Under the background of the continuous spread of covid-19, fresh food delivery platforms need to make decisions on how to incorporate epidemic factors into their delivery strategies. In this paper, considering the factors of large activity range, long path, low efficiency and high risk of delivery staff in reservation-type fresh food delivery, combined with the perspective of delivery platform, a path planning model is constructed. we apply the ALNS algorithm to the proposed model and compares it with other classical heuristic algorithms. The results show that our proposed model can effectively reduce risks and improve delivery efficiency.
The facile preparation of cost-effective VO2 thin films with high luminous transmittance and outstanding infrared modulation ability is highly desirable but still challenging since the optimization of one aspect often comes at the expense of deterioration in another. Furthermore, the complexity of the doping process or design of composite films often increases the preparation cost and complicates the operation. In this work, the film thickness was adjusted by controlling the vanadium ion concentration of the sol and spin-coating parameters. The influence of the annealing time at various film thicknesses on the optical performance was examined. The results revealed that the thin film thicknesses of VO2 ranging from 100 to 120 nm showed outstanding optical performances with Tlum,av = 36.9%, ΔTsol = 11.1%, and ΔT2500 nm ≈ 70% (Tlum,av is the average of luminous transmittance in the low/high-temperature phase). Short suitable annealing times were beneficial for expanding the optical band gap of the films, as well as increasing the porosity, thereby effectively enhancing the average luminous transmittance (Tlum,av = 43.1%) and maintaining relatively high modulation ability (ΔTsol = 11.5%, ΔT2500 nm = 65.1%). Overall, the proposed preparation method of highly compatible VO2 films simplifies the operation process and appears to be promising for a wide range of future applications, namely in visible-infrared zoom detection systems.
The generation of emotional talking faces from a single portrait image remains a significant challenge. The simultaneous achievement of expressive emotional talking and accurate lip-sync is particularly difficult, as expressiveness is often compromised for the accuracy of lip-sync. As widely adopted by many prior works, the LSTM network often fails to capture the subtleties and variations of emotional expressions. To address these challenges, we introduce DREAM-Talk, a two-stage diffusion-based audio-driven framework, tailored for generating diverse expressions and accurate lip-sync concurrently. In the first stage, we propose EmoDiff, a novel diffusion module that generates diverse highly dynamic emotional expressions and head poses in accordance with the audio and the referenced emotion style. Given the strong correlation between lip motion and audio, we then refine the dynamics with enhanced lip-sync accuracy using audio features and emotion style. To this end, we deploy a video-to-video rendering module to transfer the expressions and lip motions from our proxy 3D avatar to an arbitrary portrait. Both quantitatively and qualitatively, DREAM-Talk outperforms state-of-the-art methods in terms of expressiveness, lip-sync accuracy and perceptual quality.
The aim was to evaluate the temporal trends, characteristics and in-hospital outcomes of patients hospitalized with acute ischaemic stroke (AIS) between those with and without current or historical malignancies.Adult hospitalizations with a primary diagnosis of AIS were identified from the National Inpatient Sample database 2007-2017. Logistic regression was used to compare the differences in the utilization of AIS interventions and in-hospital outcomes. For further analysis, subgroup analyses were performed stratified by cancer subtypes.There were 892,862 hospitalizations due to AIS, of which 108,357 (12.14%) had a concurrent diagnosis of current cancer (3.41%) or historical cancer (8.72%). After adjustment for confounders, patients with current malignancy were more likely to have worse clinical outcomes. The presence of historical cancers was not associated with an increase in poor clinical outcomes. Additionally, AIS patients with current malignancy were less likely to receive intravenous thrombolysis (adjusted odds ratio 0.66, 95% confidence interval 0.63-0.71). Amongst the subgroups of AIS patients treated with intravenous thrombolysis or mechanical thrombectomy, outcomes varied by cancer types. Notably, despite these acute stroke interventions, outcome remains poor in AIS patients with lung cancer.Although AIS patients with malignancy generally have worse in-hospital outcomes versus those without, there were considerable variations in these outcomes according to different cancer types and the use of AIS interventions. Finally, treatment of these AIS patients with a current or historical cancer diagnosis should be individualized.
Heterogenous catalysis is important for future clean and sustainable energy systems. However, an urgent need to promote the development of efficient and stable hydrogen evolution catalysts still exists. In this study, ruthenium nanoparticles (Ru NPs) are in situ grown on Fe5 Ni4 S8 support (Ru/FNS) by replacement growth strategy. An efficient Ru/FNS electrocatalyst with enhanced interfacial effect is then developed and successfully applied for pH-universal hydrogen evolution reaction (HER). The Fe vacancies formed by FNS during the electrochemical process are found to be conducive to the introduction and firm anchoring of Ru atoms. Compared to Pt atoms, Ru atoms get easily aggregated and then grow rapidly to form NPs. This induces more bonding between Ru NPs and FNS, preventing the fall-off of Ru NPs and maintaining the structural stability of FNS. Moreover, the interaction between FNS and Ru NPs can adjust the d-band center of Ru NPs, as well as balance the hydrolytic dissociation energy and hydrogen binding energy. Consequently, the as-prepared Ru/FNS electrocatalyst exhibits excellent HER activity and improved cycle stability under pH-universal conditions. The developed pentlandite-based electrocatalysts with low cost, high activity, and good stability are promising candidates for future applications in water electrolysis.
This paper studies the human image animation task, which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results, these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work, we introduce MagicAnimate, a diffusion-based framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity. To achieve this, we first develop a video diffusion model to encode temporal information. Second, to maintain the appearance coherence across frames, we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations, we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably, our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='translate(288.889 -214.211)'/%3e%3cuse xlink:href='%23a' transform='translate(108 342.749)'/%3e%3cuse xlink:href='%23a' transform='translate(469.189 342.749)'/%3e%3c/svg%3e)