With the rising demand for higher data rates and more efficient power in next-generation communication systems, supply modulators play a critical role in optimizing the efficiency, speed, output power, and linearity of power amplifiers.This review delves into supply modulators (SMs) in gallium nitride (GaN) technologies for envelope tracking power amplifiers (ET-PAs).The various

Soft robotics has become a rapidly expanding field that redefines how intelligence can emerge from the interplay of body, material, and control.Unlike conventional rigid robots, soft robots leverage compliance, deformation, and morphological computation to enable safe, adaptive interaction with complex environments.This unique embodiment underpins the concept of embodied intelligence (EI), in which sensing, actuation, and reasoning are physically coupled within the robot's morphology rather than isolated in separate modules.Achieving such embodied intelligence requires the deep integration of four interrelated technologies: modeling, simulation, perception, and control.Modeling provides the physical understanding that enables robots to interpret and predict their own body dynamics.At the same time, simulation extends this capacity into virtual space, allowing embodied behaviors to be designed, tested, and optimized efficiently.Perception transforms the soft body into a distributed sensory organ, enabling robots to sense through deformation and to interact with their surroundings in an inherently adaptive manner.Control, finally, closes the loop of embodiment by exploiting mechanical compliance and learning-based strategies to generate coordinated and context-aware behaviors that arise naturally from morphologyenvironment coupling.This review offers a comprehensive synthesis of the four pillars of embodied intelligence in soft robotics, emphasizing how they coevolve to realize physically grounded intelligence, and discusses the remaining challenges and future directions toward scalable, robust, and autonomous soft robotic systems.

An essential part of solar photovoltaic (PV) systems is maximum power point tracking (MPPT), which allows for the best possible energy extraction in a variety of environmental circumstances.In dynamic settings, traditional MPPT methods like perturb and observe (P&O) and incremental conductance (INC) frequently fail, especially when there is partial shade or quickly varying irradiance.

We investigate the combined effects of velocity/thermal slip, temperature-dependent viscosity, thermal conductivity and ternary hybrid (ZrO$_2$-ZnO-NiZnFe$_2$O$_4$) nanoparticles in an unsteady Eyring-Prandtl fluid over an infinite plate using a finite difference method.

One of the keys to clean power production is the control of nitrogen oxide (NOx) emissions.The NOx concentration must be accurately tracked and monitored during the denitrification process in power plants.This research proposes a novel NOx emission prediction model, which innovatively establishes dynamic graph topology structures to explore the internal connections between thermal process data.It includes carefully curated and improved feature selection, delay analysis, graph attention mechanism, Transformer, and other modules.The method demonstrated commendable predictive capabilities in a 1000 MW ultra-supercritical power generation unit at the Shanghai Caojing power plant in China, achieving mean absolute error values of 0.7675, root mean square error values of 0.9800, and mean absolute percentage error values of 2.005 for NOx.When benchmarked against the six latest single and hybrid data-driven models, the model proposed in this research consistently surpassed them, demonstrating outstanding predictive performance.By weaving together advanced deep learning, powerful data preparation, and astute delay analysis, this work not only highlights the effectiveness of data-driven modeling solutions for complex problems in the power generation industry, but also lays a crucial foundation for clean power production and green energy transition.

Tin halide perovskites are promising lead-free alternatives due to their suitable bandgap, low toxicity, and structural analogy to lead counterparts.Compared to polycrystalline films, tin-based perovskite single crystals offer superior characteristics: absence of grain boundaries, lower defect density, longer carrier lifetime, enhanced stability, and wide spectral absorption.This review systematically summarizes growth methods (cooling crystallization, inverse temperature crystallization, solvent evaporation, Bridgman method), performance optimization strategies, and structure-property relationships.It critically analyzes each method's advantages/limitations and explores approaches for controllable synthesis of high-quality crystals via growth condition optimization.Despite their high potential in optoelectronics and environmental compatibility, significant growth challenges persist: high susceptibility of Sn 2+ to oxidation and difficulties in precise crystal size/quality control.Future research should prioritize developing effective antioxidant strategies, further optimizing growth conditions, and exploring novel synthetic methodologies to advance optoelectronic device applications.

This paper investigates the interaction between rolling stock and railway track during the emergency braking of a passenger train.A detailed simulation model of a train consisting of a locomotive and twenty passenger cars was developed in the Universal Mechanism software environment.The model accounts for longitudinal, lateral, and vertical vibrations, as well as nonlinear elastic couplings and random track irregularities.Simulation results show that the estimated braking distance is 583 m, which complies with current regulatory standards.The analysis of coupler forces revealed that braking is generally shockless due to buffer devices, except at the locomotive-first car connection.Derailment is possible only under severe track irregularity conditions, while displacement of the railsleeper lattice caused by increased transverse forces does not result in derailment.The findings contribute to improving railway safety assessment and support the development of intelligent monitoring and warning systems for modern "smart train" applications.

The design of green, efficient, and acid-resistant adsorbents is crucial for gold recovery from acidic leachates of secondary resources.In this study, a wood-based adsorbent loaded with nano-sulfurized copper exhibiting adsorption advantages in acidic environments was fabricated by loading copper sulfide nanoparticles onto delignified wood.This adsorbent exhibited excellent adsorption performance in acidic environments, with a maximum adsorption capacity of 750 mg/g achieved at pH 2.0.Adsorption equilibrium data were well fitted by the Langmuir isotherm model, while kinetic data conformed to the pseudo-second-order model.Thermodynamic analysis revealed that the adsorption process was spontaneous and endothermic.The adsorption mechanism of CuS/PW toward Au(III) was systematically investigated via X-ray diffraction (XRD), fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS).Results demonstrated that in addition to S 2-in copper sulphide and hydroxyl groups in cellulose participating in the chelation and reduction of Au(III), carbonyl groups in hemicellulose and lignin also contribute to the chelation and reduction of Au(III).Collectively, this work not only develops an efficient and eco-friendly adsorbent for Au (III) removal from acidic aqueous solutions, but also provides a promising strategy for the design of biomass-based adsorbents in precious metal recovery.