Arc fault detection using artificial intelligence: Challenges and benefits

This systematic review aims to investigate recent developments in the area of arc fault detection. The rising demand for electricity and concomitant expansion of energy systems has resulted in a heightened risk of arc faults and the likelihood of related fires, presenting a matter of considerable concern. To address this challenge, this review focuses on the role of artificial intelligence (AI) in arc fault detection, with the objective of illuminating its advantages and identifying current limitations. Through a meticulous literature selection process, a total of 63 articles were included in the final analysis. The findings of this review suggest that AI plays a significant role in enhancing the accuracy and speed of detection and allowing for customization to specific types of faults in arc fault detection. Simultaneously, three major challenges were also identified, including missed and false detections, the restricted application of neural networks and the paucity of relevant data. In conclusion, AI has exhibited tremendous potential for transforming the field of arc fault detection and holds substantial promise for enhancing electrical safety

Medication Non-adherence and Condomless Anal Intercourse Increased Substantially During the COVID-19 Pandemic Among MSM PrEP Users: A Retrospective Cohort Study in Four Chinese Metropolises

BackgroundThe coronavirus disease (COVID-19) pandemic has impacted HIV prevention strategies globally. However, changes in pre-exposure prophylaxis (PrEP) adherence and HIV-related behaviors, and their associations with medication adherence among men who have sex with men (MSM) PrEP users remain unclear since the onset of the COVID-19 pandemic.MethodsA Retrospective Cohort Study of HIV-negative MSM PrEP users was conducted in four Chinese metropolises from December 2018 to March 2020, assessing the changes in PrEP adherence and HIV-related behaviors before and during the COVID-19. The primary outcome was poor PrEP adherence determined from self-reported missing at least one PrEP dose in the previous month. We used multivariable logistic regression to determine factors correlated with poor adherence during COVID-19.ResultsWe enrolled 791 eligible participants (418 [52.8%] in daily PrEP and 373 [47.2%] in event-driven PrEP). Compared with the data conducted before the COVID-19, the proportion of PrEP users decreased from 97.9 to 64.3%, and the proportion of poor PrEP adherence increased from 23.6 to 50.1% during the COVID-19 [odds ratio (OR) 3.24, 95% confidence interval (CI) 2.62–4.02]. While the percentage of condomless anal intercourse (CAI) with regular partners (11.8 vs. 25.7%) and with casual partners (4.4 vs. 9.0%) both significantly increased. The proportion of those who were tested for HIV decreased from 50.1 to 25.9%. Factors correlated with poor PrEP adherence during the COVID-19 included not being tested for HIV (adjusted odds ratio [aOR] = 1.38 [95% CI: 1.00, 1.91]), using condoms consistently with regular partners (vs. never, aOR = 2.19 [95% CI: 1.16, 4.13]), and being married or cohabitating with a woman (vs. not married, aOR = 3.08 [95% CI: 1.60, 5.95]).ConclusionsIncreased poor PrEP adherence and CAI along with the decrease in HIV testing can lead to an increase in HIV acquisition and drug resistance to PrEP. Targeted interventions are needed to improve PrEP adherence and HIV prevention strategies

Internet-Based HIV Self-Testing Among Men Who Have Sex With Men Through Pre-exposure Prophylaxis: 3-Month Prospective Cohort Analysis From China.

BACKGROUND: Routine HIV testing accompanied with pre-exposure prophylaxis (PrEP) requires innovative support in a real-world setting. OBJECTIVE: This study aimed to determine the usage of HIV self-testing (HIVST) kits and their secondary distribution to partners among men who have sex with men (MSM) in China, who use PrEP, in an observational study between 2018 and 2019. METHODS: In 4 major cities in China, we prospectively followed-up MSM from the China Real-world oral PrEP demonstration study, which provides daily or on-demand PrEP for 12 months, to assess the usage and secondary distribution of HIVST on quarterly follow-ups. Half of the PrEP users were randomized to receive 2 HIVSTs per month in addition to quarterly facility-based HIV testing. We evaluated the feasibility of providing HIVST to PrEP users. RESULTS: We recruited 939 MSM and randomized 471 to receive HIVST, among whom 235 (49.9%) were daily and 236 (50.1%) were on-demand PrEP users. At baseline, the median age was 29 years, 390 (82.0%) men had at least college-level education, and 119 (25.3%) had never undergone facility-based HIV testing before. Three months after PrEP initiation, 341 (74.5%) men had used the HIVST provided to them and found it very easy to use. Among them, 180 of 341 (52.8%) men had distributed the HIVST kits it to other MSM, and 132 (51.6%) among the 256 men who returned HIVST results reported that used it with their sexual partners at the onset of intercourse. Participants on daily PrEP were more likely to use HIVST (adjusted hazard ratio=1.3, 95% CI 1.0-1.6) and distribute HIVST kits (adjusted hazard ratio=1.3, 95% CI 1.1-1.7) than those using on-demand PrEP. CONCLUSIONS: MSM who used PrEP had a high rate of usage and secondary distribution of HIVST kits, especially among those on daily PrEP, which suggested high feasibility and necessity for HIVST after PrEP initiation. Assuming that fourth-generation HIVST kits are available, HIVST may be able to replace facility-based HIV testing to a certain extent. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1800020374; https://www.chictr.org.cn/showprojen.aspx?proj=32481. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2019-036231

Recognition of Human Activities Using Continuous Autoencoders with Wearable Sensors

This paper provides an approach for recognizing human activities with wearable sensors. The continuous autoencoder (CAE) as a novel stochastic neural network model is proposed which improves the ability of model continuous data. CAE adds Gaussian random units into the improved sigmoid activation function to extract the features of nonlinear data. In order to shorten the training time, we propose a new fast stochastic gradient descent (FSGD) algorithm to update the gradients of CAE. The reconstruction of a swiss-roll dataset experiment demonstrates that the CAE can fit continuous data better than the basic autoencoder, and the training time can be reduced by an FSGD algorithm. In the experiment of human activities’ recognition, time and frequency domain feature extract (TFFE) method is raised to extract features from the original sensors’ data. Then, the principal component analysis (PCA) method is applied to feature reduction. It can be noticed that the dimension of each data segment is reduced from 5625 to 42. The feature vectors extracted from original signals are used for the input of deep belief network (DBN), which is composed of multiple CAEs. The training results show that the correct differentiation rate of 99.3% has been achieved. Some contrast experiments like different sensors combinations, sensor units at different positions, and training time with different epochs are designed to validate our approach

Three-dimensional convolutional restricted Boltzmann machine for human behavior recognition from RGB-D video

Abstract This paper provides a novel approach for recognizing human behavior from RGB-D video data. The three-dimensional convolutional restricted Boltzmann machine (3DCRBM) is proposed which can extract features from the raw RGB-D data. In a physical model, the 3DCRBM differs from the restricted Boltzmann machine (RBM) as its weights are shared among all locations in the input and preserving spatial locality. Adjacent frames of the RGB image and the corresponding adjacent frames of the depth image are set as the input of 3DCRBM. Then, multiple 3D convolutional kernels can be applied to these four frames to extract spatio-temporal features. In the experiment of human behavior recognition, the deep belief network (DBN) is established by a layer of 3DCRBM network, convolutional neural network (CNN), and back propagation (BP) network. 3DCRBM is adapted for unsupervised training and getting a feature, while CNN and BP are used for supervised training and classifying the human behavior. The experiment results demonstrate that the correct differentiation rate of 95.7% is achieved, so the effectiveness of our approach could be validated

Recognition of Human Activities Using Continuous Autoencoders with Wearable Sensors

This paper provides an approach for recognizing human activities with wearable sensors. The continuous autoencoder (CAE) as a novel stochastic neural network model is proposed which improves the ability of model continuous data. CAE adds Gaussian random units into the improved sigmoid activation function to extract the features of nonlinear data. In order to shorten the training time, we propose a new fast stochastic gradient descent (FSGD) algorithm to update the gradients of CAE. The reconstruction of a swiss-roll dataset experiment demonstrates that the CAE can fit continuous data better than the basic autoencoder, and the training time can be reduced by an FSGD algorithm. In the experiment of human activities’ recognition, time and frequency domain feature extract (TFFE) method is raised to extract features from the original sensors’ data. Then, the principal component analysis (PCA) method is applied to feature reduction. It can be noticed that the dimension of each data segment is reduced from 5625 to 42. The feature vectors extracted from original signals are used for the input of deep belief network (DBN), which is composed of multiple CAEs. The training results show that the correct differentiation rate of 99.3% has been achieved. Some contrast experiments like different sensors combinations, sensor units at different positions, and training time with different epochs are designed to validate our approach

Research on Value Evaluation of Traditional Door and Window Decoration Heritage in Jiangnan Area of China

As a heritage, traditional door and window decoration in Jiangnan area of China has the value of cultural relics. A group of people created them according to the political, economic, cultural, and artistic background at that time, which can reflect the social features, technical level, humanities, and arts at that time. Therefore, traditional door and window decoration in Jiangnan area is a shred of crucial historical evidence to study the society, economy, culture, and craft at that time. Through the evaluation of traditional door and window decoration heritage, the researcher can understand the traditional decorative art of doors and windows in Jiangnan area. The assessment based on the index system forms a quantitative result, and the quantitative data can more intuitively reflect the heritage value. The researcher can give feedback on the evaluation results to the relevant personnel, find the deficiencies and missing parts in the protection and inheritance, and provide suggestions for the follow-up protection and inheritance plan. The protection and inheritance plan can be more effective than traditional doors and windows in Jiangnan area

Promiscuous Catalytic Activity of a Binuclear Metallohydrolase : Peptide and Phosphoester Hydrolyses

In this study, chemical promiscuity of a binuclear metallohydrolase aminopeptidase ( AP) has been investigated using DFT calculations. AP catalyzes two diverse reactions, peptide and phosphoester hydrolyses, using its binuclear (Zn-Zn) core. On the basis of the experimental information, mechanisms of these reactions have been investigated utilizing leucine -nitro aniline (Leu- NA) and bis(4-nitrophenyl) phosphate (BNPP) as the substrates. The computed barriers of 16.5 and 16.8 kcal/mol for the most plausible mechanisms proposed by the DFT calculations are in good agreement with the measured values of 13.9 and 18.3 kcal/mol for the Leu- NA and BNPP hydrolyses, respectively. The former was found to occur through the transfer of two protons, while the latter with only one proton transfer. They are in line with the experimental observations. The cleavage of the peptide bond was the rate-determining process for the Leu- NA hydrolysis. However, the creation of the nucleophile and its attack on the electrophile phosphorus atom was the rate-determining step for the BNPP hydrolysis. These calculations showed that the chemical nature of the substrate and its binding mode influence the nucleophilicity of the metal bound hydroxyl nucleophile. Additionally, the nucleophilicity was found to be critical for the Leu- NA hydrolysis, whereas double Lewis acid activation was needed for the BNPP hydrolysis. That could be one of the reasons why peptide hydrolysis can be catalyzed by both mononuclear and binuclear metal cofactors containing hydrolases, while phosphoester hydrolysis is almost exclusively by binuclear metallohydrolases. These results will be helpful in the development of versatile catalysts for chemically distinct hydrolytic reactions