Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Robotics

The application of deep learning to robotics over the past decade has led to a wave of research into deep artificial neural networks and to a very specific problems and questions that are not usually addressed by the computer vision and machine learning communities. Robots have always faced many unique challenges as the robotic platforms move from the lab to the real world. Minutely, the sheer amount of diversity we encounter in real-world environments is a huge challenge to deal with today’s robotic control algorithms and this necessitates the use of machine learning algorithms that are able to learn the controls of a given data. However, deep learning algorithms are general non-linear models capable of learning features directly from data making them an excellent choice for such robotic applications. Indeed, robotics and artificial intelligence (AI) are increasing and amplifying human potential, enhancing productivity and moving from simple thinking towards human-like cognitive abilities. In this paper, lots of learning, thinking and incarnation challenges of deep learning robots were discussed. The problem addressed was robotic grasping and tracking motion planning for robots which was the most fundamental and formidable challenge of designing autonomous robots. This paper hope to provide the reader an overview of DL and robotic grasping, also the problem of tracking and motion planning. The system is tested on simulated data and real experiments with success.
Micro–X-ray fluorescence (µXRF) analysis of proximal impactites: High-resolution element mapping, digital image analysis, and quantifications

Quantitative insights into the geochemistry and petrology of proximal impactites are fundamental to understand the complex processes that affected target lithologies during and after hypervelocity impact events. Traditional analytical techniques used to obtain major- and trace-element data sets focus predominantly on either destructive whole-rock analysis or laboratory-intensive phase-specific micro-analysis. Here, we present micro–X-ray fluorescence (µXRF) as a state-of-the-art, time-efficient, and nondestructive alternative for major- and trace-element analysis for both small and large samples (up to 20 cm wide) of proximal impactites. We applied µXRF element mapping on 44 samples from the Chicxulub, Popigai, and Ries impact structures, including impact breccias, impact melt rocks, and shocked target lithologies. The µXRF mapping required limited to no sample preparation and rapidly generated high-resolution major- and trace-element maps (~1 h for 8 cm2, with a spatial resolution of 25 µm). These chemical distribution maps can be used as qualitative multi-element maps, as semiquantitative single-element heat maps, and as a basis for a novel image analysis workflow quantifying the modal abundance, size, shape, and degree of sorting of segmented components. The standardless fundamental parameters method was used to quantify the µXRF maps, and the results were compared with bulk powder techniques. Concentrations of most major elements (Na2O–CaO) were found to be accurate within 10% for thick sections. Overall, we demonstrate that µXRF is more than only a screening tool for heterogeneous impactites, because it rapidly produces bulk and phase-specific geochemical data sets that are suitable for various applications within the earth sciences.
Method for automatic compensation of a single-phase earth fault

The paper demonstrates improving the reliability of power supply and operating safety of power receivers in electric networks of 6-10 kV, by controlling the neutral mode in extended electric networks of 6-10 kV with capacitive earth fault currents above 15 A. The purpose of the study is to increase the reliability of power supply and the safety of operation of power receivers in such electric networks, the establishment of patterns in controlling the neutral mode in extended 6-10 kV electric networks with capacitive earth fault currents above 15 A, as well as the development of a method and device automatic detection of single-phase earth fault current. Grounding methods and neutral modes in electric networks with a voltage of 6-10 kV are considered. The method and device for automatically determining the current of a single-phase earth fault were developed. In the result, the relationship between the active and inductive components of the neutral current during series RL grounding of the network Ia/IL = (0.5 ÷ 0.3) was established, which ensures reliable operation of the SPEF protection of both current and directional principles including the changes of capacity abruptly networks caused by switching outgoing lines.
Ultrasound assisted oxidative denitrification of diesel by formic acid/hydrogen peroxide

ABSTRACT The basic nitrogen compounds in diesel were removed by using formic acid/hydrogen peroxide as oxidant under ultrasound, and alcohol was used as extractant. The effect of all factors on removing basic nitrogen compounds was investigated systematically. On the basis of single-factor experiments, the orthogonal experiments were done to optimize denitrification process. The optimum denitrification process was that volume ratio of formic acid to hydrogen peroxide was 3.0:1, volume ratio of oxidation system to diesel was 0.16:1, ultrasonic power was 64 W, reaction temperature was 313 K, ultrasonic radiation time was 2 min, and volume of alcohol was 16 mL. The content of basic nitrogen compounds in diesel was reduced from 85.79 to 3.56 μg/g, and the denitrification rate reached 95.85% under the optimum denitrification process. The treated diesel met the quality requirements.
Performance Analysis of a Wet-Retention Pond in a Small Agricultural Catchment

AbstractUrban wet-retention pond performance has been extensively studied for many water contaminants. However, very few studies have focused on wet-retention pond efficiency under agricultural production systems. This study presents the results drawn from a 5-year data set collected on a retention pond constructed in Saint-Samuel (Canada) at the outlet of a 23.1-hectare watershed under cash crop production. A total of 62 runoff events were analyzed for hydrological performance, 20 events for total suspended solids (TSS), 11 events for total nitrogen (TN), and 14 events for total phosphorus (TP). Peak flows were reduced by 38% on average and the retention pond reduced the frequency and duration of exceedance of the erosion threshold. Drawdown times for 50, 75, 90, and 100% of the runoff volume were 7, 18, 28, and 42 h, respectively. The mean detention time was 3.2 h compared to the watershed lag time of 3.4 h. Mean removal efficiency ratios based on event-mean concentrations (EMC) and event-total loads (E.
SELF-AFFIRMATION MEAT EATING 1 The ‘ me ’ in meat : Does affirming the self make eating animals seem more morally wrong ?

People typically extend limited moral standing to animals reared for food. Prominent perspectives in the literature on animal-human relations characterize this phenomenon as an outcome of moral disengagement: in other words, a strategy that protects people from moral self-condemnation. To provide a direct test of this hypothesis, we exposed people to a self-affirmation manipulation, and hypothesized that this would lead them to be more critical of their own meat eating and be more appreciative of animals’ minds and suffering. Three experiments tested this idea in meat-eaters from the United Kingdom. Two initial experiments (n = 244, n = 247) found that affirming the self made eating animals seem more morally wrong. However, a subsequent pre-registered experiment (n = 719) failed to replicate this effect. In addition, this experiment found no effects of the affirmation procedure on specific beliefs about eating animals that participants consume compared to animals they do not consume. A mini-meta analysis of all the experiments found only weak evidence in support of the idea that affirming the self makes eating meat seem more morally wrong. There was no evidence that the affirmation procedure affected beliefs about animal minds.
BMI self-selection: Exploring alternatives to self-reported BMI

Background Accurately measuring BMI in large epidemiological studies is problematic as objective measurements are expensive, so subjective methodologies must usually suffice. The purpose of this study is to explore a new subjective method of BMI measurement: BMI self-selection. Methods A cross-sectional analysis of the Mitchelstown Cohort Rescreen study, a random sample of 1,354 men and women aged 51–77 years recruited from a single primary care centre. BMI self-selection was measured by asking patients to select their BMI category: underweight, normal weight, overweight, obese. Weight and height were also objectively measured. Results 79% were overweight or obese: 86% of males, 69% of females (P < 0.001) and 59% of these underestimated their BMI. The sensitivity for correct BMI self-selection for normal weight, overweight and obese was 77%, 61% and 11% respectively. In multivariable analysis, gender, higher education levels, being told by a health professional to lose weight, and being on a diet were significantly associated with correct BMI self-selection. There was a linear trend relationship between increasing BMI levels and correct selection of BMI; participants in the highest BMI quartile had an approximate eight-fold increased odds of correctly selecting their BMI when compared to participants within the lower overweight/obese quartiles (OR = 7.72, 95%CI:4.59, 12.98). Conclusions BMI self-selection may be useful for self-reporting BMI. Clinicians need to be aware of disparities between BMI self-selection at higher and lower BMI levels among overweight/obese patients and encourage preventative action for those at the lower levels to avoid weight gain and thus reduce their all-cause mortality risk.
Treatment studies of plutonium-bearing INEEL waste surrogates in a bench-scale arc furnace

Since 1989, the Subsurface Disposal Area (SDA) at the Idaho National Environmental and Engineering Laboratory (INEEL) has been included on the National Priority List for remediation. Arc- and plasma-heated furnaces are being considered for converting the radioactive mixed waste buried in the SDA to a stabilized-vitreous form. Nonradioactive, surrogate SDA wastes have been melted during tests in these types of furnaces, but data are needed on the behavior of transuranic (TRU) constituents, primarily plutonium, during thermal treatment. To begin collecting this data, plutonium-spiked SDA surrogates were processed in a bench-scale arc furnace to quantify the fate of the plutonium and other hazardous and nonhazardous metals. Test conditions included elevating the organic, lead, chloride, and sodium contents of the surrogates. Blends having higher organic contents caused furnace power levels to fluctuate. An organic content corresponding to 50% INEEL soil in a soil-waste blend was the highest achievable before power fluctuations made operating conditions unacceptable. The glass, metal, and off-gas solids produced from each surrogate blend tested were analyzed for elemental (including plutonium) content and the partitioning of each element to the corresponding phase was calculated.
Fatigue property and failure mechanism of TC4 titanium alloy in the HCF and VHCF region considering different forging processes

Based on the self-built three point bending ultrasonic fatigue test system, fatigue behavior of TC4 titanium alloy after different forging processes in high cycle fatigue (HCF) and very high cycle fatigue (VHCF) regimes was discussed in this paper. The experimental results showed that the fatigue S-N curves of TC4 titanium alloy with three different structures presented different characteristics: continuous decline; double platform; linear decline. The fatigue property of TC4 titanium alloy is closely related to the content of primary α phase and β-transformed microstructure. For TC4 titanium alloy, grain refinement and certain volume fraction of the primary α phase contributed to enhancing fatigue property. The fatigue performance of bimodal structure by near β forging was obviously better than two structures by α + β forging in HCF and VHCF regime. It was found that failure mode shifted from the surface at relatively high stress to the subsurface at relatively low stress. For three kinds of structures, crack initiations were observed at the surfaces of specimens in HCF regime. Meanwhile, cracks of the three structures all originated from primary α cleavage planes in the interior. The fatigue life of VHCF is dominated by the crack initiation stage.
A holographic method for generating structured illumination of structured illumination microscopy

We use a holographic method to generate structured illumination which of spatial frequency and phase can be easily modulated. First, a black and white stripe with a certain spatial frequency is generated according to the period and phase of structured illumination, the black area is 0 and the white area is 1. The spatial frequency of the black and white fringe is determined by calculating the spatial frequency of the structured illumination to be finally generated and the magnification of the optical system. A prism phase that can cause lateral movement is applied to the black part of the black and white stripes, so that the light that incident on the black area tilts from the optical axis to the first order of diffraction. While, the light that incident on other parts is not modulated and does not move laterally, and continues traveling along with optical axis. In this way, the tilted beam is bright and dark, and the diaphragm is used to block the 0- order diffracted light and retain the diffracted first-order light, thereby obtaining cosine structured light. Theories and experiments verify the effectiveness of the method. The method to generate cosine structured illumination is simple, fast and accurate, and the spatial frequency and phase can be conveniently adjusted, which is very helpful for the research of structured illumination microscope.
Probabilistic Modeling and Bayesian Filtering for Improved State Estimation for Soft Robots

State estimation is one of the key requirements in robot control, which has been achieved by kinematic and dynamic models combined with motion sensors in traditional robotics. However, it is challenging to acquire accurate proprioceptive information in soft robots due to relatively high noise levels and hysteretic responses of soft actuators and sensors. In this article, we propose a method of estimating real-time states of soft robots by filtering noisy output signals and including hysteresis in the models using a Bayesian network. This approach is useful in constructing a state observer for soft robot control when both the kinematic model of the actuator and the model of the sensor are used. In our method, we regard a hysteresis function as a conditional random process model. We then introduce a dynamic Bayesian network composed of the actuator and the sensor models of the target system using distribution hysteresis mapping. Finally, we show that solving a Bayesian filtering problem is equivalent to suboptimal state estimation of the soft system. This article describes two ways for defining modeling and filtering; one is by Gaussian process regression combined with an extended Kalman filter, and the other is based on variational inference with a particle filter. While the first approach relaxes the uncertainty level in modeling to Gaussian, the second approach illustrates a general probability distribution. We experimentally validate the proposed methods through real-time state estimation of a sensor-integrated soft robotic gripper. The result shows significant improvement in state estimation compared to conventional estimation methods.
Hybrid Deep Learning based approach for ECG Heartbeat Arrhythmia Classification

Background: Myocardial infarction, or heart attack, is caused by a blockage of a coronary artery, which prevents blood and oxygen from accessing the heart properly. Arrhythmias are a form of CVD that refers to irregular variations in the normal heart rhythm, such as the heart beating too quickly or too slowly. Arrhythmias include Atrial Fibrillation(AF),Premature Ventricular Contraction(PVC), Ventricular Fibrillation(VF), and Tachycardia are just a few examples of arrhythmias. It aggravates if not detected and treated on time i.e., on-time /proper diagnosis of arrhythmias may minimize the risk of death. It is very labor-intensive to externally evaluate ECG signals, due to their small amplitude. Furthermore, the analysis of ECG signals is arbitrary and can differ between experts. As a consequence, a computer-aided diagnostic device that is more objective and reliable is needed. Methods: In the recent era, Machine Learning based approaches to detect arrhythmias has been established proficiently. In this view, we proposed a hybrid Deep Learning-based model to detect three types of arrhythmias on MIT-BIH arrhythmia databases. In particular, this paper makes two-fold contributions. First, we translated 1D ECG signals into 2D Scalogram images. When one-dimensional ECG signals are turned into two-dimensional ECG images, noise filtering and feature extraction are no longer necessary. This is notable since certain ECG beats are ignored by noise filtering and feature extraction. Then, based on experimental evidence, we suggest combining two models, 2D-CNN-LSTM, to detect three forms of arrhythmias: Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). Results: The experimental findings indicate that the model attained 99\% accuracy for "normal sinus rhythm," 100\% accuracy for "cardiac arrhythmias," and 99\% accuracy for "congestive heart failures," with an overall classification accuracy of 98.6\%. The sensitivity and specificity were 98.33\% and 98.35\%, respectively. The proposed model, in particular, will aid doctors in correctly detecting arrhythmia during routine ECG screening. Conclusion: As compared to the other State-of-the-art methods our proposed model outperformed and will greatly minimise the amount of intervention required by doctors.
Research and scholarly methods: Systematic reviews

Systematic reviews provide synthesized evidence to answer explicit research questions, using a transparent and reproducible process to minimize bias. Several guides are available to explain the in‐depth methods involved in conducting a systematic review and subsequent statistical analyses (meta‐analysis) if appropriate. Users of systematic reviews should consider the quality of the review prior to applying its evidence to practice, using a critical appraisal tool. While an in‐depth explanation of these methods is beyond the scope of this article, we present the critical steps of a systematic review and refer readers to these resources for further guidance.
Socio‐spatial strategies of school selection in a free parental choice context

The geography of education literature has demonstrated remarkable similarities in school choice strategies across different educational and spatial contexts. The literature finds that school choice strategies of the middle classes rely on deploying different forms of capital (economic, social, and cultural). At the same time, it is evident that the specific geography and institutional idiosyncrasies of the educational landscape are navigated in locally specific ways that are not just classed but also strongly intersect with ethnicity/race. In most contexts moving house plays a central role in school choice. However, residential relocation as part of school choice strategies is highly contingent on national and local regulations of school allocation and enrolment. Investigating the case of the metropolitan area of Amsterdam, this paper uses longitudinal individual, geo ‐ coded register data to study the socio ‐ spatial school choice strategies of an entire cohort of first ‐ born children (2012) in a free ‐ school choice landscape. Employing an analysis of the interconnections of residential relocation and school choice, this paper uses multinomial logit models to assess how school is differentiated by social class and migration background and how it is related to residential (re)location. It finds that school choice strategies are differentiated by both class and ethnicity, but also very strongly depend on residential context. Remarkably, the paper finds that while a substantial number of highly educated, high ‐ income parents move and enrol their children in homogeneous middle ‐ class schools, a majority of households do not move and hence navigate the school landscape in which they find themselves. It suggests that school choice policies indeed reduce the need for residential relocation, but at the same time are conducive to school segregation.
Electronic gambling machine harm in older women: a public health determinants perspective

Abstract Background: Women make up a significant proportion of all gamblers globally. However, there has been limited research which has explored women’s gambling and the factors that may make women, and subgroups of women, vulnerable to harm. Methods: Using a Constructivist Grounded Theory approach, semistructured interviews were conducted with 20 women aged 55 years and over who regularly gambled on electronic gambling machines (EGMs). Results Key factors were identified to illustrate older women’s experiences of gambling and are presented according to four determinants of health: Individual; socio-cultural; environmental; and commercial. While this research confirms that older women’s gambling behaviors are strongly influenced by individual and socio-cultural factors, gambling environments, and industry practices are also influential. Conclusion A model illustrates the interplay of individual, socio-cultural, environmental, and commercial determinants, and the article proposes how they may contribute to gambling harm in older Australian women.
Dose delivery accuracy on helical tomotherapy for 4-dimensional tumor motion — a phantom study

Background The advances in image guidance and capability of highly conformal dose deliveries made possible the use of helical tomotherapy (HT) for lung cancer treatment. To determine the effect of respiratory motion on the delivered dose in HT, film dosimetry using a dynamic phantom was performed. This was a phantom study to determine the effect of motion on the delivered dose in HT. Materials and methods 4D computed tomography (4DCT) was acquired for various target motions of CIRS dynamic phantom (CIRS Inc., Norfolk, USA) with 2.5cm diameter spherical target of volume 8.2 cc moving in the COS4 motion pattern. AveIP images and treatment plans were generated in the HT planning system. Target excursions during treatment delivery were changed in the superior-inferior, anteroposterior and lateral directions. The breathing cycle time was varied from 4 to 5 sec. and also the delivery interruptions were introduced. A film was exposed for each delivery and gamma analysis was performed. Results The gamma pass rate (GPR) with 3%, 2 mm criteria for the target motion in the S-I direction showed a significant reduction from 97.5% to 54.4% as the motion increased from 3 mm to 8 mm (p = 0.03). For the target motion in S-I = 8 mm, L-R = A-P = 3 mm, the percentage decrease in the GPR was 74% (p = 0.001) for three interruptions. Conclusion The ITV based approach in HT is ideal for a shallow breathing situation when the tumor excursions were confined to 5 mm in the S-I and 3 mm in L-R and A-P directions.