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This research aims to examine the US–China policy shift from Obama to Biden emphasizing the centrality of Taiwan question in the geostrategic competition with Beijing and its prospect if the US strategy remains unchanged.

A conceptual framework is outlined, illustrating how the US grand strategy is driven by the ideological foundation of Exceptionalism. The paper highlights the associated US policy changes that evolved from Obama to Trump and then Biden to advance Washington's strategic interests in its rivalry with China over Taiwan.

Biden's policy led to an escalating geopolitical competition with Beijing over Taiwan to maintain US supremacy. The Biden administration is more stringent than the previous administrations on the Taiwan question and there is the conviction that the USA must not back down on Taiwan because the alternative will be a retraction of US world primacy to Beijing. With Washington's persistent hegemonic strategy, the US–China confrontation over Taiwan seems inevitable.

The research highlights how the Biden administration managed a perpetuated Ukraine crisis and forged unprecedented high-level ties with Taiwan, indicating the administration's determination to exacerbate contentions with Beijing over Taiwan rather than de-escalate.

This paper aims to present a human-in-the-loop natural teaching paradigm based on scene-motion cross-modal perception, which facilitates the manipulation intelligence and robot teleoperation.

The proposed natural teaching paradigm is used to telemanipulate a life-size humanoid robot in response to a complicated working scenario. First, a vision sensor is used to project mission scenes onto virtual reality glasses for human-in-the-loop reactions. Second, motion capture system is established to retarget eye-body synergic movements to a skeletal model. Third, real-time data transfer is realized through publish-subscribe messaging mechanism in robot operating system. Next, joint angles are computed through a fast mapping algorithm and sent to a slave controller through a serial port. Finally, visualization terminals render it convenient to make comparisons between two motion systems.

Experimentation in various industrial mission scenes, such as approaching flanges, shows the numerous advantages brought by natural teaching, including being real-time, high accuracy, repeatability and dexterity.

The proposed paradigm realizes the natural cross-modal combination of perception information and enhances the working capacity and flexibility of industrial robots, paving a new way for effective robot teaching and autonomous learning.

This study aims to review the recent advancements in high entropy alloys (HEAs) called high entropy materials, including high entropy superalloys which are current potential alternatives to nickel superalloys for gas turbine applications. Understandings of the laser surface modification techniques of the HEA are discussed whilst future recommendations and remedies to manufacturing challenges via laser are outlined.

Materials used for high-pressure gas turbine engine applications must be able to withstand severe environmentally induced degradation, mechanical, thermal loads and general extreme conditions caused by hot corrosive gases, high-temperature oxidation and stress. Over the years, Nickel-based superalloys with elevated temperature rupture and creep resistance, excellent lifetime expectancy and solution strengthening L12 and γ´ precipitate used for turbine engine applications. However, the superalloy’s density, low creep strength, poor thermal conductivity, difficulty in machining and low fatigue resistance demands the innovation of new advanced materials.

HEAs is one of the most frequently investigated advanced materials, attributed to their configurational complexity and properties reported to exceed conventional materials. Thus, owing to their characteristic feature of the high entropy effect, several other materials have emerged to become potential solutions for several functional and structural applications in the aerospace industry. In a previous study, research contributions show that defects are associated with conventional manufacturing processes of HEAs; therefore, this study investigates new advances in the laser-based manufacturing and surface modification techniques of HEA.

The AlxCoCrCuFeNi HEA system, particularly the Al0.5CoCrCuFeNi HEA has been extensively studied, attributed to its mechanical and physical properties exceeding that of pure metals for aerospace turbine engine applications and the advances in the fabrication and surface modification processes of the alloy was outlined to show the latest developments focusing only on laser-based manufacturing processing due to its many advantages.

It is evident that high entropy materials are a potential innovative alternative to conventional superalloys for turbine engine applications via laser additive manufacturing.

This paper aimed a fractional-order sliding mode-based lateral lane-change control method that was proposed to improve the path-tracking accuracy of vehicle lateral motion.

In this paper the vehicle presighting and kinematic models were established, and a new sliding mode control isokinetic convergence law was devised based on the fractional order calculus to make the front wheel turning angle approach the desired value quickly. On this basis, a fractional gradient descent algorithm was proposed to adjust the radial basis function (RBF) neuron parameter update rules to improve the compensation speed of the neural network.

The simulation results revealed that, compared to the traditional sliding mode control strategy, the designed controller eliminated the jitter of the sliding mode control, sped up the response of the controller, reduced the overshoot of the system parameters and facilitated accurate and fast tracking of the desired path when the vehicle changed lanes at low speeds.

This paper combines the idea of fractional order calculus with gradient descent algorithm, proposed a fractional-order gradient descent method applied to RBF neural network and fast adjustment the position and width of neurons.

In terms of understanding the new issues emerging in the practice of monetary policies and how to evaluate the latest theories of monetary policy, this paper proposes referring to Das Kapital and developing a monetary policy theory grounded in Marxist political economy.

Based on the discussion of interest-bearing capital in Das Kapital and using a heterogeneous agent model, this paper tries to explain the determining mechanism of interest rate, leverage ratio, and asset price.

The research finds that if there are differences in the techniques possessed by capital, the resulting disparities in production efficiency will lead to differences in profit rates and further influence the functional choices of capital in the movement of social total capital. Thus, with the formation of lending relationships, interest rates, leverage ratios, and asset prices will be endogenously determined simultaneously. Moreover, as the degree of technological diffusion influences the industrial capitalists’ willingness to take loans as well as the level of profit rates, there may be counter-cyclical changes in the returns on productive investment and financial investment at different stages of the technology life cycle, contributing to diverting funds out of the real economy. Besides, this paper discusses the challenges, tools, and goals of monetary policy within the credit money system.

Clarify the intrinsic mechanism of the functional differentiation of capital determined by heterogeneous technologies and exogenous capital-labor relation and analyze the impact of capital differentiation on the economy.