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Eye Drop Instillation Success and Hand Function in Adults with Glaucoma: A Pilot Study

Ophthalmology Glaucoma 2025年第3期8卷 275-284页

作者： Weber, Madeline K. Kabil, Gül G. Niziol, Leslie M. Urrego, Daniel Duque Haire, Cameron Cain, Stephen M. Sample, Alanson P. Burke, David T. Brown, Susan H. Newman-Casey, Paula Anne University of Michigan Medical School Ann Arbor MI United States University of Michigan School of Kinesiology Ann Arbor MI United States Department of Ophthalmology and Visual Sciences University of Michigan W.K. Kellogg Eye Center Ann Arbor MI United States Department of Chemical and Biomedical Engineering West Virginia University Morgantown WV United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Department of Human Genetics University of Michigan Ann Arbor MI United States

Purpose: To investigate hand function and eye drop instillation success in adults with and without glaucoma. Design: Cross-sectional pilot study. Subjects: Adults aged ≥ 65 years with glaucoma who use eye drops daily and adults aged 65+ without glaucoma who do not regularly use eye drops. Methods: Hand function was evaluated using the Saehan Hydraulic Pinch Gauge, Jamar Hand Dynamometer, Grooved Pegboard Test (GPT), Arthritis Hand Function Tests (AHFT), Semmes-Weinstein Monofilament Test, and a tactile pattern recognition device. Eye drop instillation success was analyzed through videography. Main Outcome Measures: Proportion of successful eye drop instillation trials assessed by (1) overall success in getting at least one drop in the eye;(2) average number of drops dispensed;and (3) bottle tip contact with the eye, eyelashes, or skin. Hand function measures: mean pinch and grip strength, GPT and AHFT completion times, smallest detected monofilament, tactile pattern identification time, and accuracy. Results: A total of 25 participants with glaucoma and 79 participants without glaucoma were included. There was no difference in the proportion of trials where at least one drop was successfully instilled, disregarding bottle tip contact (glaucoma: 0.95, nonglaucoma: 0.91;P = 0.88). Participants with glaucoma were more likely to make contact between the bottle tip and the eye, eyelashes, or skin compared with those without (glaucoma: 0.49, nonglaucoma: 0.28;P = 0.01). Participants with glaucoma dispensed a similar number of drops as those without (glaucoma: 1.37, nonglaucoma: 1.46;P = 0.47). Participants with glaucoma had significantly reduced pinch strength (glaucoma: 4.8 kg, nonglaucoma 6.1 kg, P = 0.01), grip strength (glaucoma: 23.4 kg, nonglaucoma: 27.7 kg, P = 0.02), longer completion times for the GPT (glaucoma: 113.5 seconds, nonglaucoma: 85.5 seconds, P = 0.02), and specific AHFT tasks of fastening/unfastening buttons (glaucoma: 36.6 seconds, nonglaucoma: 27.7

关键词： Eye drop instillation success Glaucoma Hand function Sensorimotor deficits

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Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Agents to Enhance Young Adults' Career Exploration 25

Letters from Future Self: Augmenting the Letter-Exchange Exe...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Hayeon Jeon Suhwoo Yoon Keyeun Lee Seo Hyeong Kim Esther Hehsun Kim Seonghye Cho Yena Ko Soeun Yang Laura Dabbish John Zimmerman Eun-mee Kim Hajin Lim hci+d lab. Seoul National University Seoul Republic of Korea Department of Communication Seoul National University Seoul Republic of Korea Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA HCI Institute Carnegie Mellon University Pittsburgh Pennsylvania USA

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Correction to: Object search by a concept-conditioned object detector (Neural Computing and Applications, (2024), 36, 26, (16001-16021), 10.1007/s00521-024-09914-5)

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Neural Computing and Applications 2025年 1-2页

作者： Rigoni, Davide Serafini, Luciano Sperduti, Alessandro Department of Mathematics "Tullio Levi-Civita" University of Padova Via Trieste 63 PD Padua35131 Italy Human Inspired Technology Research Centre University of Padova Via Luzzatti 4 PD Padua35121 Italy Augmented Intelligence Center Bruno Kessler Foundation Via Sommarive 18 TN Povo38123 Italy Department of Pharmaceutical and Pharmacological Sciences University of Padova Via Marzolo 5 PD Padua35131 Italy Department of Information Engineering and Computer Science University of Trento Via Sommarive 9 TN Povo38123 Italy

Correction To: Neural Computing and Applications (2024) 36:16001–16021 https://***/10.1007/s00521-024-09914-5 In this article, the sentence ‘The models conditioned with the concepts are highlighted with the dove gray color’ in the footnote of Tables 1, 4 and 5 should be removed. Similarly, the sentence ‘Concept-conditioned models are highlighted with the dove gray color’ in the footnote of Tables 2 and 3 should be removed. In the section ‘Standard vs. concept-conditioned object detectors before filtering’, the sentence ‘Models conditioned with the concepts are highlighted with the dove gray color’ should be removed. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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Mediating The Marginal: A Quantitative Analysis of Curated LGBTQ+ Content on Instagram 25

Mediating The Marginal: A Quantitative Analysis of Curated L...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Garrett Souza Nina Lutz Katlyn M Turner Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts USA Department of Human Centered Design and Engineering University of Washington Seattle Washington USA Media Lab Massachusetts Institute of Technology Cambridge Massachusetts USA

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What Is So Deep About Deepfakes? A Multi-Disciplinary Thematic Analysis of Academic Narratives About Deepfake Technology

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2025年第1期6卷 64-79页

作者： Twomey, John Ching, Didier Aylett, Matthew Peter Quayle, Michael Linehan, Conor Murphy, Gillian University College Cork School of Applied Psychology CorkT12 CY82 Ireland Lero—The Science Foundation Ireland Research Centre for Software LimerickV94 NYD3 Ireland Heriot Watt University Mathematics and Computer Science Department EdinburghEH14 4AS United Kingdom CereProc Ltd. EdinburghEH3 9DR United Kingdom University of Limerick Centre for Social Issues Research Department of Psychology LimerickV94 T9PX Ireland University of KwaZulu-Natal School of Applied Human Sciences Department of Psychology Pietermaritzburg3201 South Africa

Deepfakes are a form of synthetic media that uses deep-learning technology to create fake images, video, and audio. The emergence of this technology has inspired much commentary and speculation from academics across a range of disciplines, who have contributed expert opinions regarding the implications of deepfake proliferation on fields such as law, politics, and entertainment. A systematic scoping review was carried out to identify, assemble, and critically analyze those academic narratives. The aim is to build on and critique previous attempts at defining the technology and categorizing the harms and benefits of deepfake technology. A range of databases were searched for relevant articles from 2017 to 2023, resulting in a large multi-disciplinary dataset of 102 papers, 181,659 words long, which were analyzed qualitatively through thematic analysis. Implications for future research include questioning the lack of research evidence for the supposed positives of deepfakes, recognizing the role that identity plays in deepfake technology, challenging the perceived accessibility/ believability of deepfakes, and proposing a more nuanced approach to the dichotomous "positive and negatives" of deepfakes. Furthermore, we show how definitional issues around what a deepfake is versus other forms of fake media feeds confusion around the novelty and impacts of deepfakes. © 2024 The Authors.

关键词： Deepfakes Reviews Generative AI Software Psychology Social networking (online) Organizations Lenses Law Internet

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Coach, Data Analyst, and Protector: Exploring Data Practices of Collegiate Coaching Staff 25

Coach, Data Analyst, and Protector: Exploring Data Practices...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Mollie Brewer Kevin Childs Spencer Thomas Celeste Wilkins Kristy Elizabeth Boyer Jennifer A. Nichols Kevin R.B. Butler Garrett F. Beatty Daniel P. Ferris Department Of Computer & Information Science & Engineering University of Florida Gainesville FL USA University Athletic Association Gainesville FL USA J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida Gainesville FL USA Department of Computer & Information Science & Engineering University of Florida Gainesville FL USA College of Health & Human Performance University of Florida Gainesville FL USA

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Misspecification-robust likelihood-free inference in high dimensions

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Computational Statistics 2025年 1-41页

作者： Thomas, Owen Sá-Leão, Raquel de Lencastre, Hermínia Kaski, Samuel Corander, Jukka Pesonen, Henri Oslo Centre for Biostatistics and Epidemiology University of Oslo Oslo Norway Laboratory of Molecular Microbiology of Human Pathogens Instituto de Tecnologia Química e Biológica António Xavier Universidade Nova de Lisboa Oeiras Portugal Laboratory of Molecular Genetics Instituto de Tecnologia Química e Biológica António Xavier Universidade Nova de Lisboa Oeiras Portugal Laboratory of Microbiology and Infectious Diseases The Rockefeller University NY United States Department of Computer Science Helsinki Institute of Information Technology Aalto University Espoo Finland Department of Computer Science University of Manchester Manchester United Kingdom Department of Mathematics and Statistics Helsinki Institute of Information Technology University of Helsinki Helsinki Finland Parasites and Microbes Wellcome Sanger Institute Hinxton United Kingdom Oslo Centre for Biostatistics and Epidemiology Oslo University Hospital Oslo Norway

Likelihood-free inference for simulator-based statistical models has developed rapidly from its infancy to a useful tool for practitioners. However, models with more than a handful of parameters still generally remain a challenge for the Approximate Bayesian Computation (ABC) based inference. To advance the possibilities for performing likelihood-free inference in higher dimensional parameter spaces, we introduce an extension of the popular Bayesian optimisation based approach to approximate discrepancy functions in a probabilistic manner which lends itself to an efficient exploration of the parameter space. Our approach achieves computational scalability for higher dimensional parameter spaces by using separate acquisition functions, discrepancies, and associated summary statistics for distinct subsets of the parameters. The efficient additive acquisition structure is combined with exponentiated loss-likelihood to provide a misspecification-robust characterisation of posterior distributions for subsets of model parameters. The method successfully performs computationally efficient inference in a moderately sized parameter space and compares favourably to existing modularised ABC methods. We further illustrate the potential of this approach by fitting a bacterial transmission dynamics model to a real data set, which provides biologically coherent results on strain competition in a 30-dimensional parameter space. © The Author(s) 2025.

关键词： Approximate Bayesian computation Bacterial transmission dynamics High-dimensional inference Loss likelihoods

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Applied intelligence with deep learning assisted automated sarcasm recognition in twitter data

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Alexandria Engineering Journal 2025年 128卷 79-91页

作者： Muhammad Swaileh A. Alzaidi Taghreed Ali Alsudais Majdy M. Eltahir Shouki A. Ebad Asma Alshuhail Mohammed Alshahrani Sultan Alanazi Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Teaching and Learning College of Education and Human Development Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Computer Department Applied College Najran University Najran 66462 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia

Sarcasm is essential in human interaction, particularly on social networking, where individuals share their feelings through criticism, humour, and satire. Sarcasm detection is vital in understanding the context of communication and the sentiment on platforms such as Twitter. This presents sarcasm detection as a complex problem in natural language processing (NLP). The challenges and importance upsurge, particularly in languages such as Arabic and Urdu, where resources for NLP are inadequate. The classical rule-based techniques need to improve performance owing to sarcasm's context-based and subtle nature. On the other hand, the current developments in deep learning (DL) and NLP offer efficient solutions. Therefore, this study presents an Applied Intelligence with Deep Learning Assisted Automated Sarcasm Recognition Approach (AIDL-ASRA) on Twitter Data. The presented AIDL-ASRA technique concentrates on identifying sarcastic and non-sarcastic contexts in tweets. In the AIDL-ASRA technique, the initial pre-processing stage is executed with different subprocesses. Next, Global Vectors for Word Representation (GloVe)-based word embedding process occurs. The AIDL-ASRA technique utilizes the Discriminative Deep Belief Networks (DDBN) method for sarcasm recognition. The DBN model's effectiveness is improved using the cat swarm optimization (CSO) model. A set of experiments were involved in evaluating the performance of the AIDL-ASRA method under the Arabic sarcastic dataset comprising 10,000 tweets. The experimental validation of the AIDL-ASRA method attained a superior accuracy value of 91.25 % over existing techniques under distinct measures.

关键词： Sarcasm Recognition Sentiment Analysis Twitter Data Deep Learning Cat Swarm Optimization Word Embedding

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Dynamic Localisation of Spatial-Temporal Graph Neural Network 25

Dynamic Localisation of Spatial-Temporal Graph Neural Networ...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Wenying Duan Shujun Guo Zimu Zhou Wei Huang Hong Rao Xiaoxi He Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau Macao Macao Macao

ISBN: (纸本)9798400712456

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings.

关键词： graph sparsification

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Neoteric Threat Intelligence Ensuring Digital Sovereignty and Trust through ML-Infused Proactive Defense Analytics for NEXT-G and Beyond Ecosystems

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Procedia computer science 2025年 254卷 39-47页

作者： Sudhakar Kumar Sunil K. Singh Rakesh Kumar Chandra Kumari Subba Kwok Tai Chui Brij B. Gupta Department of CSE Chandigarh College of Engineering and Technology Chandigarh India GL Bajaj Institute of Tech. & Mgmt Greater Noida UP India Sikkim University India Hong Kong Metropolitan University (HKMU) Hong Kong Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan University of Economics and Human Science Warsaw Poland

In the domain of Cyber-Physical Systems (CPS) and the Internet of Things (IoT), this research presents a novel approach to Neoteric Threat Intelligence ensuring Digital Sovereignty and Trust through ML-Infused Proactive Defense Analytics for NEXT-G and Beyond Ecosystems. As Sixth Generation and Beyond (6G and B) wireless networks undergo rapid evolution, our framework is designed to proactively anticipate and counter security incidents by utilizing advanced machine learning algorithms. This approach effectively addresses the shortcomings of conventional models, ensuring that digital assets and communications remain secure, trustworthy, and under rightful control. The study delves into the theoretical integration of this paradigm within the NextG network architecture, reinforcing digital sovereignty through a dynamic and adaptable defense mechanism. In-depth technical examinations include advanced machine learning algorithms, adaptive defenses, and scalability considerations. By critically analyzing and comparing existing security approaches, this study significantly advances technical knowledge and practical applications for wireless network security, supporting defenses against the evolving and complex threats characteristic of the 6G and Beyond era.

关键词： Neoteric Threat Intelligence Proactive Defense Analytics 6GandB Wireless Networks Machine Learning Algorithms NextG Network Architecture Digital Sovereignty and Trust

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