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Item type: Publication , Access status: Metadata only , 3D-Printed Flexible Microfluidic Health Monitor for In Situ Sweat Analysis and Biomarker Detection(2024-06-28) Chen, Chuchu; Fu, Yonghao; Sparks, Sonja S.; Lyu, Zhaoyuan; Pradhan, Arijit; Ding, Shichao; Boddeti, Narasimha; Liu, Yun; Lin, Yuehe; Du, Dan; Qiu, KaiyanWearable sweat biosensors have shown great progress in noninvasive, in situ, and continuous health monitoring to demonstrate individuals’ physiological states. Advances in novel nanomaterials and fabrication methods promise to usher in a new era of wearable biosensors. Here, we introduce a three-dimensional (3D)-printed flexible wearable health monitor fabricated through a unique one-step continuous manufacturing process with self-supporting microfluidic channels and novel single-atom catalyst-based bioassays for measuring the sweat rate and concentration of three biomarkers. Direct ink writing is adapted to print the microfluidic device with self-supporting structures to harvest human sweat, which eliminates the need for removing sacrificial supporting materials and addresses the contamination and sweat evaporation issues associated with traditional sampling methods. Additionally, the pick-and-place strategy is employed during the printing process to accurately integrate the bioassays, improving manufacturing efficiency. A single-atom catalyst is developed and utilized in colorimetric bioassays to improve sensitivity and accuracy. A feasibility study on human skin successfully demonstrates the functionality and reliability of our health monitor, generating reliable and quantitative in situ results of sweat rate, glucose, lactate, and uric acid concentrations during physical exercise.Item type: Publication , Access status: Metadata only , Effectiveness assessment of recent large vision-language models(2024) Jiang, Yao; Yan, Xinyu; Ji, Ge Peng; Fu, Keren; Sun, Meijun; Xiong, Huan; Fan, Deng Ping; Khan, Fahad ShahbazThe advent of large vision-language models (LVLMs) represents a remarkable advance in the quest for artificial general intelligence. However, the models’ effectiveness in both specialized and general tasks warrants further investigation. This paper endeavors to evaluate the competency of popular LVLMs in specialized and general tasks, respectively, aiming to offer a comprehensive understanding of these novel models. To gauge their effectiveness in specialized tasks, we employ six challenging tasks in three different application scenarios: natural, healthcare, and industrial. These six tasks include salient/camouflaged/transparent object detection, as well as polyp detection, skin lesion detection, and industrial anomaly detection. We examine the performance of three recent open-source LVLMs, including MiniGPT-v2, LLaVA-1.5, and Shikra, on both visual recognition and localization in these tasks. Moreover, we conduct empirical investigations utilizing the aforementioned LVLMs together with GPT-4V, assessing their multi-modal understanding capabilities in general tasks including object counting, absurd question answering, affordance reasoning, attribute recognition, and spatial relation reasoning. Our investigations reveal that these LVLMs demonstrate limited proficiency not only in specialized tasks but also in general tasks. We delve deep into this inadequacy and uncover several potential factors, including limited cognition in specialized tasks, object hallucination, text-to-image interference, and decreased robustness in complex problems. We hope that this study can provide useful insights for the future development of LVLMs, helping researchers improve LVLMs for both general and specialized applications.Item type: Item , Access status: Metadata only , 3D-Printed Flexible Microfluidic Health Monitor for In Situ Sweat Analysis and Biomarker Detection(2024-06-28) Chen, Chuchu; Fu, Yonghao; Sparks, Sonja S.; Lyu, Zhaoyuan; Pradhan, Arijit; Ding, Shichao; Boddeti, Narasimha; Liu, Yun; Lin, Yuehe; Du, Dan; Qiu, KaiyanWearable sweat biosensors have shown great progress in noninvasive, in situ, and continuous health monitoring to demonstrate individuals’ physiological states. Advances in novel nanomaterials and fabrication methods promise to usher in a new era of wearable biosensors. Here, we introduce a three-dimensional (3D)-printed flexible wearable health monitor fabricated through a unique one-step continuous manufacturing process with self-supporting microfluidic channels and novel single-atom catalyst-based bioassays for measuring the sweat rate and concentration of three biomarkers. Direct ink writing is adapted to print the microfluidic device with self-supporting structures to harvest human sweat, which eliminates the need for removing sacrificial supporting materials and addresses the contamination and sweat evaporation issues associated with traditional sampling methods. Additionally, the pick-and-place strategy is employed during the printing process to accurately integrate the bioassays, improving manufacturing efficiency. A single-atom catalyst is developed and utilized in colorimetric bioassays to improve sensitivity and accuracy. A feasibility study on human skin successfully demonstrates the functionality and reliability of our health monitor, generating reliable and quantitative in situ results of sweat rate, glucose, lactate, and uric acid concentrations during physical exercise.Item type: Item , Access status: Metadata only , Effectiveness assessment of recent large vision-language models(2024-12) Jiang, Yao; Yan, Xinyu; Ji, Ge Peng; Fu, Keren; Sun, Meijun; Xiong, Huan; Fan, Deng Ping; Khan, Fahad ShahbazThe advent of large vision-language models (LVLMs) represents a remarkable advance in the quest for artificial general intelligence. However, the models’ effectiveness in both specialized and general tasks warrants further investigation. This paper endeavors to evaluate the competency of popular LVLMs in specialized and general tasks, respectively, aiming to offer a comprehensive understanding of these novel models. To gauge their effectiveness in specialized tasks, we employ six challenging tasks in three different application scenarios: natural, healthcare, and industrial. These six tasks include salient/camouflaged/transparent object detection, as well as polyp detection, skin lesion detection, and industrial anomaly detection. We examine the performance of three recent open-source LVLMs, including MiniGPT-v2, LLaVA-1.5, and Shikra, on both visual recognition and localization in these tasks. Moreover, we conduct empirical investigations utilizing the aforementioned LVLMs together with GPT-4V, assessing their multi-modal understanding capabilities in general tasks including object counting, absurd question answering, affordance reasoning, attribute recognition, and spatial relation reasoning. Our investigations reveal that these LVLMs demonstrate limited proficiency not only in specialized tasks but also in general tasks. We delve deep into this inadequacy and uncover several potential factors, including limited cognition in specialized tasks, object hallucination, text-to-image interference, and decreased robustness in complex problems. We hope that this study can provide useful insights for the future development of LVLMs, helping researchers improve LVLMs for both general and specialized applications.Item type: Publication , Access status: Metadata only , EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues(2025) Soni, Sagar; Dudhane, Akshay; Debary, Hiyam; Fiaz, Mustansar; Munir, Muhammad Akhtar; Danish, Muhammad Sohail; Fraccaro, Paolo; Watson, Campbell D.; Klein, Levente J.; Khan, Fahad Shahbaz; Khan, SalmanAutomated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and resource management. Existing generic VLMs do not perform well on Remote Sensing data, while the recent Geo-spatial VLMs remain restricted to a fixed resolution and few sensor modalities. In this paper, we introduce EarthDial, a conversational assistant specifically designed for Earth Observation (EO) data, transforming complex, multi-sensory Earth observations into interactive, natural language dialogues. EarthDial supports multi- spectral, multi-temporal, and multi-resolution imagery, enabling a wide range of remote sensing tasks, including classification, detection, captioning, question answering, visual reasoning, and visual grounding. To achieve this, we introduce an extensive instruction tuning dataset comprising over 11.11M instruction pairs covering RGB, Synthetic Aperture Radar (SAR), and multispectral modalities such as Near-Infrared (NIR) and infrared. Furthermore, EarthDial handles bi-temporal and multi-temporal sequence analysis for applications like change detection. Our extensive experimental results on 44 downstream datasets demonstrate that EarthDial outperforms existing generic and domain-specific models, achieving better generalization across various EO tasks. Our source codes and pre-trained models are at https://github.com/hiyamdebary/EarthDial.