Microfluidic Nanoparticle Separation for Precision Medicine by Zhenwei Lan, Rui Chen, Da Zou, Chun‐Xia Zhao

“…Finally, it addresses existing challenges and envisions future development spurred by emerging technologies such as advanced materials science, 3D printing, and artificial intelligence. These interdisciplinary collaborations are anticipated to propel the platformization of microfluidic separation techniques, significantly expanding their potential in precision medicine.…”
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Integrating Sustainable HRM, AI, and Employee Well-Being to Enhance Engagement in Greater Jakarta: An SDG 3 Perspective by Grace Herlina Maria, Iskandar Karto

“…This study explores a combination of Sustainable Human Resource Management and Artificial Intelligence on employee well-being with a view to improving employee engagement for workers in Greater Jakarta, Indonesia. …”
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Improving Imitation Skills in Children with Autism Spectrum Disorder Using the NAO Robot and a Human Action Recognition by Abeer Alnafjan, Maha Alghamdi, Noura Alhakbani, Yousef Al-Ohali

“…However, teaching and training programs for children with ASD can also be enhanced by assistive technologies, artificial intelligence, and robotics. <b>Methods:</b> In this study, we examined whether robotics can improve the imitation skills of children with autism and support therapists during therapeutic sessions. …”
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Noise-attention-based forgery face detection method by Bolin ZHANG, Chuntao ZHU, Qilin YIN, Jingqiao FU, Lingyi LIU, Jiarui LIU, Hongmei LIU, Wei LU

“…With the advancement of artificial intelligence and deep neural networks, the ease of image generation and editing has increased significantly.Consequently, the occurrence of malicious tampering and forgery using image generation tools is on the rise, posing a significant threat to multimedia security and social stability.Therefore, it is crucial to research detection methods for forged faces.Face tampering and forgery can occur through various means and tools, leaving different levels of forgery traces during the tampering process.These traces can be partly reflected in the image noise.From the perspective of image noise, the noise components reflecting tampering traces of forged faces were extracted through a noise removal module.Furthermore, noise attention was generated to guide the backbone network in the detection of forged faces.The training of the noise removal module was supervised using SRM filters.In order to strengthen the guidance of the noise removal module, the noise obtained by the noise removal module was added back to the real face image, forming a pair of supervised training samples in a self-supervised manner.The experimental results illustrate that the noise features obtained by the noise removal module have a good degree of discrimination.Experiments were also conducted on several public datasets, and the proposed method achieves an accuracy of 98.32% on the Celeb-DF dataset, 92.61% on the DFDC dataset, and more than 94% on the FaceForensics++ dataset, thus proving the effectiveness of the proposed method.…”
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TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma by Wang W, Zhang Q, Cui Y, Zhang S, Li B, Xia T, Song Y, Zhou S, Ye F, Xiao W, Cao K, Chi Y, Qu J, Zhou G, Chen Z, Zhang T, Chen X, Ju S, Wang YC

“…Weilang Wang,1,&ast; Qi Zhang,2,&ast; Ying Cui,1,&ast; Shuhang Zhang,1 Binrong Li,1 Tianyi Xia,1 Yang Song,3 Shuwei Zhou,1 Feng Ye,4 Wenbo Xiao,5 Kun Cao,6 Yuan Chi,7 Jinrong Qu,8 Guofeng Zhou,9,10 Zhao Chen,11 Teng Zhang,12 Xunjun Chen,13 Shenghong Ju,1 Yuan-Cheng Wang1 1Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China; 2Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, People’s Republic of China; 3MR Scientific Marketing, Siemens Healthineers Ltd, Shanghai, People’s Republic of China; 4Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 5Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People’s Republic of China; 6Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, People’s Republic of China; 7Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China; 8Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, People’s Republic of China; 9Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China; 10Shanghai Institute of Medical Imaging, Shanghai, People’s Republic of China; 11Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 12Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, People’s Republic of China; 13Department of Radiology, The People’s Hospital of Xuyi County, Huaian, Jiangsu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yuan-Cheng Wang, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, Jiangsu, 210009, People’s Republic of China, Tel +086 25 83272121, Fax +086 25 83311083, Email yuancheng_wang@seu.edu.cnPurpose: To develop and validate a predictive model for predicting six-month outcome by integrating pretreatment MRI features and one-month treatment response after TACE.Methods: A total of 108 patients with 160 hCCs from a single-arm, multicenter clinical trial (NCT03113955) were analyzed and served as the training cohort. …”
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New design paradigm for federated edge learning towards 6G:task-oriented resource management strategies by Zhiqin WANG, Jiamo JIANG, Peixi LIU, Xiaowen CAO, Yang LI, Kaifeng HAN, Ying DU, Guangxu ZHU

“…Objectives: To make full use of the abundant data distributed at the edge of the network to serve the training of artificial intelligence models, edge intelligence technology represented by federated edge learning emerges as the times require. …”
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Explainable and perturbation-resilient model for cyber-threat detection in industrial control systems Networks by Urslla Uchechi Izuazu, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim, Jae Min Lee

“…XC-TDF enhances robustness against noise and adversarial attacks using regularization and adversarial training respectively, and also improves transparency through an eXplainable Artificial Intelligence (XAI) module. Simulation results demonstrate its effectiveness, showing resilience to perturbation by achieving commendable accuracy of 100% and 99.4% on the Wustl-IIoT2021 and Edge-IIoT datasets, respectively.…”
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A generic self-learning emotional framework for machines by Alberto Hernández-Marcos, Eduardo Ros

“…Here we propose that emotions correspond to distinct temporal patterns perceived in crucial values for living beings in their environment (like recent rewards, expected future rewards or anticipated world states) and introduce a fully self-learning emotional framework for Artificial Intelligence agents convincingly associating them with documented natural emotions. …”
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Оn the genesis and essence of the process of digitalization of global economic development by Alina Shevtsova

“…The following results are obtained: based on the analysis, it is noted that the genesis of the process of digitalization of global economic development can be considered in several stages: 1) the pre-digital stage; 2) the initial stage of digitization; 3) the stage of digitization of processes; 4) the stage of development of network technologies; 5) the stage of e-commerce and business Internet; 6) the stage of artificial intelligence and data analysis; 7) the stage of the digital transformation boom. …”
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Digitalization and Inclusive Education: Common Ground by D. Z. Akhmetova, T. S. Artyukhina, M. R. Bikbayeva, I. A. Sakhnova, M. A. Suchkov, E. A. Zaytseva

“…In order to do this, the entire process of digitalization and the use of artificial intelligence must be mastered. The main thing is to remember that “person” should be in the center of attention during the process of digitalization of socio-political processes.…”
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A scoping review of robustness concepts for machine learning in healthcare by Alan Balendran, Céline Beji, Florie Bouvier, Ottavio Khalifa, Theodoros Evgeniou, Philippe Ravaud, Raphaël Porcher

“…Abstract While machine learning (ML)-based solutions—often referred to as artificial intelligence (AI) solutions—have demonstrated comparable or superior performance to human experts across various healthcare applications, their vulnerability to perturbations and stability to variations due to new environments—essentially, their robustness—remains ambiguous and often overlooked. …”
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When Healthcare Professionals Use AI: Exploring Work Well-Being Through Psychological Needs Satisfaction and Job Complexity by Weiwei Huo, Qiuchi Li, Bingqian Liang, Yixin Wang, Xuanlei Li

“…This study examines how the use of artificial intelligence (AI) by healthcare professionals affects their work well-being through the satisfaction of basic psychological needs, framed within Self-Determination Theory. …”
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Exploring the Effects of Information and Communication Technology as a Pedagogy for Teaching Business Studies in Grade 10 in the Lejweleputswa Education District, South Africa by Masilo Pule David Mashabe, Motalenyane Alfred Modise

“…It should be acknowledged that the goal of ICT in schools is to inculcate learners with technological skills in order to mend and respond to the requirements of the Fourth Industrial Revolution (4IR) as well as Artificial Intelligence (AI).…”
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Enhancing adversarial transferability with local transformation by Yang Zhang, Jinbang Hong, Qing Bai, Haifeng Liang, Peican Zhu, Qun Song

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AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation by Shimpei Okamoto, Masanori Kataoka, Makoto Itano, Tsutomu Sawai

“…Abstract Background With artificial intelligence (AI) increasingly revolutionising medicine, this study critically evaluates the integration of large language models (LLMs), known for advanced text processing and generation capabilities, in medical ethics education, focusing on promoting virtue. …”
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An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis by Ahmed I. Saleh, Asmaa H. Rabie, Shimaa E. ElSayyad, Ali E. Takieldeen, Fahmi Khalifa

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Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics by Xiao Jing, Xin Pei, Pengpeng Xu, Yun Yue, Chunyang Han

“…Although the rapid development in big data and artificial intelligence motivates long-haul freeway logistics towards informatization and intellectualization, the transportation of bulk commodities still faces serious challenges arisen from dispersed freight demands and the lack of co-ordination among different operators. …”
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CFD Analysis for Aerodynamic Performance Optimization of a Vertical Axis Helical Wind Turbine Using Rigid PVC by Outar Hamza, Ennadafy Hamza, Jammoukh Mustapha, El Khaili Mohamed

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Prediction of buckling damage of steel equal angle structural members using hybrid machine learning techniques by Nang Xuan Ho, Tien-Thinh Le, The-Hung Dinh, Van-Hai Nguyen

“…In particular, a hybrid Artificial Intelligence (AI)-based model involving Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) was developed and calibrated for the problem at hand. …”
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