Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnostic of Combined Mechanical and Electrical Faults in ASD-Powered Induction Motor Using MODWT and a Lightweight 1-D CNN

The early fault detection in the rotary electrical machines,such as induction motors (IMs), has been growing in modern industry. IMs have been widely used in industrial applications due to its easy installation, reliability, and low cost. However, the increasing usage of IMs also increases the need for timely maintenance in order to ensure their operation and a longer service life. This article proposes a new diagnosis methodology based on maximal overlap discrete wavelet transform and a lightweight 1-D convolutional neural network (CNN) architecture, in order to detect mechanical and electrical faults and their combination, in adjustable speed drive (ASD)-powered IMs. Specifically, single and combined faults were studied from the next: Outer raceway bearing (mechanical), turn-to-turn short-circuit, and phase-to-ground short circuit (electrical). The presented study was developed using current signals acquired from stators of IMs of 1 hp. The current signals are measured at powered conditions introduced by a power grid with a constant frequency at 60 Hz, and an ASD at three different frequencies. The proposed diagnostic methodology reaches more than 99% of accuracy.
A lightweight verifiable secret sharing scheme in IoTs

Verifiable secret sharing (VSS) is a fundamental tool of cryptography and distributed computing in Internet of things (IoTs). Since network bandwidth is a scarce resource, minimizing the number of verification data will improve the performance of VSS. Existing VSS schemes, however, face limitations in meeting the number of verification data and energy consumptions for low-end devices, which make their adoption challenging in resource-limited IoTs. To address above limitations, we propose a VSS scheme according to Nyberg’s one-way accumulator for one-way hash functions (NAHFs). The proposed scheme has two distinguished features: first, the security of the scheme is based on NAHFs whose computational requirements are the basic criteria for known IoT devices and, second, upon receiving only one verification data, participants can verify the correctness of both their shares and the secret without any communication. Experimental results demonstrate that, compared to the Feldman scheme and Rajabi-Eslami scheme, the energy consumption of a participant in the proposed scheme is respectively reduced by at least 24% and 83% for a secret.
Review of Problems of Registration of ECG Signals in Model Animals in Pharmacological Studies

One of the stages of pharmacological research is a study using model animals, the most common species are mice, rats, rabbits, and other mammals. However, in recent years, it has been gaining popularity to conduct experiments on species of a lower organization, for example, the zebrafish (Danio rerio). The development of ECG recording devices for model animals is associated with specific methodological difficulties in conducting research; this article considers several proposals for improving animals' ECG recording equipment.
Efficient Energy Consumption Techniques for Cloud and IoT Systems

Internet of things (IoT) is a fundamental segment of everyone's daily life, thanks to rapid advancements in wireless technology and the Internet. Services based on localization are of the most interesting IoT application. They are, capable of collecting and transmitting data in an order where they detect the position of the targets, due to the deployment of sensor networks. Localization is related to the target in device-based approaches, and localization is determined through collaboration alongside other wireless devices that have been deployed. We also show how location is important in modern IoT applications like smart cities, smart transport system, and potency. In spite of its possibilities, the web of the things experiences the ill effects of restricted storerooms and look for the administrations that are in cloud which helps for the capacity arrangements for the information which are detected. However, cloud is worked for the colossal assets of capacity, the setting of the detected information into the cloud would be energy devouring and inclined to the privacy dangers resulting in unlawful approach. Under these circumstances, the summary is given which is, primary IoT localization resistance that have recently been highlighted in the literature. Finally, in this paper, we suggest communication technologies that can be used for efficient energy consumption and some energy efficiency techniques through which energy consumption can be reduced.
Detection and visualization of COVID-19 in chest X-ray images using CNN and Grad-CAM (GCCN)

The quick and early detection of COVID-19 infection is very important in the fight against the pandemic. Deep learning can be considered as a helpful method to provide help and assist the medical staff to detect the infection of COVID-19, which will definitely have a positive effect in controlling the outbreak of COVID-19. In this paper, we will propose a simple CNN based deep learning model called Grad-CAM CNN (GCNN), for the purpose of detecting the infection of COVID-19 disease from chest X-ray images and visualizing a heat map with the help of the Grad-CAM technique in order to determine which area in the X-ray image of the chest has COVID-19. Since CNN is a very powerful method in processing images, we use it to build the model. We evaluated the performance of the proposed method on public online available datasets of X-ray images of the chest from Kaggle. The proposed method is able to achieve an accuracy score of 97.78 % using a learning rate of 0.003 with the Adam optimizer. In the light of the promising results obtained from this method, it is possible to say that the proposed method can be helpful in the early diagnosis in the upcoming waves of COVID-19.
An End-to-end Flow Control Method Based on DQN

We propose an end-to-end flow control method based on DQN (Deep-Q-Network). The flow control method is intelligent and flow-based, it is suitable for those DCNs (Data Center Network) based on SDN (Software Define Network) architecture. The system architecture is analyzed and an improved DQN algorithm is proposed. The training results show that the method has good convergence speed and convergence effect. The final test shows that the DQN-based end-to-end flow control method can effectively achieve DCN flow control by allocating the flow rates globally, and can improve the link utilization rate of the entire network as much as possible.
Blind accumulators for e-voting

We present a novel cryptographic primitive, blind accumulator, aimed at constructing e-voting systems. Blind accumulators collect private keys of eligible voters in a decentralized manner not getting information about the keys. Once the accumulation is complete, a voter processes the resulting accumulator deriving a public key that refers to the private key previously added by this voter. Public keys are derived deterministically and can therefore stand as ﬁxed voter pseudonyms. The voter can prove that the derived key refers to some accumulated private key without revealing neither that key nor the voter itself. The voter uses the accumulated private key to sign a ballot. The corresponding public key is used to verify the signature. Since the public key is ﬁxed, it is easy to achieve veriﬁability, to protect against multiple submissions of ballots by the same voter or, conversely, to allow multiple submissions but count only the last one. We suggest a syntax of blind accumulators and security requirements for them. We embed blind ac-cumulators in the Pseudonymous Key Generation (PKG) protocol which details the use of accumulators in practical settings close to e-voting. We propose an implementation of the blind accumulator scheme whose main computations resemble the Diﬃe–Hellman protocol. We justify the security of the proposed implementation.
A novel based on parameter optimization for enhancing images

Image enhancement is a very signiﬁcant topic in medical image processing. This paper introduces a new algorithm about enhancing images that is based on parameter optimization. This algorithm is called the particle swarm optimization (PSO) based on Image Enhancement (PSOIE). First, from input image, the algorithm creates 3 intermediate images using basic transformations: histogram equalization, candy edge detection and median ﬁltering. Next, we use the PSO optimization algorithm to ﬁnd the optimal coeﬃcients for each image based on the contrast index function. The output image combines three intermediate images with optimized coeﬃcients with the following advantages: enhanced brightness, edge enhancement, image sharpening and noise reduction. The experimental results of the paper show that the proposed algorithm outperforms than some recent methods.
A Bug Report Analysis and Search Tool and Its Validation

According to recent work, duplicate bug report entries in bug trackers impact negatively on software maintenance and evolution productivity due to, among other factors, the increased time spent on report analysis. Such type of duplication is characterized by the submission of two or more bug reports that describe the same software issue. In this sense, this dissertation investigates and characterizes the problem of bug report duplication and proposes a solution to it.
Association of time-temperature curves with outcomes in temperature management for cardiac arrest

Background/Purpose Cardiac arrest is a common cause of death and neurological injury; therapeutic cooling for neuroprotection is standard of care. Despite numerous and ongoing trials targeting a specified cooling temperature for a target duration, the concept of temperature dose—the duration spent at a given depth of hypothermia—is not as well explored. Methods In this retrospective study, we examined 66 patients 18 years of age or older undergoing therapeutic hypothermia for cardiac arrest between 2007 and 2010 to assess the relationship of temperature dose with outcomes. Demographic, clinical, outcome and temperature data were collected. Demographic and clinical data underwent bivariate regression analysis for association with outcome. Time-temperature curves were divided into pre-determined temperature thresholds and assessed by logistic regression analysis for association with outcome. A second, multivariate regression analysis was performed controlling for factors associated with poor outcomes. Results Old age was significantly associated with poor outcome and a shockable arrest rhythm was significantly associated with positive outcome. Subjects spent an average of 2.82 hours below 35°C, 7.31 hours ≥35°C to ≤36.5°C, 24.75 hours >36.5 to <38.0°C and 7.06 hours ≥38°C. Logistic regression analysis revealed borderline significant positive association between good outcome and time at a cooling depth (35°C–36.5°C, p=0.05); adjusted for old age, the association became significant (p=0.04). Conclusion Controlling for old age, longer durations between >35°C, ≤36.5°C during therapeutic hypothermia for cardiac arrest were significantly associated with good clinical outcomes. Time spent within a given temperature range may be useful for measuring the effect of temperature management.
A comparative study of ultrasound assisted versus landmark technique for combined spinal-epidural anaesthesia in patients undergoing lower limb orthopaedic surgery

Background and Aims: Spinal anatomy is better visualised in the para sagittal oblique view with the aid of ultrasonography. The present study was undertaken to investigate whether preprocedural ultrasonography can facilitate the ease of establishing combined spinal epidural (CSE) via paramedian approach versus landmark approach in patients undergoing lower limb orthopaedic surgery. Methods: This prospective randomised study was conducted in 100 American Society of Anesthesiologists (ASA) grade I-II patients, aged 18-60 years requiring CSE and randomly divided into two groups: Ultrasound-assisted (USG) group (n = 50) and Surface landmark (SLG) group (n = 50). The primary outcome was to compare the first pass needle success rate to establish CSE and the secondary outcomes were to compare the number of needle puncture attempts, time to establish landmarks (t1), time to accomplish CSE (t2) and complications. Results: First pass needle success rate in USG group was 43 (86.0%) versus 36 (60.0%) in SLG group (P = 0.001). Number of attempts taken to establish CSE was lower in USG group as compared to SLG group (P = 0.023). t1 was greater in USG group (1.45 ± 0.47) minutes as compared to (0.79 ± 0.34) minutes in SLG group (P = 0.003). t2 was reduced in USG group (1.47 ± 0.55) minutes versus (2.73 ± 1.36) minutes in SLG group (P = 0.005). Conclusion: Preprocedural USG for CSE via paramedian approach increases first pass needle success rate and reduces needle puncture attempts in patients undergoing lower limb orthopaedic surgery.
Efficacy of Exercise for Fall Prevention in Older Adults

Apply It! • The exercise professional will be given evidenced-based effective exercises to be used for fall mitigation and prevention purposes with older adults. • The exercise professional will learn to look for common risk factors for falling in an assessment and how to address these in an exercise program. • The exercise professional will be provided with strategies for successful work with a client who experiences fear of falling. Summary Statement This article synthesizes the existing research on exercise geared toward fall prevention for older adults and uses it to outline an exercise program template to be used by exercise professionals working with this population.
Pothole Detection Using Deep Learning: A Real-Time and AI-on-the-Edge Perspective

Asphalt pavement distresses are the major concern of underdeveloped and developed nations for the smooth running of daily life commute. Among various pavement failures, numerous research can be found on pothole detection as they are injurious to automobiles and passengers that may turn into an accident. This work is intended to explore the potential of deep learning models and deploy three superlative deep learning models on edge devices for pothole detection. In this work, we have exploited the AI kit (OAK-D) on a single-board computer (Raspberry Pi) as an edge platform for pothole detection. Detailed real-time performance comparison of state-of-the-art deep learning models and object detection frameworks (YOLOv1, YOLOv2, YOLOv3, YOLOv4, Tiny-YOLOv4, YOLOv5, and SSD-mobilenetv2) for pothole detection is presented. The experimentation is performed on an image dataset with pothole in diverse road conditions and illumination variations as well as on real-time video captured through a moving vehicle. The Tiny-YOLOv4, YOLOv4, and YOLOv5 evince the highest mean average precision (mAP) of 80.04%, 85.48%, and 95%, respectively, on the image set, thus proving the strength of the proposed approach for pothole detection and deployed on OAK-D for real-time detection. The study corroborated Tiny-YOLOv4 as the befitted model for real-time pothole detection with 90% detection accuracy and 31.76 FPS.
Evolutionary Ecology of Fixed Alternative Male Mating Strategies in the Ruff (Calidris pugnax)

A few empirical examples document fixed alternative male mating strategies in animals. Here we focus on the polymorphism of male mating strategies in the ruff (Calidris pugnax, Aves Charadriiformes). In ruffs, three fixed alternative male mating strategies coexist and are signaled by extreme plumage polymorphism. We first present relevant data on the biology of the species. Then we review the available knowledge of the behavioral ecology of ruffs during the breeding season, and we detail the characteristics of each of the three known fixed male mating strategies. We next turn to the results of exceptional quality accumulated on both the structural and functional genomics of the ruff over the past few years. We show how much these genomic data can shed new, mechanistic light on the evolution and maintenance of the three fixed alternative male mating strategies. We then look if there is sufficient indication to support frequency-dependent selection as a key mechanism in maintaining these three strategies. Specifically, we search for evidence of equal fitness among individuals using each of the three strategies. Finally, we propose three lines of research avenues that will help to understand the eco-evolutionary dynamics of phenotypic differences within natural populations of this iconic model species.
Defining and Developing Translation Competence for Didactic Purposes: Some Insights from Product-Oriented Research

This chapter provides an outline of the main issues concerning the conceptualisation and modelling of Translation Competence (TC) and proposes the adoption of a product-based definition for didactic purposes. Such definition is based on an empirical longitudinal product-oriented study on TC aiming to identify possible textual features and conventions that can be related to the translator’s level of competence. The preliminary results from this longitudinal study presented in this chapter appear to suggest the existence of a possible relation between specific textual and procedural patterns and the participants’ translation experience. Such patterns could provide translator trainers and trainees with a set of pragmatic indications for the definition and achievement of specific learning goals and could potentially serve as predictive developmental hypotheses in translator training.
A Robust Early Warning System for Preventing Flash Floods in Mountainous Area in Vietnam

The early-warning model for flash floods is based on a hydrological and geomorphological concept connected to the river basin, with the principle that flash floods will only occur where there is a high potential risk and when rainfall exceeds the threshold. In the model used to build flash-floods risk maps, the parameters of the basin are analyzed and evaluated and the weight is determined using Thomas Saaty’s analytic hierarchy process (AHP). The flash-floods early-warning software is built using open source programming tools. With the spatial module and online processing, a predicted precipitation of one to six days in advance for iMETOS (AgriMedia—Vietnam) automatic meteorological stations is interpolated and then processed with the potential risk maps (iMETOS is a weather-environment monitoring system comprising a wide range of equipment and an online platform and can be used in various fields such as agriculture, tourism and services). The results determine the locations of flash floods at several risk levels corresponding to the predicted rainfall values at the meteorological stations. The system was constructed and applied to flash floods disaster early warning for Thuan Chau in Son La province when the rainfall exceeded the 150 mm/d threshold. The system initially supported positive decision-making to prevent and minimize damage caused by flash floods.
A Personalized ð α , β , l , k Þ -Anonymity Model of Social Network for Protecting Privacy

By mining the data published on social network, we can discover the hidden value of information including the privacy of individuals and organizations. Protecting privacy of individuals and organizations on social network has become the focus of more and more researchers. Based on the actual privacy protection need of edge sensitive attribute and vertexes sensitive attribute, we propose a new personalized ð α , β , l , k Þ -anonymity technology of privacy preserving to reduce distortion extent of the data in the privacy processing of data of social network. Experimental results of personalized ð α , β , l , k Þ -anonymity algorithm show that d -neighborhood attack of graph, background knowledge attack, and homogeneity attack can be prevented e ﬀ ectively by using anonymous vertexes and edges, as well as the in ﬂ uence matrix based on background knowledge. The diversity of vertex sensitive attribute can be achieved. Personalized protecting privacy requirements can be met by using such parameter as α , β , l , k .
Factors Influencing Consumers’ Purchase Intention on Cold Chain Aquatic Products under COVID-19: An Investigation in China

Following the detection of COVID-19 in cold chain aquatic products (CCAP) at Xinfadi Produce Wholesale Market in Beijing, China, in June 2020, novel coronavirus positive tests of CCAP have been reported in such markets of Dalian, Xi’an, Qingdao, Taiyuan, and other places, which has aroused the concern of Chinese consumers. The CCAP outbreak puts tremendous pressure on public health management and threatens customer well-being. This article uses the theoretical model of planned behavior (TPB) to investigate Chinese consumers’ purchasing intentions of CCAP under this circumstance. A total of 783 questionnaires were administered in China with empirical analysis through a structural equation model. The results show that attitudes (ATT) towards the safety of CCAP and subjective norms (SN) have significant positive effects on customers’ purchasing behavior intention (BI); the emotional response to the health concern (EM) of CCAP has a significant positive impact on SN, ATT, and BI; and BI of CCAP is significantly affected by individual characteristics. The BI of CCAP for those married consumers living in cities and towns with a low monthly consumption frequency is more likely to be affected by the new coronavirus epidemic events. This paper is one of the first studies that contribute to the literature by exploring the influencing factors of the consumption behavior intention over the COVID-19 pandemic in China from a public health perspective. The findings provide significant implications for cold chain operators, market managers, and policymakers to develop guidelines and offer a framework to initiate and support the produce market and boost consumer health confidence in CCAP at the practitioner level.