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“This Wasn’t Pedagogy, It Was Panicgogy”: Perspectives of the Challenges Faced by Students and Instructors during the Emergency Transition to Remote Learning Due to COVID-19

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College Teaching 2023年第4期71卷 227-243页

作者： Spinks, M’Lyn Metzler, Mike Kluge, Stacy Langdon, Jody Gurvitch, Rachel Smitherman, Marina Esmat, Tiffany Bhattacharya, Sylvia Carruth, Laura Crowther, Katy Denton, Ren Edwards, Ordene V. Shrikhande, Milind Strong-Green, Ashley WellStar College of Health and Human Services Kennesaw State University United States Center for Teaching and Learning Georgia State University United States Instructional Innovation Augusta University United States Georgia Southern University United States Georgia State University United States Department of Life Science Dalton State College United States WellStar College of Health and Human Services Exercise Science Kennesaw State University United States College of Computing and Software Engineer Engineering Technology Kennesaw State University United States Center for Excellence in Teaching and Learning Georgia State University United States Department of English Georgia State University United States Academic Affairs East Georgia State College - Statesboro Campus United States Department of Psychological Science Kennesaw State University United States Department of Finance Georgia State University United States Department of English Augusta Technical College United States

This qualitative study explores the impact of the emergency transition to remote education (ETRE) during the COVID-19 pandemic on instructors and students through the lens of self-determination theory (SDT). A modified thematic analysis of narratives from a cross-sectional survey revealed eight themes: Sense of loss/grief, Role conflict, Helplessness, I had no choice, This felt impossible, Lost connections, Am I safe, and They don’t care about me. Sub-themes expound on their associated themes. Participant narratives shared feelings of trauma and crisis as they related experiences of higher education during the mandated global shutdown. The stories of these experiences are indicative of loss of autonomy, competence, and relatedness, tenets of self-determination. These experiences, for the majority of students, led to a loss of motivation to learn, participate, or produce meaningful work. For most instructors, the experiences led to a similar lassitude and frustration. The authors conclude that the experience of the ETRE negatively impacted both teaching and learning in the higher education setting. Recommendations include further development in higher education to support both instructors’ and students’ self-determination during catastrophic change. © 2021 Taylor & Francis Group, LLC.

关键词： COVID-19 emergency transition instructors qualitative self-determination students

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Unsupervised domain adaptation with dual-scheme fusion network for medical image segmentation 29

Unsupervised domain adaptation with dual-scheme fusion netwo...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zou, Danbing Zhu, Qikui Yan, Pingkun School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Wuhan University China Rensselaer Polytechnic Institute Department of Biomedical Engineering TroyNY United States

ISBN: (纸本)9780999241165

Domain adaptation aims to alleviate the problem of retraining a pre-trained model when applying it to a different domain, which requires large amount of additional training data of the target domain. Such an objective is usually achieved by establishing connections between the source domain labels and target domain data. However, this imbalanced source-to-target one way pass may not eliminate the domain gap, which limits the performance of the pre-trained model. In this paper, we propose an innovative Dual-Scheme Fusion Network (DSFN) for unsupervised domain adaptation. By building both source-to-target and target-to-source connections, this balanced joint information flow helps reduce the domain gap to further improve the network performance. The mechanism is further applied to the inference stage, where both the original input target image and the generated source images are segmented with the proposed joint network. The results are fused to obtain more robust segmentation. Extensive experiments of unsupervised cross-modality medical image segmentation are conducted on two tasks - brain tumor segmentation and cardiac structures segmentation. The experimental results show that our method achieved significant performance improvement over other state-of-the-art domain adaptation methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Image segmentation

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Waste plastics derived nickel-palladium alloy filled carbon nanotubes for hydrogen evolution reaction

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Chemosphere 2023年 341卷 139982页

作者： Nangan, Senthilkumar Natesan, Thirumalaivasan Sukmas, Wiwittawin Okhawilai, Manunya Justice Babu, Kaliyamoorthy Tsuppayakorn-aek, Prutthipong Bovornratanaraks, Thiti Wongsalam, Tawan Vimal, Vrince Uyama, Hiroshi Al-Enizi, Abdullah M. Kansal, Lavish Sehgal, Satbir S. Metallurgy and Materials Science Research Institute Chulalongkorn University Bangkok10330 Thailand Saveetha University Tamilnadu Chennai600077 India Center of Excellence in Polymeric Materials for Medical Practice Devices Department of Chemical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Institute of Nano Science and Technology Punjab Mohali140306 India Department of Physics Faculty of Science Chulalongkorn University Bangkok10330 Thailand Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Department of Applied Chemistry Graduate School of Engineering Osaka University Osaka565-0871 Japan Department of Chemistry College of Science King Saud University Riyadh11451 Saudi Arabia School Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara144411 India Division of Research Innovation Uttaranchal University Dehradun India

Carbon nanotubes (CNTs) composed of bimetallic nickel-palladium (NiPd) nanoparticles encapsulated in graphitic carbon shells (NdPd@CNT) are prepared by the chemical vapour deposition method using waste polyethylene terephthalate (PET) plastic carbon sources and NiPd-decorated carbon sheets (NiPd@C) catalyst. The characterization results reveal that the face-centered cubic crystalline (fcc)-structured NiPd bimetallic alloy nanoparticles are encased by thin carbon nanotubes. The bimetallic synergism of NiPd nanoparticles actuates the outer CNT layers and accelerates the electrical conductivity, stimulating the electrochemical activity toward an effective hydrogen evolution reaction (HER). By virtue of the collective individualities of highly conductive aligned carbon walls and bimetallic active sites, the NiPd@CNT-equipped HER delivers a minimum overpotential of 87 mV and a Tafel slope value of 95 mV dec−1. The existing intact contact between NiPd and CNT facilitates continuous electron and ion transportation and firm stability toward long-term hydrogen production in HER. Notably, the NiPd@CNT reported here produces excellent electrochemical activity with minimal charge transference resistance, substantiating the efficacy of NiPd@CNT for futuristic green hydrogen production. © 2023 Elsevier Ltd

关键词： Carbon nanotubes

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Integrating educational theories with virtual reality: Enhancing engineering education and VR laboratories

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Social Sciences and Humanities Open 2024年 10卷

作者： Abbas Shah, Syed Faisal Mazhar, Tehseen Shahzad, Tariq khan, Muhammad Amir Ghadi, Yazeed Yasin Hamam, Habib Department of Computer Science & Information Technology Virtual University of Pakistan Lahore 54000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 12555 United Arab Emirates Faculty of Engineering University de Moncton Moncton E1A3E9 NB Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles BP Libreville 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Immersive technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR), create realistic digital experiences by overlaying digital elements. This study aims to highlight the benefits of utilizing VR technology in education, particularly in universities and higher education institutions, by integrating VR with educational theories to create engaging and interactive learning experiences that enhance understanding and retention. This review includes a comprehensive analysis of 103 works, synthesizing existing literature to evaluate the suitability of various educational approaches and theories for implementation in VR-based educational applications. The findings emphasize VR's potential to transform learning by providing immersive experiences that bridge different domains and encourage engagement. By integrating learning theory into VR development, meaningful learning environments can be created, improving student understanding and retention across diverse learning settings. The study concludes that the combination of technology and educational theories provides a novel approach to motivate students and foster deep learning in VR laboratories. © 2024 The Authors

关键词： Augmented reality Educational theories engineering education Immersive technology Interactive learning VR laboratories

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Soft Contact Simulation and Manipulation Learning of Deformable Objects with Vision-based Tactile Sensor

arXiv

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arXiv 2024年

作者： Shan, Jianhua Sun, Yuhao Zhang, Shixin Sun, Fuchun Chen, Zixi Shen, Zirong Stefanini, Cesare Yang, Yiyong Luo, Shan Fang, Bin School of Mechanical Engineering Anhui University of Technology China Institute for Artificial Intelligence Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing100083 China Biorobotics Institute The Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa56127 Italy Department of Engineering King’s College London LondonWC2R 2LS United Kingdom Zhili College Tsinghua University Beijing100084 China

Deformable object manipulation is a challenging problem due to its complex deformable properties. With the development of artificial intelligence, learning-based methods have shown outstanding performance in the manipulation of robots. Previous works have investigated the manipulation of deformable objects via Reinforcement Learning (RL) in simulation. However, they approximate object deformation with particles, and particle states are employed as observations, which are not available in reality. To address these issues, we propose a novel approach utilizing Vision-Based Tactile Sensors (VBTSs) as the end-effector in simulation to produce observations like relative position, squeezed area, and object contour, which are transferable to real robots. However, this makes contact simulation more complex due to the gel layer of VBTS is also a deformable object. Existing simulation methods of the vision-based tactile sensor can only simulate elastic deformation, while the simulation of plastic and elastoplastic deformation is poor. This must be overcome for a more realistic contact simulation between deformable objects. In this work, we build a new contact simulation environment for deformable objects including elastic, plastic, and elastoplastic deformation. We utilize RL strategies to train agents in the simulation, and expert demonstrations are applied for challenging tasks. To achieve simulation-to-real-world (sim-to-real) transfer, transferable observations like relative position, squeezed area, and object contour are applied in RL training. Also, we build a real experimental platform, including a VBTS, to complete the sim-to-real work and robustness testing. Leveraging the developed simulation and real experiment setup, a benchmark has been created for contact simulation and manipulation learning of deformable objects with VBTS. Our work innovatively proposes a strategy that employs high-resolution VBTSs in contact simulation and manipulation of deformable objects. W

关键词： Reinforcement learning

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From Conception to Deployment: Intelligent Stroke Prediction Framework using Machine Learning and Performance Evaluation

arXiv

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arXiv 2023年

作者： Ismail, Leila Materwala, Huned Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Stroke is the second leading cause of death worldwide. Machine learning classification algorithms have been widely adopted for stroke prediction. However, these algorithms were evaluated using different datasets and evaluation metrics. Moreover, there is no comprehensive framework for stroke data analytics. This paper proposes an intelligent stroke prediction framework based on a critical examination of machine learning prediction algorithms in the literature. The five most used machine learning algorithms for stroke prediction are evaluated using a unified setup for objective comparison. Comparative analysis and numerical results reveal that the Random Forest algorithm is best suited for stroke prediction. © 2023, CC BY-NC-ND.

关键词： Data Analytics

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Driving Behavior Safety Assessment in Edge Computing Using Multitask Discriminative TS Fuzzy Model

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IEEE Transactions on Consumer Electronics 2024年

作者： Gu, Yi Chen, Zhiyu Wang, Lu Shen, Jinsong Bashir, Ali Kashif Wang, Wei Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China Jiangnan University Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Wuxi214122 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Jiangsu Keli Internet Information Technology Co. Ltd Jingjiang214500 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Woxsen University Woxsen School of Business Hyderabad502 345 India Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China

Driving behavior safety analysis in edge computing involves the real-time collection and processing of vehicle driving data. Fuzzy models have advantages in analyzing driving behavior due to their excellent ability to handle uncertain information. This study develops a multitask discriminative Takagi-Sugeno fuzzy model (MD-TS-FM) for driving behavior safety assessment. To comprehensively analyze the correlation and differences in multitask driving behaviors, the designed consequent part consists of two parts: task-shared part reflects the shared intrinsic structure information that are consistent across different driving tasks, and task-specific part reflects distinct characteristics and variations specific to each task, allowing the model to address the unique aspects of different driving behaviors. Accordingly, the task-shared consequent part is characterized by low-rank property, which mines global structural information within the fuzzy space;whereas the task-specific consequent part exhibits sparsity, which removes non-discriminative and irrelevant information. This sparsity ensures that the model focuses on the most critical features for each specific task. Furthermore, a discriminative diversity term is introduced to enhance the diversity between tasks, which explores the consistent information of task-shared consequents while reducing the overlap of task-specific consequents. Experimental results indicate that the MD-TS-FM model can be effectively applied to driving behavior safety assessment. © 1975-2011 IEEE.

关键词： driving behavior safety assessment edge computing fuzzy model multitask learning

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Textured growth and electrical characterization of Zinc Sulfide on back-end-of-the-line (BEOL) compatible substrates

arXiv

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arXiv 2025年

作者： Wu, Claire Surendran, Mythili Priyoti, Anika Tabassum Anilkumar, Gokul Wu, Cheng-Hsien Wang, Chun-Chen Kuo, Cheng-Chen Kumasubramanian, Harish Lin, Kenta Butler, Amari Kapadia, Rehan Bao, Xinyu Ravichandran, Jayakanth Mork Family Department of Chemical Engineering and Materials Science University of Southern California Los AngelesCA90089 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Core Center for Excellence in Nano Imaging University of Southern California Los AngelesCA90089 United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Corporate Research Taiwan Semiconductor Manufacturing Company Ltd. Hsinchu Taiwan

Scaling of transistors has enabled continuous improvement in the performance of logic devices, especially with contributions from materials engineering. However, there is a need to surpass the horizontal limitations in chip manufacturing and incorporate the vertical or third dimension. To enable monolithic three-dimensional (M3D) integration of high-performance logic, one needs to solve the fundamental challenge of low temperature (1. Metal oxides, especially indium oxides alloyed with gallium and tungsten2,3, are promising n-type semiconductor channel materials;however there is a lack of p-type channel materials that can meet the stringent synthesis conditions of BEOL manufacturing. Zinc sulfide (ZnS), a transparent wide band-gap semiconductor, has shown room temperature p-type conductivity when doped with copper and nitrogen, and crystallizes below 400°C.4 Here, we report growth of crystalline thin films of ZnS by pulsed laser deposition (PLD) on a variety of amorphous and polycrystalline surfaces including silicon nitride (SiNx), thermal silicon dioxide (SiO2), yttrium oxide (Y2O3), hafnium dioxide (HfO2), sapphire (Al2O3), platinum (Pt), and titanium nitride (TiN). X-ray diffraction scans show out-of-the-plane texturing of ZnS on all surfaces. In-plane crystalline quality is investigated using grazing incidence wide-angle X-ray scattering measurements. Surface and interface quality is measured using X-ray reflectivity and atomic force microscopy measurements. Electrical characterization of the ZnS films is done by J-V measurements of ZnS on platinum and metal-oxide-semiconductor capacitor (MOSCAP) measurements of ZnS on SiO2 on heavily doped silicon. The J-V measurements indicate low leakage current on the order of 10-5 A/cm-2 with electric field of 0.40 MV cm-1 and the MOSCAP characteristics show bilayer capacitor behavior, which points to ZnS being highly intrinsic with very low unintentional, electrically active point defects. Further work on doping ZnS with

关键词： Silicon nitride

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Toward Automated Detection of Microbleeds with Anatomical Scale Localization: A Complete Clinical Diagnosis Support Using Deep Learning

arXiv

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arXiv 2023年

作者： Kim, Jun-Ho Noh, Young Lee, Haejoon Lee, Seul Kim, Woo-Ram Kang, Koung Mi Kim, Eung Yeop Al-Masni, Mohammed A. Kim, Dong-Hyun Department of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of Neuroscience Research Institute Gachon University Incheon Korea Republic of Department of Neurology Gachon University College of Medicine Gil Medical Center Incheon Korea Republic of Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States Department of Radiology Seoul National University Hospital Seoul Korea Republic of Department of Radiology Seoul National University College of Medicine Seoul Korea Republic of Department of Radiology Gachon University College of Medicine Gil Medical Center Incheon Korea Republic of Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul05006 Korea Republic of

Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to various cerebrovascular diseases depending on their anatomical location, including cognitive decline, intracerebral hemorrhage, and cerebral infarction. However, manual detection of CMBs is a time-consuming and error-prone process because of their sparse and tiny structural properties. The detection of CMBs is commonly affected by the presence of many CMB mimics that cause a high false-positive rate (FPR), such as calcification and pial vessels. This paper proposes a novel 3D deep learning framework that does not only detect CMBs but also inform their anatomical location in the brain (i.e., lobar, deep, and infratentorial regions). For the CMB detection task, we propose a single end-to-end model by leveraging the U-Net as a backbone with Region Proposal Network (RPN). To significantly reduce the FPs within the same single model, we develop a new scheme, containing Feature Fusion Module (FFM) that detects small candidates utilizing contextual information and Hard Sample Prototype Learning (HSPL) that mines CMB mimics and generates additional loss term called concentration loss using Convolutional Prototype Learning (CPL). For the anatomical localization task, we exploit the U-Net segmentation network to segment the brain anatomical structures. This task does not only tell to which region the CMBs belong but also eliminate some FPs from the detection task by utilizing anatomical information. We utilize Susceptibility-Weighted Imaging (SWI) and phase images as 3D input to efficiently capture 3D information. The results show that the proposed RPN that utilizes the FFM and HSPL outperforms the vanilla RPN and achieves a sensitivity of 94.66% vs. 93.33% and an average number of false positives per subject (FPavg) of 0.86 vs. 14.73. Also, the anatomical localization task further improves the detection performance by reducing the FPavg to 0.56 while

关键词： Brain

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A holistic review of the effects of dust buildup on solar photovoltaic panel efficiency

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Solar Compass 2025年 13卷

作者： Yakubu, Sufyan Samikannu, Ravi Gawusu, Sidique Wetajega, Samuel Dodobatia Okai, Victor Shaibu, Abdul-Kadir Seini Workneh, Getachew Adam Sustainable Energy Center of Excellence Addis Ababa Science and Technology University P.O. Box 16417 Addis Ababa Ethiopia Department of Industrial Chemistry Addis Ababa Science and Technology University P.O. Box 16417 Addis Ababa Ethiopia Pan African University Institute of Water and Energy Sciences-University of Tlemcen Tlemcen Algeria Department of Electrical and Electronic Engineering Bolgatanga Technical University Bolgatanga Ghana Department of Electrical Computer & Telecommunications Engineering Botswana International University of Science and Technology Palpaye Botswana Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Whiting School of Engineering Johns Hopkins University Baltimore MD United States Department of Chemistry Joseph Sarwuan Tarka University Benue State Makurdi Nigeria Department of Electrical and Electronic Engineering Dr Hilla Limann Technical University Wa Ghana

Understanding the drivers of photovoltaic system performance is very important in the backdrop of the increasingly significant role that solar energy plays in mitigating carbon emissions. Dust accumulation on surface of photovoltaic panel may result in a high degradation of PVs' efficiency with losses ranging from 10% in mild conditions to over 40% in arid regions. This review systematically explores the effects of dust deposition on PV performance, emphasizing the role of environmental factors such as wind speed, precipitation, humidity, and dust composition. Dust particles impede light transmission, raise cell temperatures, and increase resistive losses, leading to reduced output power. Notable efficiency reductions are linked to specific dust types, such as coal dust (up to 64% losses), fine sand (32%), and gypsum (30%), as highlighted by global case studies. This review further underlines how dust accumulation patterns are influenced by complex interactions of environmental factors such as wind precipitation and humidity. The installation characteristics, such as the height of the panels and their orientation, further exacerbate or mitigate the impact of soiling. This study has been designed to put a great deal of significance on the maximisation of solar PV efficiency, given the critical requirements for global renewable energy targets. It flags some promising cleaning methods and emphasises a holistic approach to the optimisation of PV system design and maintenance practices to empower the widespread adoption of this transformative clean energy solution. © 2024 The Author(s)

关键词： Cleaning techniques Dust Efficiency Performance degradation Solar photovoltaic

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