This notice continues a Regional Emphasis Program (REP) for the purpose of conducting inspections within the oil and gas industry. The employers to be targeted are in the oil and gas industry and are classified within NAICS 213111 , Drilling Oil and Gas Wells; NAICS 213112, Support Activities for Oil and Gas Operations; NAICS 541360, Geophysical Surveying and Mapping Services; NAICS 237120, Oil and Gas Pipeline and Related Structures Construction; and 238910, Site Preparation Contractors. The goal of this REP is to reduce the days away and fatality rates in this industry in line with OSHA's targeting of high risk industries.
Amine Belhadi, Sachin S. Kamble, C. Jabbour
et al.
Highlights • Insights into the impact of COVID-19 outbreak on automobile and airline supply chain is provided.• Integrated time-to-recovery and financial impact analysis, empirical survey and semi-structured interviews were used.• Localized supply sources and industry 4.0 technologies identified as significant strategies by automobile industry.• Business continuity by defining operations at the airport and flights perceived significant strategy by airline industry.• Real-time information sharing and cooperation among supply chain stakeholders is critical.
Mario Lezoche, J. H. Hormazabal, M. M. E. A. Díaz
et al.
Abstract The term “Agri-Food 4.0” is an analogy to the term "Industry 4.0", coming from the concept “agriculture 4.0”. Since the origins of the industrial revolution, where the steam engines started the concept of Industry 1.0 and later the use of electricity upgraded the concept to Industry 2.0, the use of technologies generated a milestone in the industry revolution by addressing the Industry 3.0 concept. Hence, Industry 4.0, it is about including and integrating the latest developments based on digital technologies as well as the interoperability process across them. This allows enterprises to transmit real-time information in terms behaviour and performance. Therefore, the challenge is to maintain these complex networked structures efficiently linked and organised within the use of such technologies, especially to identify and satisfy supply chain stakeholders dynamic requirements. In this context, the agriculture domain is not an exception although it possesses some specialities depending from the domain. In fact, all agricultural machinery incorporates electronic controls and has entered to the digital age, enhancing their current performance. In addition, electronics, using sensors and drones, support the data collection of several agriculture key aspects, such as weather, geographical spatialization, animals and crops behaviours, as well as the entire farm life cycle. However, the use of the right methods and methodologies for enhancing agriculture supply chains performance is still a challenge, thus the concept of Industry 4.0 has evolved and adapted to agriculture 4.0 in order analyse the behaviours and performance in this specific domain. Thus, the question mark on how agriculture 4.0 support a better supply chain decision-making process, or how can help to save time to farmer to make effective decision based on objective data, remains open. Therefore, in this survey, a review of more than hundred papers on new technologies and the new available supply chains methods are analysed and contrasted to understand the future paths of the Agri-Food domain.
Using the idea that firm-specific assets associated with marketing, management, and product-specific R & D can be used to service production plants in countries other than the country in which these inputs are employed, I develop a simple general equilibrium model of international trade in which the location of plants in a differentiated product industry is a decision variable. The model is then used to derive predictions of trade pattern, volumes of trade, the share of intra-industry trade, and the share of intrafirm trade as functions of relative country size and differences in relative factor endowments.
[Objective] It is aimed to construct an evaluation index system for urban rail transit （URT） track structure health status based on comprehensive inspection vehicles， carrying out scientific evaluation of urban rail transit track structure health status， thereby providing a scientific basis for track structure maintenance and management. [Method] Leveraging the detection capabilities of urban rail comprehensive inspection vehicles， literature reviews， standard and specific analyses， and field investigations are conducted to identify the influencing factors of track structure health status. Following the scientificity， completeness， operability， hierarchy， and updatability principles， a preliminary evaluation index system for URT track structure health status is developed， consisting of 6 primary indicators and 40 secondary indicators. Based on this， a questionnaire survey is used to adjust and optimize the preliminary system， ultimately establishing a finalized evaluation index system for track structure health status based on urban rail comprehensive inspection vehicles. [Result & Conclusion] The constructed rail structure health status evaluation index system integrates existing academic research results， established standards and specifications， industry consensus and expert insights. It provides technical support for evaluating the health status of URT track structures and serves as a decision-making basis for their maintenance and management.
As shipping routes and resource exploration move toward high-latitude oceans, sea ice becomes a major threat to the safety of ship navigation, posing significant challenges to the shipping industry and offshore resource development. Continuous development of satellite remote sensing and deep learning has made large-scale and wide-ranging ship detection (SD) possible, which is of great significance for ship safety. However, existing ship datasets used for deep learning only include ship images in open waters (OW), such as ports and inland rivers. Currently, remote sensing datasets suitable for SD in ice-infested waters (IIW) are lacking. SD in IIW is more difficult than SD in OW because of complex background interference from sea ice. Thus, it is infeasible to directly use the features of ships in OW for SD in IIW. Herein, we propose a remote sensing SD dataset called IceRegionShip, which includes subdatasets IceRegionShip–red, green and blue (RGB) and IceRegionShip–ice region ship index (IRSI). IceRegionShip–IRSI consists of low-resolution images processed with IRSI. IceRegionShip–RGB and IceRegionShip–IRSI contain 11 436 and 9073 ship instances, respectively. IRSI was proposed to address false alarms caused by ice interference. To the best of our knowledge, this is the first dataset designed specifically for SD in IIW. In addition, the dataset was evaluated using several advanced detection algorithms, providing a benchmark for SD in IIW and demonstrating the effectiveness of IRSI for SD in low-resolution optical remote sensing images.
Objective: The aim of this study was to investigate the effect of lacidophilin on inflammation and intestinal microflora in H. pylori-induced gastritis mice. Methods: Established an H. pylori-infected mice model by orally administering an H. pylori suspension. The mice were randomly divided into a model group and a lacidophilin treatment group, which received treatment for four weeks. Gram staining, Warthin-Starry silver staining, and H. pylori immunohistochemical staining were conducted to confirm the colonization of H. pylori in the gastric mucosa of mice. Hematoxylin and eosin (HE) staining was performed to observe any histopathological changes in the gastric mucosa of mice. Immunohistochemistry was carried out to observe the expression of iNOS, IL-1β, and 3-Nitrotyrosine in the gastric mucosa of mice. Additionally, the structure of intestinal microflora was analyzed using 16S rRNA sequencing. Results: The successful establishment of H. pylori-infected mice. Lacidophilin improved the stomach histopathological morphology in mice with H. pylori-induced gastritis and inhibited iNOS, IL-1β, and 3-Nitrotyrosine expression in the gastric mucosa. The composition and structure of the gut microbiota among the lacidophilin group showed noticeable differences in comparison to the H. pylori-infected group. Lacidophilin augmented the diversity of intestinal microflora in H. pylori-infected mice by regulating the abundance of Firmicutes, Actinobacteriota, Bacteroidete, and Verrucomicrobiota. At the genus level, Lacidophilin resulted in significant growth stimulation of beneficial bacteria such as g_norank_f_Muribaculaceae, Akkermansia, and Alistipes, while suppressing the proliferation of inflammation-related bacteria such as Dubosiella, thereby improving the composition and structure of the gut microbiota. Conclusion: Lacidophilin has the potential to rebalance the composition and structure of gut microbiota that has been compromised by H. pylori infection. It may have a protective effect on mice with H. pylori-induced gastritis by promoting the growth of beneficial bacteria, reducing inflammatory reactions, and alleviating oxidative damage to the gastric mucosa.
Due to its exceptional quality as a biomedical metal, stainless steel is often utilized to produce a broad range of medical tools. The resistance of stainless steel to corrosion is a key indicator of how long and how effective it will serve its intended purpose, and it is an important factor in determining the biocompatibility of the material. However, due to the complex physiological environment within the human body, the corrosion management of medical-grade stainless steel is facing several challenges. In this article, an overview of the factors that influence the corrosion performance of medical-grade stainless steel is provided, and new technologies and methods that have been developed in recent years to improve the corrosion resistance are discussed. These cutting-edge methods are expected to improve the corrosion resistance and longevity of medical-grade stainless steel, providing strong support for the increased applicability of the material in the medical industry.
Driven by both business and technology, the communication industry is focused on smart network operations. It is an industry consensus to promote network intelligent operation with autonomous networks as the driving force. It was analyzed that operational support system (OSS) was the core component of the three-layer architecture of autonomous network, and the key to improve the level of autonomous networks was to enhance the intelligence capability of OSS. Specific implementation schemes such as the definition method of OSS product business scope, the analysis of the shortcoming of OSS product ability,and systematic improvement of OSS product capabilities driven by autonomous networks were elaborated. The solution was described in detail using the digital operation value scenario of broadband services as an example. Finally, the intelligent ability map of OSS driven by autonomous network was discussed. Improving the intelligence capability of OSS for autonomous network can effectively promote the research and development direction of OSS, and guide the planning and research and development of OSS products for operators.