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“For the moment, I am not F*cking,” I am Tweeting: Platforms of / as Sexuality
Get PDFThis article develops the argument that digital platforms are significantly infused with originary (and unconscious) residues of the sexual. Drawing on Laplancheian conceptualizations of sexuality, I argue that the digital has always been sexual(ised) in itself – a process that precedes and exceeds the erotic or pornographic. For Laplanche, sexuality is constitutive of the human subject as such. Infantile sexuality is shaped and transformed in an enigmatic relation with the caregiver. Drawing on this model as an analogy, I claim that users are drawn to platforms because they (unconsciously) desire to return to infantile sexuality and a holding environment but are disciplined and policed by platforms into adopting modes of adult sexuality that are shaped by ideology and the social. Platforms resemble a child – caregiver relation that is further complicated by other users who, from the perspective of the individual user, occupy the position of siblings. The user – sibling relation is marked by competition as well as moments of care and cooperation. As users work, chat, share, like, and practically live online and via networked objects, their forms of usage mimic, or come close to, the sensual-affective rhythms of sexuality itself
Mathematics RTI/MTSS Implementation: A Literature Review from the Perspective of Implementation Science
Get PDFThis article reviews published research on implementing the Response to Intervention (RTI)/Multi-tiered System of Support (MTSS) educational framework in mathematics at schools. We utilized the Implementation Driver framework from Implementation Science (Eccles & Mittman, 2006) to analyze current RTI/MTSS implementation practices. Eleven studies qualified to be included in this research. Findings showed more research is needed to expand the investigations in implementation fidelity, systems intervention, facilitative administration, decision-support data systems, coaching, and selection driver
Growing in STEM: Hands-On Education via Tower Gardening
Get PDFSole use of a textbook as the primary source of learning in the middle school setting has been linked to potentially limit students’ conceptual understanding of complex content (Driscoll et al., 1994), while the positive impact of hands-on learning has been well documented (Ekwueme et al., 2015; Satterthwait, 2010). Ideally, when a textbook is used in conjunction with content applied in a hands-on manner, student engagement and understanding of standards-based content are increased. In a grant funded collaboration between the Northeast Indiana STEM Education Resource Center housed at Purdue University Fort Wayne and an East Allen Community Schools middle school science classroom, a vertical tower garden was purchased and used by students under the guidance of their teacher to learn plant-based science content. This research brief examines some of the many benefits and ways integration of a tower garden in the classroom allowed student appreciation of science to grow
SEA-PHAGES Course-Based Undergraduate Research Experience for Creating a Biotechnology Workforce Development Pipeline
Get PDFPurdue University has been a member of the SEA-PHAGES (Science Education Alliance-Phage Hunters Advancing Genomics and Evolutionary Science) program since 2011. This program, created in conjunction with the Howard Hughes Medical Institute (HHMI), facilitates undergraduate curriculum for the authentic discovery of novel bacteriophages within the classroom. Since joining the program, undergraduates at Purdue have harnessed wet lab and bioinformatics principles to contribute over 200 previously uncharacterized bacteriophages and 25 novel genomes to the wider scientific literature. The SEA-PHAGES classes at Purdue have resulted in tangible professional deliverables for students through conference presentations and publications. Student outcomes also include transferable skills such as knowledge of aseptic technique, the nature of science, and genomic annotation which positions the classes as ideal platforms for workforce development for the biotechnology industry
Automated Delineation of Visual Area Boundaries and Eccentricities by a CNN Using Functional, Anatomical, and Diffusion-weighted MRI Data
Get PDFDelineating visual field maps and iso-eccentricities from fMRI data is an important but time-consuming task for many neuroimaging studies on the human visual cortex because the traditional methods of doing so using retinotopic mapping experiments require substantial expertise as well as scanner, computer, and human time. Automated methods based on gray-matter anatomy or a combination of anatomy and functional mapping can reduce these requirements but are less accurate than experts. Convolutional Neural Networks (CNNs) are powerful tools for automated medical image segmentation. We hypothesize that CNNs can define visual area boundaries with high accuracy. We trained U-Net CNNs with ResNet18 backbones to predict either V1, V2, and V3 boundaries or 5 regions of iso-eccentricity using human-labeled maps. Separate CNNs were trained to predict these regions using different combinations of the following input data: (1) anatomical data from a T1-weighted image only, (2) anatomical data from T1-weighted and T2*-weighted images, (3) white-matter tract endpoints from diffusion-weighted imaging, (4) functional data from retinotopic mapping. All CNNs using functional data had cross-validated accuracy that was statistically indistinguishable from the inter-rater reliability of the training dataset (dice coefficient of 92%) while the CNNs lacking functional data had lower but similar accuracies (~75%). Existing models that do not use CNNs had accuracies lower than any of the CNNs. These results demonstrate that with current methods and data quality, CNNs can replace the time and effort of human experts in manually defining early retinotopic maps, but cannot yet replace the acquisition of functional data
A Signal Detection Model for the Analysis of Continuous Response Gradients and an Application to Confidence Rating Data
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Cesium Lead Bromide Quantum Dot Superlattices: Quantifying Structural Heterogeneity and Its Influence on Exciton Delocalization
No full textColloidal cesium lead bromide (CsPbBr3) quantum dots (QDs) have emerged as an exciting class of quantum emitters due to their near-unity quantum yields, large oscillator strengths, and long coherence time. Ordered superlattices (SLs) grown from these QDs exhibit emergent properties resulting from their assembly. In this work, we explore the self-assembly, disorder, and superradiant properties of 3D superlattices of CsPbBr3 to understand how structural heterogeneity influences optical properties. A thorough understanding of the competition between coherence and dephasing from phonon scattering and energetic disorder is currently lacking in the literature. Here, we present an investigation of exciton coherence in perovskite QD solids using temperature-dependent photoluminescence linewidth and lifetime measurements. The properties of perovskite QDS described above should also enable them to overcome hurdles experienced by other materials that limit solid-state superradiance, such as fast dephasing processes from inherent disorder and thermal fluctuations. Our results demonstrate that excitons can coherently delocalize in highly ordered CsPbBr3 superlattices leading to superradiant emission. We observe loss of coherence and exciton localization to a single QD at higher temperatures, resulting from scattering by optical phonons. At low temperatures, static disorder and defects limit exciton coherence, and a wide range of coherence numbers are observed across a self-assembled sample of SLs. These results highlight the promise and challenge in achieving long-range coherence in perovskite QD solids. A thorough understanding of structural heterogeneity in CsPbBr3 quantum dot superlattices is necessary for the realization of robust exciton coherence in these systems. 3D SLs self-assemble from a colloidal solution of cubic QDs as the solvent evaporates, leading to SLs ranging widely in macroscopic size, shape, and aspect ratio. Scanning transmission electron microscopy (STEM) coupled to fast-Fourier transform (FFT) analysis is utilized to characterize the structural properties of individual SLs, such as the average constituent quantum dot size, size dispersity, and number of crystalline domains. Analysis reveals that SLs are structurally heterogeneous but tend to have a narrower size distribution than the precursor solution due to size selection that occurs during evaporative self-assembly. We directly correlate STEM-FFT structural properties to low-temperature photoluminescence spectra for individual SLs, demonstrating that substructure in the photoluminescence peak arises from multiple, locally-ordered domains within the SL. In addition, we show that long-range structural disorder in a SL does not necessarily impact short-range phenomena such as exciton delocalization
Enhancing Interpretability and Adaptability of Manufacturing Equipment Health Models and Establishment of Cost Models for Maintenance Decisions
No full textThe integration of Industry 4.0 technologies such as cyber-physical systems, the internet of things, and artificial intelligence has revolutionized the traditional manufacturing systems, making them smart and digital. Maintenance, a critical component of manufacturing, has been incorporated with data-driven strategies such as prognostic and health management (PHM) to improve production efficiency and reliability. This is achieved by real-time sensing and AI-based modeling, which monitor the health condition of operational equipment for fault detection or failure prediction. The results generated by these models provide crucial support for decision-making processes in manufacturing, ranging from maintenance scheduling to production management. This research focuses on data-driven machine health models based on deep learning in manufacturing systems and explores three directions towards the practical implementation of PHM: model interpretation, model adaptability and robustness enhancement, and cost-benefit analysis of maintenance strategies. In terms of model interpretation, the RNN-LSTM-based model prediction on bearing health estimation was analyzed, and the relationship between the model input and output was investigated. The adoption of the LRP technique improved the explainability of the LSTM model beyond predictive maintenance applications. To enhance model adaptability and robustness, a Transformer-based method was developed for fault diagnosis and novel fault detection, which achieved superior performance compared to conventional fault classification AI-based models. The decision-making aspect of PHM was addressed by conducting a cost-benefit analysis on different maintenance strategies, which provided a new perspective for decision-makers in maintenance management