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1. Extending Coverage of Stationary Sensing Systems with Mobile Sensing Systems for Human Mobility Modeling

   https://doi.org/10.1145/3411827  

   Yang, Yu; Fang, Zhihan; Xie, Xiaoyang; Zhang, Fan; Liu, Yunhuai; Zhang, Desheng (September 2020, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Human mobility modeling has many applications in location-based services, mobile networking, city management, and epidemiology. Previous sensing approaches for human mobility are mainly categorized into two types: stationary sensing systems (e.g., surveillance cameras and toll booths) and mobile sensing systems (e.g., smartphone apps and vehicle tracking devices). However, stationary sensing systems only provide mobility information of human in limited coverage (e.g., camera-equipped roads) and mobile sensing systems only capture a limited number of people (e.g., people using a particular smartphone app). In this work, we design a novel system Mohen to model human mobility with a heterogeneous sensing system. The key novelty of Mohen is to fundamentally extend the sensing coverage of a large-scale stationary sensing system with a small-scale sensing system. Based on the evaluation on data from real-world urban sensing systems, our system outperforms them by 35% and achieves a competitive result to an Oracle method. 

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2. Edulyze: Learning Analytics for Real-World Classrooms at Scale

   https://doi.org/10.18608/jla.2024.8367  

   Patidar, Prasoon; Ngoon, Tricia; Vogety, Neeharika; Behari, Nikhil; Harrison, Chris; Zimmerman, John; Ogan, Amy; Agarwal, Yuvraj (August 2024, Journal of Learning Analytics)

   Classroom sensing systems can capture data on teacher-student behaviours and interactions at a scale far greater than human observers can. These data, translated to multi-modal analytics, can provide meaningful insights to educational stakeholders. However, complex data can be difficult to make sense of. In addition, analyses done on these data are often limited by the organization of the underlying sensing system, and translating sensing data into meaningful insights often requires custom analyses across different modalities. We present Edulyze, an analytics engine that processes complex, multi-modal sensing data and translates them into a unified schema that is agnostic to the underlying sensing system or classroom configuration. We evaluate Edulyze’s performance by integrating three sensing systems (Edusense, ClassGaze, and Moodoo) and then present data analyses of five case studies of relevant pedagogical research questions across these sensing systems. We demonstrate how Edulyze’s flexibility and customizability allow us to answer a broad range of research questions made possible by Edulyze’s translation of a breadth of raw sensing data from different sensing systems into relevant classroom analytics.  

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3. Microbial Primer: LuxR-LuxI Quorum Sensing: This article is part of the Microbial Primer collection.

   https://doi.org/10.1099/mic.0.001343  

   Soto-Aceves, Martin P; Diggle, Stephen P; Greenberg, E Peter (September 2023, Microbiology)

   Quorum sensing is a term describing bacterial cell-to-cell communication systems for monitoring and responding to changes in population density. This primer serves as an introduction to the canonical LuxR-LuxI-type quorum sensing circuits common to many species of Gram-negative bacteria. Quorum sensing can synchronize behaviours across a community. Different species employ quorum sensing strategies to control specific behaviours such as bioluminescence, virulence factor production, secondary metabolite production, and biofilm formation. 

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4. Improved Hydrogen Sensitivity and Selectivity in PdO with Metal-Organic Framework Membrane

   https://doi.org/10.1149/1945-7111/abc0ac  

   Gardner, David W.; Xia, Yong; Fahad, Hossain M.; Javey, Ali; Carraro, Carlo; Maboudian, Roya (October 2020, Journal of The Electrochemical Society)

   Metal-organic frameworks (MOFs) are highly designable porous materials and are recognized for their exceptional selectivity as chemical sensors. However, they are not always suitable for incorporation with existing sensing platforms, especially sensing modes that rely on electronic changes in the sensing material (e.g., work-function response or conductometric response). One way that MOFs can be utilized is by growing them as a porous membrane on a sensing layer and using the MOF to affect the electronic structure of the sensing layer. In this paper, a proof-of-concept for electronic modulation with MOFs is demonstrated. A PdO nanoparticle sensing layer on a chemical-sensitive field-effect-transistor is made more sensitive to a reducing gas, hydrogen, and less sensitive to oxidizng molecules, like H₂S and NO₂, by growing a layer of the MOF “ZIF-8” over the nanoparticles. The proposed mechanism is supported by X-ray photoelectron spectroscopy showing that the ZIF-8 membrane partially reduces the PdO sensing layer. 

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5. A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines

   https://doi.org/10.1177/26339137231170825  

   Rashid, Md Tahmid; Wei, Na; Wang, Dong (April 2023, Collective Intelligence)

   Propelled by the omnipresence of versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for decisively interpreting the real world. However, various limitations hinder physical sensing’s effectiveness in critical scenarios such as disaster response and urban anomaly detection. Meanwhile, social sensing is contriving as a pervasive sensing paradigm leveraging observations from human participants equipped with portable devices and ubiquitous Internet connectivity to perceive the environment. Despite its virtues, social sensing also inherently suffers from a few drawbacks (e.g., inconsistent reliability and uncertain data provenance). Motivated by the complementary strengths of the two sensing modes, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that explores the collective intelligence of humans and machines to reconstruct the “state of the world,” both physically and socially. While a good number of interesting SPS applications have been studied, several critical unsolved challenges still exist in SPS. In this paper, we provide a comprehensive survey of SPS, emphasizing its definition, key enablers, state-of-the-art applications, potential research challenges, and roadmap for future work. This paper intends to bridge the knowledge gap of existing sensing-focused survey papers by thoroughly examining the various aspects of SPS crucial for building potent SPS systems. 

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