Miracle-dz/UniFAR-FTUs huggingface dataset:
Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN

Owing to the symmetry of the rolling bearing structure and the rotating operation mode, the rolling bearing works in a complex environment. It is very easy to be submerged by noise and misdiagnosis. For the non-stationary signal in variable speed state, this paper presents a condition monitoring method based on deep belief network (DBN) optimized by multi-order fractional Fourier transform (FRFT) and sparrow search algorithm (SSA). Firstly, the fractional Fourier transform based on curve feature segmentation is used to filter the fault vibration signal and extract the fault feature frequency. Then, the fault features are input into the SSA-DBN model for training, and the bearing fault features are classified, identified, and diagnosed. Finally, the rotating machinery fault simulator in the laboratory of Ottawa University is taken as the practical application object to verify the effectiveness of the method. The experimental results show that the proposed method has higher recognition accuracy and stronger stability.
Six Sigma approach for reducing rejection of in-house cast component

Six Sigma is a continuous improvement cyclic process which goes on until the perfect desired target is achieved. Its main strategy is to make use of the Deming's design measure analyse improve and control (DMAIC) process to achieve the desired goals. Through this study we have tried to shed some light on how Six Sigma methods are helpful in reducing the cost and improving the process output. A cast component (bypass flap housing) was observed to be prone to a high rejection rate owing to defects in casting process. Being the major source of economic losses, the goal of the study was to discern and rectify the cause of rejection so that economic savings can be made. We decreased the rejection of the component from 7.8% to 1.54% using DMAIC process.
A Sim-to-Real Object Recognition and Localization Framework for Industrial Robotic Bin Picking

We present a generic and robust sim-to-real deep-learning-based framework, namely S2R-Pick, for fast and accurate object recognition and localization in industrial robotic bin picking. Unlike existing works designed for general everyday environments, objects for industrial bin picking are often texture-less, metallic, and also suffer from severe occlusion and cluttering. In this work, we first develop an automated synthetic data generation pipeline to produce large-scale photo-realistic data with precise annotations. We then formulate an instance segmentation network for object recognition and a 6D pose estimation network for localization. We cascade and train the two networks only on our generated synthetic data and can directly transfer them for processing real inputs without needing to retrain them on real samples. Extensive experiments show the effectiveness and superiority of our framework over state-of-the-art methods.
Concept of Fluorescent Transport Activity Biosensor for the Characterization of the Arabidopsis NPF1.3 Activity of Nitrate

The NRT1/PTR FAMILY (NPF) in Arabidopsis (Arabidopsis thaliana) plays a major role as a nitrate transporter. The first nitrate transporter activity biosensor NiTrac1 converted the dual-affinity nitrate transceptor NPF6.3 into fluorescence activity sensors. To test whether this approach is transferable to other members of this family, screening for genetically encoded fluorescence transport activity sensor was performed with the member of the NPF family in Arabidopsis. In this study, NPF1.3, an uncharacterized member of NPF in Arabidopsis, was converted into a transporter activity biosensor NiTrac-NPF1.3 that responds specifically to nitrate. The emission ratio change of NiTrac-NPF1.3 triggered by the addition of nitrate reveals the important function of NPF1.3 in nitrate transport in Arabidopsis. A functional analysis of Xenopus laevis oocytes confirmed that NPF1.3 plays a role as a nitrate transporter. This new technology is applicable in plant and medical research.
EXPERIMENTAL AND NUMERICAL STUDIES ON ALUMINUM HONEYCOMB STRUCTURE WITH VARIOUS CELL SPECIFICATIONS UNDER IMPACT LOADING

Through analyzing an Y-shaped cross-section structure of honeycomb cell,the formula for calculating relative density of honeycomb with regular hexagon cell shape was derived.The typical dynamic collapse process of honey- comb was illuminated.Three kinds of aluminum alloy (3003) honeycomb specimens for experiment were attained by cut- ting honeycomb plate using the self-made cutting tool.These specimens' size specifications are 0.06mm(cell foil thickness t)×4mm(cell sides length l)×60mm(specimen height h),0.06mm(t)×6mm(l)×30mm(h) and 0.08mm(t)×10mm(l)×50mm(h) respectively.Impact experiment using a drop-hammer apparatus (Instron dynatup 9250HV),in which the initial mean impact velocity is 5m/s and the drop mass is 14.6687kg,and the corresponding quasi-static test were both performed.Then the stain-stress curves of the three kinds of specimens under dynamic and quasi-static states were recorded.Assuring the same load conditions in experiment and simplifying the honeycomb cell as an Y-shaped cross- section column,numerical simulations for these three kinds of honeycomb's compression collapse processes under impact loading were carried out by using FEM code LS-DYNA.Then the strain-stress curves obtained by experiment and numeri- cal simulation were compared.It is shown that the result of the numerical simulation well coincides with that of the experi- ment.The elastic and plastic collapse stress by theory,experiment and simulation were compared and the result shows that the plastic collapse stress of simulation is fairly well-matched with that of the experiment while the theoretical stress is less than others.At last,an aluminum honeycomb buffer which can be used in the Landing Gear's primary/auxiliary legs was designed.And the test result shows that the designed buffered acceleration is in accordance with the test value.
Explainable Machine Learning for Intrusion Detection via Hardware Performance Counters

The exponential proliferation of Malware over the past decade has threatened system security across a plethora of Internet of Things (IoT) devices. Furthermore, the improvements in computer architectures to include speculative branching and out-of-order executions have engendered new opportunities for adversaries to carry out microarchitectural attacks in these devices. Both Malware and microarchitectural attacks are imperative threats to computing systems, as their behaviors range from stealing sensitive data to total system failure. With the cat-and-mouse game between Anti-Virus Software (AVS) and attackers, the frequent bolstering of AVS induces large computational overhead. Consequently, hardware performance counter (HPC)-based detection strategies augmented with machine learning (ML) classifiers have gained popularity as a low overhead solution in identifying these malicious threats. However, ML models are operated as black boxes, which results in decisions that are not human understandable. Clarity of the models’ results facilitates the development of more robust systems. Existing explainable frameworks are only capable of determining each feature’s impact on a prediction which does not provide meaningful interpretable outcomes for HPC-based intrusion detection. In this article, we address this issue by proposing an explainable HPC-based double regression (HPCDR) ML framework. Our proposed technique provides relevant transparency through isolation of the most malevolent transient window of an application, thereby allowing a user to efficiently locate the pernicious instructions within the program. We evaluated HPCDR on five microarchitectural attacks and two Malware. HPCDR was successfully able to identify the most malicious function manifested in each intrusive application.
Adversarial Mixup Ratio Confusion for Unsupervised Domain Adaptation

Multimedia applications often involve knowledge transfer across domains, e.g., from images to texts, where Unsupervised Domain Adaptation (UDA) can be used to reduce the domain shifts. Most of the UDA methods are based on adversarial learning. However, previous adversarial domain adaptation methods may suffer from three issues. First, although the features learned by previous methods could fool the domain classifier to make false classification predictions, they may not be domain-invariant. Second, the limited number of training samples make the latent space of features not smooth and continuous enough. Third, the target domain features may lack discriminability. In this paper, we propose a novel adversarial domain adaptation method named Adversarial Mixup Ratio Confusion (AMRC) to alleviate all the above issues. Specifically, we propose a new adversarial training pattern that uses mixup to generate multiple features with different mixup ratios, which represent different intermediate states between the source and target domain. Then, on one hand, we train an estimator to estimate the mixup ratio as accurately as possible. On the other hand, we train a generator to make the estimator be uncertain about the mixup ratio. In this way, our method could learn a continuous and domain-invariant latent space. Furthermore, we apply the intra-domain and cross-domain mixup regularizations to ensure the smoothness and continuity of the latent space, while making the classifier behave more linearly on in-between samples. At last, we exploit the sharpened pseudo-labels of the target samples for self-supervised learning to enhance the discriminability of the target features.The experimental results on 3 benchmarks verify the effectiveness of our method.
Alarmingly Low Physical Activity in Pakistani College Students Compared to American College Students

To identify differences in physical activity (PA) between intercollegiate students in Pakistan and the United States (US). Three hundred (167 US; 133 Pakistani) intercollegiate students completed the International Physical Activity Questionnaire (IPAQ) to assess PA. Perceived Barriers to Physical Activity Questionnaire (BPAQ) was used to assess barriers to PA. Participants were also asked about the recommended level of PA. Chi-squared analysis (χ2 = 99.50, p-value < .001) revealed that American students, specifically females (χ2 = 97.27, p-value < .001), were more active compared to Pakistani students. T-test showed significantly more major barriers to PA in Pakistani students (6.860 ± 5.56) than American students (3.78 ± 3.94). 59.6% of all students correctly recounted the standard PA recommendations, only 1.2% of those students were Pakistani. Pakistani students had lower PA with more major barriers to PA than Americans. Majority of the Pakistani students did not know the recommended level of PA unlike Americans.
Developing Adjustment Factors of Saturation Flow Rates at Signalized Intersections

The purpose of this paper is to study the saturation flow rate and its adjustment factors which is the basis of estimating the signalized intersection capacity.Based on the field data,the passenger car equivalences are calculated using the time headway method,which is adopted to analyze the effect of different type vehicles on the saturation flow rates.In order to determine the effect of lane width on the saturation flow rate,a regression model is developed and the adjustment factor for lane width is obtained.The approach grade effect on the saturation flow rate is studied and the adjustment factor for approach grade is provided.The multiple regression method is used to analyze the right-turn vehicle effect on the through-turn shared lane saturation flow rate and the adjustment factor for right turn is suggested.
Attention-Driven Appearance-Motion Fusion Network for Action Recognition

Recent years have witnessed the popularity of using a two-stream architecture and attention mechanism for action recognition with videos. However, it is time-consuming to train two separate convolutional neural networks (ConvNets), especially with the complex attention mechanism. In this article, we present a novel architecture, termed as Appearance-Motion Fusion Network (AMFNet), to learn efficient and robust action representation from RGB and optical flow data in an end-to-end manner. AMFNet is constructed by connecting a convolutional neural network with an appearance-motion fusion block (AMFB), whose goal is to incorporate appearance and motion streams into a unified framework driven by a cross-modality attention (CMA) mechanism. More specifically, the CMA only relies on optical flow data, which consists of a Key-Frame Adaptive Selection Module (KFASM) and an Optical-Flow-Driven Spatial Attention Module (OFDSAM). The former aims to adaptively identify the discriminative key frames from a sequence, while the latter is able to guide our networks to focus on the action-relevant regions of each frame. We explore two schemes for appearance and motion streams fusion in AMFB from hierarchical and comprehensive levels. The proposed AMFNet is extensively evaluated on five action recognition data sets, including HMDB-51, UCF-101, JHMDB, Penn and Kinetics-400. Compared to the state-of-the-art methods operated at RGB and optical flow, the experimental results validate that our AMFNet achieves a comparable performance with a pure 2D-Single-ConvNet design.
Indoor Location Data for Tracking Human Behaviours: A Scoping Review

Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken.
Virtual Peer Educator Training Program About Sexually Transmitted Infections in Adolescents

Sexually transmitted infections (STIs) are caused by fungi, bacteria, parasites or viruses and are transmitted from one person to another through having intercourse. Lack of knowledge will cause teenagers to have sex unsafely. Teenagers need to be taught about reproductive health because STIs are common and there is little knowledge about STIs. This study aimed to find out the effectiveness of a virtual peer educator training program about STIs in teenagers. This was a quantitative quasiexperimental study. There were 38 participants who were aged 15-18 years old and were recruited through convenience sampling. The data were collected by using two questionnaires: one about the awareness of STIs (Cronbach’s alpha = 0.795) and one about the attitudes towards STIs (Cronbach’s alpha = 0.709). The data were analyzed by using paired t-tests. This study showed that the training program was effective in enhancing teenagers’ awareness and attitude about STIs. Educating teenagers about reproductive health is important to make they know about STIs, themselves and their surroundings. Keywords: virtual, peer educator, training, STI, youth
Design, modeling, and testing of a soft actuator with variable stiffness using granular jamming

Abstract Soft robots combine the load-bearing capability of rigid material with the resilience, shape-shifting capabilities of soft materials. This paper presents a novel soft actuator with stiffness variation using particulate jamming technology. We design a hybrid composite structure consisting of driving layer and jamming layer. The driving layer with the arc air chamber aim to achieve large bending deformation. A membrane containing particles is integrated with driving layer to module its stiffness. The influence factors of stiffness variation were analyzed from energy of point of view. The dependence of granular attributes on the stiffness of the actuator was studied. Furthermore, we illustrated influence of stiffness changes on the kinematic and dynamic performance of the soft actuator. The experimental results showed these performance indexes are twofold. On the one hand, the structural parameters have significant effect on the bending angle, but on the other hand they have little effect on the end force. We found that flow resistance inside air chamber results in bending morphology variation. The dynamic response subjected to a square-wave air pressure was analyzed to exhibit the actuator’s transient and steady vibration behavior. The actuator with greater stiffness has faster responsiveness, but smaller range of motion. These conclusions are helpful to adjust the stiffness behavior and to improve motion performance.
Accuracy and Error Trends of Commercially Available Bat Swing Sensors in Baseball

In baseball, the swing speed and swing angle of the bat just before ball impact are important to increase the speed and horizontal distance of a batted ball. This study investigated the accuracies and error trends of four commercially available bat sensors to measure these parameters. The hitting motions of seven healthy participants were measured simultaneously using the bat sensors and an optical motion capture system, and the swing speeds and swing angles were compared. The swing speed was measured with high accuracy, as indicated by the high intraclass correlation coefficient (ICC) between the bat sensor and the motion capture system measurements (mean ICC = 0.78). However, the ICC for the swing angle was lower (mean ICC = 0.58) than that of the swing speed for all but one bat sensor, indicating low accuracy. Moreover, in the high swing speed range, the accuracy of the swing speed tended to decrease for three bat sensors, but the trend of the swing angle was different among bat sensors. Significant systematic biases or proportional errors were found for all bat sensors, indicating the possibility of error correction. The sensor used in this study can help to evaluate the differences between players with different competition levels and hitting motions. Coaches need to be cautious in taking measurements of players with high swing speeds and in assessing slight changes within an individual.
RoCoF-Minimizing H₂ Norm Control Strategy for Multi-Wind Turbine Synthetic Inertia

The reduction in system inertia under the penetration of power electronic-interfaced generation has fostered various proposals on wind synthetic inertia. Several works in the state of the art propose control strategies to optimize synthetic inertia performance; however, most of the performance metrics indirectly represent frequency control adequacy indicators such as rate of change of frequency or frequency nadir. Also, control definitions assume that there is one variable speed wind turbine and one controller, which limits the applicability of the proposals as power systems normally count with various wind turbines/farms over extended geographical areas with different wind regimes. This work presents a $H_{2}$ optimal control approach for a variable speed wind turbine synthetic inertia controller. First, the objective of the formulation is to explicitly minimize the rate of change of power system frequency. Second, a stability proof for the multi-wind turbine case is proposed, allowing optimal controllers to be independently implemented for an arbitrary number of wind turbines and wind regimes. The effectiveness of the proposed control scheme is demonstrated with a numerical example, considering an empirically-validated, reduced-order model of the Electric Reliability Council of Texas frequency dynamics.
Study on the Relationship Between Male Infertility and DNA Integrity in Spermatozoa Nuclei

The purpose of the present study is to assess the potential relationships between the diagnoses for male infertility based on conventional sperm analysis and the levels of sperm nuclei with damaged DNA in men with reproductive problems using the acridine orange fluorescent test. Totally 87 men with reproductive problems have been investigated. Excellent and good fertility potential was established for 58.6% of the studied individuals, and fair and poor potential of male fertility – for 41.4%. Different diagnoses have been found for the men included in the study – normosoospermia (58.6%), asthenozoospermia (18.4%), oligoasthenozoospermia (11.5%), oligoasthenoteratozoospermia (9.2%) and asthenoteratozoospermia (2.3%). Statistically significant dependencies (P < 0.001) have been observed between the studied sperm quality parameters, as well as for infertility diagnoses done and the level of spermatozoa nuclei with DNA damage. The present study suggests that the acridine orange fluorescence test is sufficiently informative to determine the quality of the fertility potential. The obtained results could be used as a basis for future detailed research on male reproductive health and the potential risks for its deterioration.
Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review

Fine-grained entity typing (FGET) is an important natural language processing (NLP) task. It is to assign fine-grained semantic types of a type taxonomy (e.g., Person/artist/actor) to entity mentions. Fine-grained entity semantic types have been successfully applied in many natural language processing applications, such as relation extraction, entity linking and question answering. The key challenge for FGET is how to deal with label noises that disperse in corpora since the corpora are normally automatically annotated. Various type taxonomies, typing methods and representation learning approaches for FGET have been proposed and developed in the past two decades. This paper systematically categorizes and reviews these various typing methods and representation learning approaches to provide a reference for future studies on FGET. We also present a comprehensive review of type taxonomies, resources, applications for FGET and methods for automatically generating FGET training corpora. Furthermore, we identify the current trends in FGET research and discuss future research directions for FGET. To the best of our knowledge, this is the first comprehensive review of FGET.