Optimum investment strategy for hydrogen-based steelmaking project coupled with multiple uncertainties.

The large-scale application of hydrogen steelmaking technology is expected to substantially accelerate the decarbonization process of the iron and steel industry. However, hydrogen steelmaking projects are still in the experimental or demonstration stage, and scientific investment decision-making methods are urgently needed to support the large-scale development of the technology. When assessing the investment value, existing studies usually only consider the intrinsic project value under a specific pathway, while ignoring the option value under realistic multiple uncertainties in terms of technology, market, and policy, leading to an underestimation of the investment value. To address this issue, this study constructs a real options model to explore the optimal investment timing and revenue of the hydrogen steelmaking project, by taking into account multi-dimensional uncertainties stemming from price fluctuations in the steel market, the development of the carbon market, and technological advances. Additionally, the impacts of various subsidy policies on the investment strategy are also investigated. Least Squares Monte Carlo method is applied to overcome computational challenges posed by dynamic programming under multi-dimensional uncertainties. The results show that: (i) Investment is not recommended based on current crude steel price and hydrogen price. (ii) When the annual reduction rate of hydrogen price reaches 5%, the optimal investment timing would advance to 2036. (iii) On this basis, with the introduction of a 20% green hydrogen subsidy policy, the optimal investment timing would be further brought forward to 2033. The implementation of tax incentives would significantly increase the investment value. The investment value would surge from 170 million CNY to 262 million CNY as the tax rate decreases from 20% to zero. The findings could provide reasonable suggestions for investment decisions under realistic volatile environments, as well as scientific references for policy design, thus facilitating the large-scale and high-level development of hydrogen-based steelmaking technology.

Therapeutic effect of tempo in Mozart's "Sonata for two pianos" (K. 448) in patients with epilepsy: An electroencephalographic study.

BACKGROUND: Mozart's "Sonata for two pianos" (Köchel listing 448) has proven effective as music therapy for patients with epilepsy, but little is understood about the mechanism of which feature in it impacted therapeutic effect. This study explored whether tempo in that piece is important for its therapeutic effect. METHODS: We measured the effects of tempo in Mozart's sonata on clinical and electroencephalographic parameters of 147 patients with epilepsy who listened to the music at slow, original, or accelerated speed. As a control, patients listened to Haydn's Symphony no. 94 at original speed. RESULTS: Listening to Mozart's piece at original speed significantly reduced the number of interictal epileptic discharges. It decreased beta power in the frontal, parietal, and occipital regions, suggesting increased auditory attention and reduced visual attention. It also decreased functional connectivity among frontal, parietal, temporal, and occipital brain regions, also suggesting increased auditory attention and reduced visual attention. No such effects were observed after patients listened to the slow or fast version of Mozart's piece, or to Haydn's symphony at normal speed. CONCLUSIONS: These results suggest that Mozart's "Sonata for two pianos" may exert therapeutic effects by regulating attention when played at its original tempo, but not slower or faster. These findings may help guide the design and optimization of music therapy against epilepsy.

Numerical Study of Particle Separation through Integrated Multi-Stage Surface Acoustic Waves and Modulated Driving Signals.

The manipulation of biomedical particles, such as separating circulating tumor cells from blood, based on standing surface acoustic wave (SSAW) has been widely used due to its advantages of label-free approaches and good biocompatibility. However, most of the existing SSAW-based separation technologies are dedicated to isolate bioparticles in only two different sizes. It is still challenging to fractionate various particles in more than two different sizes with high efficiency and accuracy. In this work, to tackle the problems of low efficiency for multiple cell particle separation, integrated multi-stage SSAW devices with different wavelengths driven by modulated signals were designed and studied. A three-dimensional microfluidic device model was proposed and analyzed using the finite element method (FEM). In addition, the effect of the slanted angle, acoustic pressure, and the resonant frequency of the SAW device on the particle separation were systemically studied. From the theoretical results, the separation efficiency of three different size particles based on the multi-stage SSAW devices reached 99%, which was significantly improved compared with conventional single-stage SSAW devices.

Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination.

Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.

Uniformly embedded metal oxide nanoparticles in vertically aligned carbon nanotube forests as pseudocapacitor electrodes for enhanced energy storage.

Carbon nanotube (CNT) forests were grown directly on a silicon substrate using a Fe/Al/Mo stacking layer which functioned as both the catalyst material and subsequently a conductive current collecting layer in pseudocapacitor applications. A vacuum-assisted, in situ electrodeposition process has been used to achieve the three-dimensional functionalization of CNT forests with inserted nickel nanoparticles as pseudocapacitor electrodes. Experimental results have shown the measured specific capacitance of 1.26 F/cm(3), which is 5.7 times higher than pure CNT forest samples, and the oxidized nickel nanoparticle/CNT supercapacitor retained 94.2% of its initial capacitance after 10,000 cyclic voltammetry tests.

Characterizations of contact and sheet resistances of vertically aligned carbon nanotube forests with intrinsic bottom contacts.

Comprehensive studies on the sheet and contact resistances of vertically aligned carbon nanotube (CNT) forests with as-grown bottom contacts to the metal layer have been conducted. Using microfabrication and four distinct methods: (1) the transfer length method (TLM), (2) the contact chain method, (3) the Kelvin method, and (4) the four point probe method, we have designed multiple testing devices to characterize the resistances of CNT-forest-based devices. Experimental results show that devices based on stripe-shaped CNT forests 100 µm in height and 100 µm in width have a sheet resistance of approximately [Formula: see text]. The corresponding specific contact resistance to the molybdenum layer is roughly 5 × 10(4) Ω µm(2). Consistency of the results from the four different methods validates the study. After two months of storage of the CNT forest samples in open air, less than 0.9% deviations in the resistance values were observed. We further demonstrated one application of CNT forests as an NH(3) gas sensor and measured 0.5 ppm of sensing resolution with a detection response time of 1 min.