Automatic identification of clinically important Aspergillus species by artificial intelligence-based image recognition: proof-of-concept study by Chi-Ching Tsang, Chenyang Zhao, Yueh Liu, Ken P. K. Lin, James Y. M. Tang, Kar-On Cheng, Franklin W. N. Chow, Weiming Yao, Ka-Fai Chan, Sharon N. L. Poon, Kelly Y. C. Wong, Lianyi Zhou, Oscar T. N. Mak, Jeremy C. Y. Lee, Suhui Zhao, Antonio H. Y. Ngan, Alan K. L. Wu, Kitty S. C. Fung, Tak-Lun Que, Jade L. L. Teng, Dirk Schnieders, Siu-Ming Yiu, Susanna K. P. Lau, Patrick C. Y. Woo

“…While morphological examination is the most widely used for Aspergillus identification in clinical laboratories, PCR–sequencing and MALDI–TOF MS are emerging technologies in more financially-competent laboratories. …”
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Rough-and-Refine Model for Scene Graph Generation by Li Junliang, Lv Shirong, Li Wei

“…In the Rough Part, image features are initially extracted using convolutional neural networks and a Transformer encoder. …”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…Firstly, a lightweight ADown module was incorporated to replace the conventional stride-2 convolution. The ADown module dynamically adapts its downsampling strategy according to the feature characteristics, effectively reducing the number of parameters and computational complexity, while enhancing the model's ability to capture crack edges and fine textural details. …”
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Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis by Zohreh Javanmard, Saba Zarean Shahraki, Kosar Safari, Abbas Omidi, Sadaf Raoufi, Mahsa Rajabi, Mohammad Esmaeil Akbari, Mehrad Aria

“…Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), emerged as the preferred modern algorithm within these methodologies. …”
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Amazonian manatee critical habitat revealed by artificial intelligence‐based passive acoustic techniques by Florence Erbs, Mike van derSchaar, Miriam Marmontel, Marina Gaona, Emiliano Ramalho, Michel André

“…Subsequently, we investigated the vocal behavior parameters based on vocalization frequencies and temporal characteristics in the context of habitat use. A Convolutional Neural Network model successfully detected Amazonian manatee vocalizations with a 0.98 average precision on training data. …”
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Correction of crop water deficit indicators based on time-lag effects for improved farmland water status assessment by Yujin Wang, Zhitao Zhang, Yinwen Chen, Shaoshuai Fan, Haiying Chen, Xuqian Bai, Ning Yang, Zijun Tang, Long Qian, Zhengxuan Mao, Siying Zhang, Junying Chen, Youzhen Xiang

“…However, time-lag effects between canopy temperature (Tc) and environmental factors can influence their correlation with SWC, thereby complicating the identification of the most reliable diagnostic indicator. This study conducted a two-year field experiment on winter wheat under four irrigation levels (80–95 %, 65–80 %, 50–65 %, and 40–50 % field capacity). …”
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Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder by Yilu Zhao, Zhao Fu, Eric J. Barnett, Ning Wang, Kangfuxi Zhang, Xuping Gao, Xiangyu Zheng, Junbin Tian, Hui Zhang, XueTong Ding, Shaoxian Li, Shuyu Li, Qingjiu Cao, Suhua Chang, Yufeng Wang, Stephen V. Faraone, Li Yang

“…Notably, the variant that contributed most to the CNN model was NKAIN2, an ADHD-related gene, which is also associated with metabolic processes. …”
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Robust development of data-driven models for methane and hydrogen mixture solubility in brine by Kashif Saleem, Abhinav Kumar, K. D. V. Prasad, Ahmad Alkhayyat, T. Ramachandran, Protyay Dey, Navdeep Kaur, R. Sivaranjani, I. B. Sapaev, Mehrdad Mottaghi

“…In addition, it was shown that solubility is mostly affected by hydrogen mole fraction in mixture and pressure. …”
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Early Diagnosis of Knee Osteoarthritis With a Natural Language Processing–Driven Approach Based on Clinician Notes: Development and Validation Study by Narathip Thanyakunsajja, Kulsawasd Jitkajornwanich, Shan Xu, Donghee Shin, Pattama Charoenporn

“…We then incorporated deep learning models, including convolutional neural networks, bidirectional long short-term memory (BiLSTM), and gated recurrent units. …”
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Design of a Miniaturized and Polarization-Independent Frequency-Selective Surface for Targeted EMI Shielding by Muhammad Idrees, Yejun He, Youquan Wen, Abdul Majeed

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“Locality – Adaptation” Research of Hydropower Resettlement Communities in the Jinsha River Basin: A Case Study of Ludila Hydropower Station by Fang WANG, Zhuoqi LI, Haoyi XU, Jiaqi YAN

“…ObjectiveWithin human – environment systems, river basins constitute the earliest geographical units of human activity and currently represent the most critical zones of strained human – environment interactions. …”
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L’impérialisme humanitaire de Michael Ignatieff : le cas d’un « liberal hawk » by Pierre Guerlain

“…Though he often ends up in agreement with neoconservative or neoliberal imperialists his justifications are more intellectual and convoluted and therefore address a different audience.…”
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Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy by Mingxi Zhang, Zefang Shen, Lewis Walden, Farid Sepanta, Zhongkui Luo, Lei Gao, Oscar Serrano, Raphael A. Viscarra Rossel

“…Previous spectroscopic modelling has mostly ignored the compositional nature of the SOC fractions (i.e. …”
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Quantum ensemble learning with a programmable superconducting processor by Jiachen Chen, Yaozu Wu, Zhen Yang, Shibo Xu, Xuan Ye, Daili Li, Ke Wang, Chuanyu Zhang, Feitong Jin, Xuhao Zhu, Yu Gao, Ziqi Tan, Zhengyi Cui, Aosai Zhang, Ning Wang, Yiren Zou, Tingting Li, Fanhao Shen, Jiarun Zhong, Zehang Bao, Zitian Zhu, Zixuan Song, Jinfeng Deng, Hang Dong, Pengfei Zhang, Wei Zhang, Hekang Li, Qiujiang Guo, Zhen Wang, Ying Li, Xiaoting Wang, Chao Song, H. Wang

“…Abstract Quantum machine learning is among the most exciting potential applications of quantum computing. …”
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Wireless Authentication Method Based on Near-Field Feature Fusion Network by QIU Jiefan, ZHOU Kezhong, ZHU Dongfu, ZHANG Jinhong, CHI Kaikai

“…Initially, in the dynamic feature extraction module, an Inception-based WiFi Convolutional Network (IWCN) was employed to capture multi-scale spatial features across subcarriers. …”
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Mast cell hyperactivity as the factor in the pathogenesis of male infertility by N. G. Kulchenko

“…Against the background of an increase in the number of mast cells in the interstitium of the testis, fibrosis of the wall of the convoluted seminiferous tubules increases. Moreover, in severe spermatogenesis disorders (Sertoli cell-only syndrome, Germ cell aplasia), mast cells were found both in the peritubular space and in the lumen of the convoluted seminiferous tubules. …”
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Labor protests of employees of cultural organizations by Petr V. Bizyukov, Taisiya A. Vishnyakova

“…The situation is further exacerbated by the fact that the Russian labor code outlines an exceedingly complex and convoluted procedure for organizing lawful protests and strikes. …”
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Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…In addition, we developed a novel hybrid feature selection model that combined correlation filtering with mutual information, Cross-Validation (CV) along with Recursive Feature Eliminatio (RFE) (R, and stability selection to identify the most impactful features. Moreover, This study develops the proposed EffiXNet, a more refined version of EfficientNet augmented with self-attention mechanisms, dynamic convolutions, improved normalization methods, and Sparrow Search Optimization Algorithm for hyperparameter optimization. …”
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A systematic review on the associations between built environment and mental health among older people by Yongkang Chen, Lizhen Xu, Xiangfen Cui, Haoran Yang, Haoran Yang, Yiling Liu, Xin Gao, Jianhong Huang

“…The findings reveal that the GDS-15 scale is the most commonly used tool for assessing MH outcomes. …”
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Characteristics of symptomatic epilepsy and other neurological disorders in children with lissencephaly by V. I. Guzeva, I. V. Ochrim, V. R. Kasumov, O. V. Guzeva, V. V. Guzeva, N. E. Maksimova

“…Brain malformations are one of the most frequent causes of epilepsy in childhood. In patients with malformations of the brain epilepsy is diagnosed in 75–80% of cases. …”
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