Solvent-free carbon sphere nanofluids towards intelligent lubrication regulation

By simply switching the electrical circuit installed on steel/steel contact,the tribological behaviors of nanofluids(NFs)can be regulated in real time,thereby achieving the desired performance of friction reduction and wear resistance.Herein,solvent-free carbon spherical nanofluids(C-NFs)were successfully prepared for intelligent lubrication regulation.C-NFs with excellent lubrication performance can immediately reduce the coefficient of friction(COF)despite applying a weak electric potential(1.5 V).Moreover,polyethylene glycol 400(PEG400)containing 5.0 wt%C-NFs remained responsive to electrical stimulation under the intermittent voltage application with an average coefficient of friction(ACOF)reduction of 20.8%over PEG400.Such intelligent lubrication regulation of C-NFs under an external electric field(EEF)mainly depends on the orderly arranged double-electric adsorption film of ion canopy-adsorbed carbon spheres(CSs).The intermittent electrical application can continuously reinforce the adsorption film and its durability for real-time controlling the sliding interfaces.Electrical-stimulation-responsive intelligent lubricants provide a new technical support for realizing intelligent stepless control of devices.

Artificial Intelligence Regulation and Machine Translation Ethics

The technological breakthroughs in generative artificial intelligence,represented by ChatGPT,have brought about significant social changes as well as new problems and challenges.Generative artificial intelligence has inherent flaws such as language imbalance,algorithmic black box,and algorithmic bias,and at the same time,it has external risks such as algorithmic comfort zone,data pollution,algorithmic infringement,and inaccurate output.These problems lead to the difficulty in legislation for the governance of generative artificial intelligence.Taking the data contamination incident in Google Translate as an example,this article proposes that in the process of constructing machine translation ethics,the responsibility mechanism of generative artificial intelligence should be constructed around three elements:data processing,algorithmic optimisation,and ethical alignment.

Intelligent sowing depth regulation system based on Flex sensor and Mamdani fuzzy model for a no-till planter

Sowing depth has an important impact on the performance of no-tillage planters,it is one of the key factors to ensure rapid germination.However,the consistency of sowing depth is easily affected by the complex environment of no-tillage operation.In order to improve the performance of no-tillage planters and improve the control precision of sowing depth,an intelligent depth regulation system was designed.Three Flex sensors installed on the inner surface of the gauge wheel at 120°intervals were used to monitor the downward force exerted by the seeding row unit against ground.The peak value of the output voltage of the sensor increased linearly with the increase of the downward force.In addition,the pneumatic spring was used as a downforce generator,and its intelligent regulation model was established by the Mamdani fuzzy algorithm,which can realize the control of the downward force exerted by the seeding row unit against ground and ensure the proper seeding depth.The working process was simulated based on MATLAB-Simulink,and the results showed that the Mamdani fuzzy model performed well in changing the pressure against ground.Field results showed that when the operating speed was 6 km/h,8 km/h and 10 km/h,the error of the system’s control of sowing depth was±9 mm,±12 mm,and±22 mm,respectively,and its sowing performance was significantly higher than that of the unadjusted passive operation.