Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System

Innovative enhancement in embedded system platforms, specifically hardware accelerations, significantly influence the application of deep learning in real-world scenarios. These innovations translate human labor efforts into automated intelligent systems employed in various areas such as autonomous driving, robotics, Internet-of-Things (IoT), and numerous other impactful applications. NVIDIA's Jetson platform is one of the pioneers in offering optimal performance regarding energy efficiency and throughput in the execution of deep learning algorithms. Previously, most benchmarking analysis was based on 2D images with a single deep learning model for each comparison result. In this paper, we implement an end-to-end video-based crime-scene anomaly detection system inputting from surveillance videos and the system is deployed and completely operates on multiple Jetson edge devices (Nano, AGX Xavier, Orin Nano). The comparison analysis includes the integration of Torch-TensorRT as a software developer kit from NVIDIA for the model performance optimisation. The system is built based on the PySlowfast open-source project from Facebook as the coding template. The end-to-end system process comprises the videos from camera, data preprocessing pipeline, feature extractor and the anomaly detection. We provide the experience of an AI-based system deployment on various Jetson Edge devices with Docker technology. Regarding anomaly detectors, a weakly supervised video-based deep learning model called Robust Temporal Feature Magnitude Learning (RTFM) is applied in the system. The approach system reaches 47.56 frames per second (FPS) inference speed on a Jetson edge device with only 3.11 GB RAM usage total. We also discover the promising Jetson device that the AI system achieves 15% better performance than the previous version of Jetson devices while consuming 50% less energy power.Comment: 18 pages, 7 figures, 5 table

An update and reassessment of vascular plant species richness and distribution in Bach Ma National Park, Central Vietnam

Bach Ma National Park (BMNP) is recognized as an essential biodiversity hotspot in Vietnam because of its diverse topography, high species richness and threatened and endemic species. This study updates the richness and distribution of vascular plant species in the BMNP by intergrading data from literature, field surveys, key-informant interviews and participatory observations. Our results showed that the park has a high diversity of vascular plants with 1,874 species belonging to 192 families, 6 phylums including Psilotophyta, Lycopodiophyta, Equisetophyta, Polypodiophyta, Pinophyta, and Magnoliophyta. It also indicates that 199 out of 1,874 vascular species in the BMNP are listed as endangered, precious and rare plant species of Vietnam. In particular, 55 species are part of the IUCN 2020 list, in which 9 are critically endangered species (CR), 15 are endangered species (EN), and 31 are vulnerable species (VU). According to the rankings of the Red List Vietnam (2007), 6 species of CR (accounting for 13.64% compared with the whole country), 36 species of EN (20%), and 52 species of VU (26%) were found in this area. The results provided that vascular plant species are distributed into 2 types based on high altitude (threshold at 900m), but there are no dominant communities. The findings may be essential information for foresters and biologists to recognize and use it as the newest update for their next scientific research in conservation and resource management.Vườn Quốc gia (VQG) Bạch Mã được xem là một điểm nóng đa dạng sinh học quan trọng ở Việt Nam vì địa hình đa dạng, độ phong phú loài cao, đặc biệt là các loài đặc hữu và nguy cấp. Trong nghiên cứu này, chúng tôi đã cập nhật sự phong phú và phân bố của các loài thực vật bậc cao tại VQG Bạch Mã bằng cách kết hợp dữ liệu từ tổng quan tài liệu, khảo sát thực địa, phỏng vấn người am hiểu và điều tra có sự tham gia. Kết quả cho thấy VQG có hệ thực vật bậc cao phong phú với 1.874 loài, thuộc 192 họ, 6 ngành bao gồm Psilotophyta, Lycopodiophyta, Equisetophyta, Polypodiophyta, Pinophyta, Magnoliophyta. Kết quả chỉ ra rằng 199 trong số 1.874 loài thực vật bậc cao tại VQG này được xếp vào danh sách các loài nguy cấp của Việt Nam. Đặc biệt, có 55 loài thuộc danh mục của IUCN năm 2020, trong đó có 9 loài Cực kỳ nguy cấp (CR), 15 loài Nguy cấp (EN) và 31 loài Sẽ nguy cấp (VU). Trong khi đó, theo xếp hạng của Sách Đỏ Việt Nam (2007), nghiên cứu cho thấy có 6 loài CR (chiếm 13,64% so với cả nước), 36 loài EN (20%) và 52 loài VU (26%). Phát hiện của chúng tôi cũng chỉ ra rằng đặc điểm phân bố của các loài thực vật bậc cao ở VQG Bạch Mã gồm 2 kiểu rừng dựa trên độ cao (mức 900m), nhưng không có quần xã nào chiếm ưu thế. Các kết quả này được kỳ vọng sẽ là nguồn thông tin cần thiết cho các nhà hoạt động lâm nghiệp và sinh vật học sử dụng nó như một bản cập nhật mới nhất cho các nghiên cứu khoa học tiếp theo trong bảo tồn và quản lý tài nguyên

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

What role renewable energy consumption, renewable electricity, energy use and import play in environmental quality?

Climate change has gained an increasing trend and has become an international issue, and sustainable energy technologies have been considered the best solution for it that needs new researchers’ focus. Hence, the article examines the impact of sustainable energy technologies such as renewable electricity output on climate change (carbon dioxide emissions) in E7 countries. The article also investigates the impact of renewable energy (RE) consumption, energy use, urbanization, energy import, and industrialization on the carbon dioxide (CO2) emissions in E7 countries. The secondary data has been gathered using secondary sources such as World Development Indicators (WDI) from 2001 to 2020. The study has used the Method of Moments Quantile Regression (MMQR) to check the linkage among the variables. The results indicated that the renewable electricity output and RE consumption are negatively associated with CO2 emissions in E7 countries. The results also indicated that energy usage, industrialization, energy import, and urbanization positively correlate with CO2 emissions in E7 countries. This research guides the policymakers in developing the policies related to the adoption of sustainable energy technologies to control climate change

Direct Synthesis of Gold Nanoparticles in Polymer Matrix

We report an original method for directly fabricating gold nanoparticles (Au NPs) in a polymer matrix using a thermal treatment technique and theoretically and experimentally investigate their plasmonic properties. The polymeric-metallic nanocomposite samples were first prepared by simply mixing SU-8 resist and Au salt with different concentrations. The Au NPs growth was triggered inside the polymer through a thermal process on a hot plate and in air environment. The Au NPs creation was confirmed by the color of the nanocomposite thin films and by absorption spectra measurements. The Au NPs sizes and distributions were confirmed by transmission electron microscope measurements. It was found that the concentrations of Au salt and the annealing temperatures and durations are all crucial for tuning the Au NPs sizes and distributions, and, thus, their optical properties. We also propose a simulation model for calculations of Au NPs plasmonic properties inside a polymer medium. We realized that Au NPs having large sizes (50 to 100 nm) play an important role in absorption spectra measurements, as compared to the contribution of small NPs (<20 nm), even if the relative amount of big Au NPs is small. This simple, low-cost, and highly reproducible technique allows us to obtain plasmonic NPs within polymer thin films on a large scale, which can be potentially applied to many fields

Scalable, low-cost, and versatile system design for air pollution and traffic density monitoring and analysis

Vietnam requires a sustainable urbanization, for which city sensing is used in planning and de-cision-making. Large cities need portable, scalable, and inexpensive digital technology for this purpose. End-to-end air quality monitoring companies such as AirVisual and Plume Air have shown their reliability with portable devices outfitted with superior air sensors. They are pricey, yet homeowners use them to get local air data without evaluating the causal effect. Our air quality inspection system is scalable, reasonably priced, and flexible. Minicomputer of the sys-tem remotely monitors PMS7003 and BME280 sensor data through a microcontroller processor. The 5-megapixel camera module enables researchers to infer the causal relationship between traffic intensity and dust concentration. The design enables inexpensive, commercial-grade hardware, with Azure Blob storing air pollution data and surrounding-area imagery and pre-venting the system from physically expanding. In addition, by including an air channel that re-plenishes and distributes temperature, the design improves ventilation and safeguards electrical components. The gadget allows for the analysis of the correlation between traffic and air quali-ty data, which might aid in the establishment of sustainable urban development plans and poli-cies

Synthèse directe de nanoparticules d'or dans un film de polymère

We report an original method for directly fabricating gold nanoparticles (Au NPs) in a polymer matrix using a thermal treatment technique and theoretically and experimentally investigate their plasmonic properties. The polymeric-metallic nanocomposite samples were first prepared by simply mixing SU-8 resist and Au salt with different concentrations. The Au NPs growth was triggered inside the polymer through a thermal process on a hot plate and in air environment. The Au NPs creation was confirmed by the color of the nanocomposite thin films and by absorption spectra measurements. The Au NPs sizes and distributions were confirmed by transmission electron microscope measurements. It was found that the concentrations of Au salt and the annealing temperatures and durations are all crucial for tuning the Au NPs sizes and distributions, and, thus, their optical properties. We also propose a simulation model for calculations of Au NPs plasmonic properties inside a polymer medium. We realized that Au NPs having large sizes (50 to 100 nm) play an important role in absorption spectra measurements, as compared to the contribution of small NPs (<20 nm), even if the relative amount of big Au NPs is small. This simple, low-cost, and highly reproducible technique allows us to obtain plasmonic NPs within polymer thin films on a large scale, which can be potentially applied to many fields

Novel machine learning approach toward classification model of HIV-1 integrase inhibitors

HIV-1 (Human immunodeficiency virus-1) has been causing severe pandemics by attacking the immune system of its host. Left untreated, it can lead to AIDS (acquired immunodeficiency syndrome), where death is inevitable due to opportunistic diseases. Therefore, discovering new antiviral drugs against HIV-1 is crucial. This study aimed to explore a novel machine learning approach to classify compounds that inhibit HIV-1 integrase and screen the dataset of repurposing compounds. The present study had two main stages: selecting the best type of fingerprint or molecular descriptor using the Wilcoxon signed-rank test and building a computational model based on machine learning. In the first stage, we calculated 16 different types of fingerprint or molecular descriptors from the dataset and used each of them as input features for 10 machine-learning models, which were evaluated through cross-validation. Then, a meta-analysis was performed with the Wilcoxon signed-rank test to select the optimal fingerprint or molecular descriptor types. In the second stage, we constructed a model based on the optimal fingerprint or molecular descriptor type. This data followed the machine learning procedure, including data preprocessing, outlier handling, normalization, feature selection, model selection, external validation, and model optimization. In the end, an XGBoost model and RDK7 fingerprint were identified to be the most suitable. The model achieved promising results, with an average precision of 0.928 ± 0.027 and an F1-score of 0.848 ± 0.041 in cross-validation. The model achieved an average precision of 0.921 and an F1-score of 0.889 in external validation. Molecular docking was performed and validated by redocking for docking power and retrospective control for screening power, with the AUC metrics being 0.876 and the threshold being identified at –9.71 kcal/mol. Finally, 44 compounds from DrugBank repurposing data were selected from the QSAR model, then three candidates were identified as potential compounds from molecular docking, and PSI-697 was detected as the most promising molecule, with in vitro experiment being not performed (docking score: -17.14 kcal/mol, HIV integrase inhibitory probability: 69.81%