Decreasing thermal conductivity is important for designing efficient thermoelectric devices. Traditional engineering strategies have focused on point defects and interface design. Recently, dislocations as line defect...
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Background: According to the National Safety Council and the Bureau of Labor Statistics there were over 4,000 preventable injury related deaths. The Occupational Health and Safety Administration emphasizes that safety...
Background: According to the National Safety Council and the Bureau of Labor Statistics there were over 4,000 preventable injury related deaths. The Occupational Health and Safety Administration emphasizes that safety cultures should consist of shared beliefs, practices, and attitudes that exist at an establishment. The purpose of this industry-sponsored research project was to assist a medical manufacturing facility with safety program OSHA compliance. Method: Job Safety Analysis (JSA), also ca lled job hazard analysis, and facility inspections were used to identify hazards focused on the worker, task, tools, and the work environment. Results: Areas with high hazard scores according to the JSA were a ssigned to supervisors and the research team to provide immediate process and environment changes. Conclusion: JSA is a great tool to identify safety hazards on a job site though job functions. This will prevent injuries and a llow even small companies to remain compliant with federal regulations. Creating an atmosphere from top to bottom with a culture of safety responsibility andownership can promote a safe and productive environment.
Maintaining railway tracks in healthy conditions is critical to ensuring the safe operation of railroad transportation. According to the Federal Railroad Administration (FRA), nearly 23% of train accidents that occurr...
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Ethereum is one of the most popular blockchain platforms with a high number of adoption in the blockchain world today. Ethereum token (ERC-20) can tokenize any real-world object while it is also possible to exchange t...
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Most processes in many shipyards involve a large amount of work in progress at every work stage. Efficient construction, such as shortening work periods on production systems and reducing stocks of work in progress, t...
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Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potenti...
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In integrated energy systems(IESs),traditional fixed time-interval dispatching scheme is unable to adapt to the need of dynamic properties of the transient network,demand response characteristics,dispatching time scal...
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In integrated energy systems(IESs),traditional fixed time-interval dispatching scheme is unable to adapt to the need of dynamic properties of the transient network,demand response characteristics,dispatching time scales in energy subsystems and renewable power *** scheme may easily result in uneconomic source-grid-load-storage operations in *** this paper,we propose a dispatching method for IES based on dynamic time-interval of model predictive control(MPC).We firstly build models for energy sub-systems and multi-energy loads in the power-gas-heat ***,we develop an innovative optimization method leveraging trajectory deviation control,energy control,and cost control frameworks in MPC to handle the requirements and constraints over the timeinterval of ***,a dynamic programming algorithm is introduced to efficiently solve the proposed *** and simulation results prove the effectiveness of the method.
Industry 4.0 is characterized by a dynamic market that constantly looking for new methods to optimize and integrate manufacturing processes. In this context, Artificial Intelligence has gained prominence in problem-so...
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Industry 4.0 is characterized by a dynamic market that constantly looking for new methods to optimize and integrate manufacturing processes. In this context, Artificial Intelligence has gained prominence in problem-solving, such as failure prediction and decision making, thus improving product quality, and consequently bringing competitiveness to the company. Aiming to contribute to this scenario, this research develops a data treatment system that, from an intelligent tool and an interoperable ontological model, automates the prediction and detection of failures in machining machines lines. The system was developed for the prediction of faults in machining lines includes an artificial intelligence formed from prediction algorithms and inferences, it is possible to guarantee the correct treatment and communication of data at different stages of the process. For the experimental research, was used data collected from a machining line of a public dataset. The information is collected and classified by Artificial Intelligence that supports a decision system. The prediction of tool wear would enable the system to infer the type of problem that is causing this wear, a possible root cause, and the needed maintenance based on the ontological inference tool. By this classification of data, it is possible to achieve, through inferences, a reduction in the decision scope, bringing the possible problems caused by the incoming value. The semantic interoperability ensures correct data exchange and processing, which generates a more assertive view of production failures. The system may help companies to increase their productive process by helping them identify future failures in production if applied in a real scenario.
Fluoride (F) contamination in groundwater affects millions of people across the world. Although several sorbents have been identified for low-cost F removal, the choice of the optimal sorbent is dictated by the specif...
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Within the domain of industrial control systems, safeguarding data integrity stands as a pivotal endeavor, especially in light of the burgeoning menace posed by malicious tampering and potential data loss. Traditional...
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