, this paper systematically introduces the concept and core technologies of “Oil and Gas 4.0”, such as big data and the industrial Internet of Things (IIoT). Moreover, this paper analyzes typical application scenarios of the oil and gas industry chain (upstream, midstream and downstream) through examples, such as intelligent oilfield, intelligent pipeline, and intelligent refinery. It is concluded that the essence of “Oil and Gas 4.0” is a data-driven intelligence system based on the highly digitization. To the best of our knowledge, this is the first academic peer-reviewed paper on the “Oil and Gas 4.0” era, aiming to let more oil and gas industry personnel understand its benefits and application scenarios, so as to better apply it to practical engineering in the future. In the discussion section, this paper also analyzes the opportunities and difficulties that may be brought about by the “Oil and Gas 4.0” era. Finally, relevant policy recommendations are proposed.

Chad Syverson

Enghin Atalay

A. Bernard, J. Jensen, Peter K. Schott

Harrison G. Hong, W. Torous, Rossen Valkanov