Measuring the Accuracy of Crowd Counting using Wi-Fi Probe-Request-Frame Counting Technique

Wi-Fi in smartphones are designed to periodically transmit probe-request-frame to determine when a known access point is within range and by capitalizing this Wi-Fi behavior, crowd counting and analysis have been done by continuous monitoring and counting these Wi-Fi frames. The proliferation of Wi-Fi enabled mobile devices and the ever-increasing number of mobile devices in use, suggests opportunities for developing lowcost crowd counting and analysis solution. This work attempt to measure how well do monitoring and counting these Wi-Fi frames correlate with the actual number of people presence in a crowd. In this paper, we also compare the pros and cons of various crowd counting technologies, describe the system that we used for counting Wi-Fi frames and compare its accuracy against manual crowd counting technique in an event involving the public continuously for 8 hours. The results are promising, the correlation between manual counting and Wi-Fi frames counting is 0.89322. In addition to that, the Wi-Fi frames counting technique can even reveal the retention rate of the crowd

Thioflavin dye degradation by using magnetic nanoparticles augmented PolyvinylideneFlouride (PVDF) microcapsules / Mohamed Syazwan Osman ... [et al.]

Microcapsule has remarkable advantages in engineering application for pollutants removal and biomedical field for transportation. It has obviously drawn attention from the research community. Undeniably, it does have shortages but the key is to balance both the advantages and limitations to enhance microcapsule benefits. In environmental engineering applications, microcapsules could serve as encapsulation agents of nanoparticles (NPs) to drastically reduce the risk associated to nano-toxicity when it is indirect contact with surroundings. In addition, this technique could improve the physical contact and promote catalytic degradations of pollutants while exhibit better recyclability without loss of activity after multiple catalytic degradation cycles. Even though magnetic responsiveness of capsules can be used for ease of separation, one of the constraints is that the encapsulated particles will restrict the performance of capsules materials in pollutants removal. However, encapsulated magnetite particles interact with polymeric matrix chains and thus tying up the chains as knot which can restrict the expansions of whole capsules. Some-times, capsules shell is designated to remove certain target contaminants and so does for encapsulated particles. This may possibly reduce or increase the removal performance of integrated capsules which depends on the target contaminants and the underlying mechanism involved in pollutant removal. Hence, this work primarily focuses on the synthesis of magnetic nanoparticles augmented microcapsule with dual functionalities namely adsorptive and catalytic activities using membrane material, PolyvinylideneFlouride (PVDF). Feasibility study using Thioflavin dye as the representable model system for degradation will be explored

RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions

Depth estimation from monocular images is pivotal for real-world visual perception systems. While current learning-based depth estimation models train and test on meticulously curated data, they often overlook out-of-distribution (OoD) situations. Yet, in practical settings -- especially safety-critical ones like autonomous driving -- common corruptions can arise. Addressing this oversight, we introduce a comprehensive robustness test suite, RoboDepth, encompassing 18 corruptions spanning three categories: i) weather and lighting conditions; ii) sensor failures and movement; and iii) data processing anomalies. We subsequently benchmark 42 depth estimation models across indoor and outdoor scenes to assess their resilience to these corruptions. Our findings underscore that, in the absence of a dedicated robustness evaluation framework, many leading depth estimation models may be susceptible to typical corruptions. We delve into design considerations for crafting more robust depth estimation models, touching upon pre-training, augmentation, modality, model capacity, and learning paradigms. We anticipate our benchmark will establish a foundational platform for advancing robust OoD depth estimation.Comment: NeurIPS 2023; 45 pages, 25 figures, 13 tables; Code at https://github.com/ldkong1205/RoboDept

Automated Quantification of Traffic Particulate Emissions via an Image Analysis Pipeline

Traffic emissions are known to contribute significantly to air pollution around the world, especially in heavily urbanized cities such as Singapore. It has been previously shown that the particulate pollution along major roadways exhibit strong correlation with increased traffic during peak hours, and that reductions in traffic emissions can lead to better health outcomes. However, in many instances, obtaining proper counts of vehicular traffic remains manual and extremely laborious. This then restricts one's ability to carry out longitudinal monitoring for extended periods, for example, when trying to understand the efficacy of intervention measures such as new traffic regulations (e.g. car-pooling) or for computational modelling. Hence, in this study, we propose and implement an integrated machine learning pipeline that utilizes traffic images to obtain vehicular counts that can be easily integrated with other measurements to facilitate various studies. We verify the utility and accuracy of this pipeline on an open-source dataset of traffic images obtained for a location in Singapore and compare the obtained vehicular counts with collocated particulate measurement data obtained over a 2-week period in 2022. The roadside particulate emission is observed to correlate well with obtained vehicular counts with a correlation coefficient of 0.93, indicating that this method can indeed serve as a quick and effective correlate of particulate emissions

Observing Nearby Nuclei on Paramagnetic Trityls and MOFs via DNP and Electron Decoupling

Dynamic nuclear polarization (DNP) is an NMR sensitivity enhancement technique that mediates polarization transfer from unpaired electrons to NMR-active nuclei. Despite its success in elucidating important structural information on biological and inorganic materials, the detailed polarization-transfer pathway-from the electrons to the nearby and then the bulk solvent nuclei, and finally to the molecules of interest-remains unclear. In particular, the nuclei in the paramagnetic polarizing agent play significant roles in relaying the enhanced NMR polarizations to more remote nuclei. Despite their importance, the direct NMR observation of these nuclei is challenging because of poor sensitivity. Here, we show that a combined DNP and electron decoupling approach can facilitate direct NMR detection of these nuclei. We achieved an ~80 % improvement in NMR intensity via electron decoupling at 0.35 T and 80 K on trityl radicals. Moreover, we recorded a DNP enhancement factor of $\epsilon$ ~ 90 and ~11 % higher NMR intensity using electron decoupling on a paramagnetic metal-organic framework, magnesium hexaoxytriphenylene (MgHOTP MOF)

Storage Performance Evaluation for IoT Gateway Implementation Using Raspberry Pi 2

IoT gateway is a core module exists in many of the IoT architectures that plays a role to connect WSNs to the internet, or specifically to the Cloud. However, conventional internet gateway is not sufficient to be IoT gateway. One of the most critical issue faced by IoT gateway is unstable internet connection especially when using cellular network. This work proposes that IoT gateway should have temporary storage to mask network issue. The objective of this work is to find out the most efficient solution for IoT gateway with temporary storage based on the elements of hardware, scheduler and storage method including database versus flat file on Raspberry Pi and NAND flash. From our experimental results, we found that the most efficient solution for temporary storage in IoT Gateway is using 4-threaded flat file I/O with Deadline scheduler

Healthcare Worker Seroconversion in SARS Outbreak

Serum samples were obtained from healthcare workers 5 weeks after exposure to an outbreak of severe acute respiratory syndrome (SARS). A sensitive dot blot enzyme-linked immunosorbent assay, complemented by a specific neutralization test, shows that only persons in whom probable SARS was diagnosed had specific antibodies and suggests that subclinical SARS is not an important feature of the disease

Three-spin solid effect and the spin diffusion barrier in amorphous solids

Dynamic nuclear polarization (DNP) has evolved as the method of choice to enhance NMR signal intensities and to address a variety of otherwise inaccessible chemical, biological and physical questions. Despite its success, there is no detailed understanding of how the large electron polarization is transferred to the surrounding nuclei or where these nuclei are located relative to the polarizing agent. To address these questions we perform an analysis of the three-spin solid effect, and show that it is exquisitely sensitive to the electron-nuclear distances. We exploit this feature and determine that the size of the spin diffusion barrier surrounding the trityl radical in a glassy glycerol–water matrix is <6 Å, and that the protons involved in the initial transfer step are on the trityl molecule. 1H ENDOR experiments indicate that polarization is then transferred in a second step to glycerol molecules in intimate contact with the trityl

Comparison between gradients and parcellations for functional connectivity prediction of behavior

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average “hard” parcellations (Schaefer et al., 2018), individual-specific “hard” parcellations (Kong et al., 2021a), and an individual-specific “soft” parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average “hard” parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison

NRF2-driven miR-125B1 and miR-29B1 transcriptional regulation controls a novel anti-apoptotic miRNA regulatory network for AML survival

Transcription factor NRF2 is an important regulator of oxidative stress. It is involved in cancer progression, and has abnormal constitutive expression in acute myeloid leukaemia (AML). Posttranscriptional regulation by microRNAs (miRNAs) can affect the malignant phenotype of AML cells. In this study, we identified and characterised NRF2-regulated miRNAs in AML. An miRNA array identified miRNA expression level changes in response to NRF2 knockdown in AML cells. Further analysis of miRNAs concomitantly regulated by knockdown of the NRF2 inhibitor KEAP1 revealed the major candidate NRF2-mediated miRNAs in AML. We identified miR-125B to be upregulated and miR-29B to be downregulated by NRF2 in AML. Subsequent bioinformatic analysis identified putative NRF2 binding sites upstream of the miR-125B1 coding region and downstream of the mir-29B1 coding region. Chromatin immunoprecipitation analyses showed that NRF2 binds to these antioxidant response elements (AREs) located in the 5′ untranslated regions of miR-125B and miR-29B. Finally, primary AML samples transfected with anti-miR-125B antagomiR or miR-29B mimic showed increased cell death responsiveness either alone or co-treated with standard AML chemotherapy. In summary, we find that NRF2 regulation of miR-125B and miR-29B acts to promote leukaemic cell survival, and their manipulation enhances AML responsiveness towards cytotoxic chemotherapeutics