Platelet
发表于 2025-3-25 06:18:58
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STRIA
发表于 2025-3-25 07:34:00
Lorena Recalde,Gabriela Suntaxi,Diana Martinez-Mosquera,Rommel Masabanda,Danny Cabreraheir stakeholders’ interests — regardless of their product lines. Corporations that renege on their social responsibilities — and more so those without a compelling reason — tend to lose societal and stakeholder support or, as Davis noted more than three decades ago, their social power:.Corporations
适宜
发表于 2025-3-25 14:34:25
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GAVEL
发表于 2025-3-25 18:55:17
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GLEAN
发表于 2025-3-25 22:14:24
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follicular-unit
发表于 2025-3-26 02:50:55
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ear-canal
发表于 2025-3-26 04:17:24
Marcos Orellana,Jonnathan Emmanuel Cubero Lupercio,Juan Fernando Lima,Patricio Santiago García-MonteIn common with all other professionals, our work has led to the experience of ethical dilemmas: typically, these have involved major confrontations, either with our col leagues or our consciences. This book, however, is not limited to a discussion of such major themes. Rather, we have tried to use
客观
发表于 2025-3-26 09:45:17
Roberth Chachalo,Jaime Astudillo,Saba Infante,Israel Pinedaproblems. In common with all other professionals, our work has led to the experience of ethical dilemmas: typically, these have involved major confrontations, either with our col leagues or our consciences. This book, however, is not limited to a discussion of such major themes. Rather, we have tri
使腐烂
发表于 2025-3-26 14:46:02
Image Classification of Peach Leaves Using a Siamese Neural Network vertical farming and aquaponics have been proposed to maximize output while conserving resources and space. However, there is still room for improvement in crop care processes. Deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a leading approach for addressing various
Heretical
发表于 2025-3-26 19:14:39
Pancreas Segmentation Using SRGAN Combined with U-Net Neural Network vital for early diagnosis. This study focused on accurate pancreatic segmentation in medical images using subtle changes in 2D U-Net architecture to aid in diagnosis and treatment. Additionally, we proposed using a super-resolution generative adversarial network (SRGAN) for better-resolution images