Wei Shi^1,2, Shusheng Xiong^1,2,3,*, Wei Li^2,3, Kai Meng⁴, Qingsheng Liu⁴

Energy Engineering, Vol.122, No.9, pp. 3507-3523, 2025, DOI:10.32604/ee.2025.065579 - 26 August 2025

Abstract In the realm of all-electric aircraft research, the integration of cathode-open proton exchange membrane fuel cells (PEMFC) with lithium batteries as a hybrid power source for small to medium-sized unmanned aerial vehicles (UAVs) has garnered significant attention. The PEMFC, serving as the primary energy supply, markedly extends the UAV’s operational endurance. However, due to payload limitations and spatial constraints in the airframe layout of UAVs, the stack requires customized adaptation. Moreover, the implementation of auxiliary systems to facilitate cold starts of the PEMFC under low-temperature conditions is not feasible. Relying solely on thermal insulation measures… More >

Ravi Nahta¹, Nagaraj Naik^2,*, Srivinay³, Swetha Parvatha Reddy Chandrasekhara⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 461-487, 2025, DOI:10.32604/cmes.2025.063973 - 31 July 2025

Abstract The exponential growth of over-the-top (OTT) entertainment has fueled a surge in content consumption across diverse formats, especially in regional Indian languages. With the Indian film industry producing over 1500 films annually in more than 20 languages, personalized recommendations are essential to highlight relevant content. To overcome the limitations of traditional recommender systems—such as static latent vectors, poor handling of cold-start scenarios, and the absence of uncertainty modeling—we propose a deep Collaborative Neural Generative Embedding (C-NGE) model. C-NGE dynamically learns user and item representations by integrating rating information and metadata features in a unified neural More >

Chao Ma^1,*, Yinggang Sun¹, Zhenguo Yang¹, Hai Huang¹, Dongyang Zhan^2,3, Jiaxing Qu⁴

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6201-6217, 2022, DOI:10.32604/cmc.2022.022717 - 14 January 2022

Abstract The number of mobile application services is showing an explosive growth trend, which makes it difficult for users to determine which ones are of interest. Especially, the new mobile application services are emerge continuously, most of them have not be rated when they need to be recommended to users. This is the typical problem of cold start in the field of collaborative filtering recommendation. This problem may makes it difficult for users to locate and acquire the services that they actually want, and the accuracy and novelty of service recommendations are also difficult to satisfy… More >

Hongbo Wang^{1, *}, Qian Xue¹, Tong Cui¹, Yangyang Li², Huacheng Zeng³

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 337-351, 2020, DOI:10.32604/cmc.2020.07290 - 30 March 2020

Abstract As a major function of smart transportation in smart cities, vehicle model recognition plays an important role in intelligent transportation. Due to the difference among different vehicle models recognition datasets, the accuracy of network model training in one scene will be greatly reduced in another one. However, if you don’t have a lot of vehicle model datasets for the current scene, you cannot properly train a model. To address this problem, we study the problem of cold start of vehicle model recognition under cross-scenario. Under the condition of small amount of datasets, combined with the More >