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Solar energy, a pivotal renewable resource, faces operational challenges due to its intermittent and unstable power output. Thermal energy storage systems emerge as a promising solution, with phase change materials (PCMs) packed beds attracting attention for their compactness and stable temperature transitions. This paper details a laboratory-scale solar thermal storage PCM packed bed integrated with a heat pump, utilizing a novel form-stable PCM. A numerical model was established to assess the thermal storage characteristics and heat extraction performance of the solar PCM packed bed coupled with a heat pump. Simulation results show that increasing solar irradiance significantly reduces storage duration, achieving full thermal storage in 3.4 h at 900 W/m2 irradiance. Optimal starting times were identified as 9:00 a.m. or 11:00 a.m., with later starts resulting in incomplete storage due to the PCM not reaching its phase change temperature. Additionally, packed bed parameters influenced storage conditions; increasing the paraffin content in the PCM extended the phase change duration, while graphene nanoparticles slightly reduced it. Lower porosity (0.49) beds, with higher PCM content, reached 70 °C quicker than higher porosity (0.61) beds due to higher pressure drops promoting more uniform flow and temperature distribution. During heat extraction, coupling the heat pump at 2 liters/min achieved temperatures below 45 °C in 4.1 h, while at 6 liters/min, the time reduced to 1.6 h, demonstrating adaptability to different extraction rates. These findings provide insight into the thermal performance of solar PCM packed beds coupled with heat pumps, contributing to efficient and stable thermal utilization of solar energy. [ABSTRACT FROM AUTHOR]

In this paper, a high-order internal model (HOIM)-based iterative learning control (ILC) scheme is proposed for discrete-time nonlinear systems to tackle the tracking problem under iteration-varying desired trajectories. By incorporating the HOIM that is utilized to describe the variation of desired trajectories in the iteration domain into the ILC design, it is shown that the system output can converge to the desired trajectory along the iteration axis within arbitrarily small error. Furthermore, the learning property in the presence of state disturbances and output noise is discussed under HOIM-based ILC with an integrator in the iteration axis. Two simulation examples are given to demonstrate the effectiveness of the proposed control method. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The term "psychosurgery" reflecting neurosurgical treatment of mental disorders, was coined by a Portuguese neurologist Egas Moniz (1874-1955), who, in 1935, suggested a procedure named prefrontal leucotomy (or lobotomy) aimed to divide white matter tracts connecting prefrontal cortex and thalamus. Starting from 1936, this technique and its subsequent modification (transorbital lobotomy) was zealously promoted by a neurologist Walter Freeman (1895-1972) and a neurosurgeon James Watts (1904-1994) at George Washington University, who in 1942 summarized their experience in a monograph, which publication resulted in a tremendous worldwide interest in psychosurgical interventions. The present review describes comparative development of prefrontal leucotomy followed by stereotactic ablation and neurostimulation in three different geographical regions: USA, USSR/Russia, and Far East (China and Japan), where psychosurgery followed nearly similar courses, progressing from the initial enthusiasm and high clinical caseloads to nearly complete disregard. The opposition to neurosurgical interventions for mental disorders around the world was led by different groups and for varying reasons, but, unfortunately, always with political considerations mixed in. Today, with vast advancements in neuroimaging, stereotactic neurosurgical techniques, and physiological knowledge, psychiatric neurosurgery can be performed with much greater precision and safety., (Copyright © 2022 Elsevier B.V. All rights reserved.)