Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Physics-Based Manipulation in Clutter: Combining Image-Based Generalization and Look-Ahead Planning

Physics-based manipulation in clutter involves complex interaction between multiple objects. In this paper, we consider the problem of learning, from interaction in a physics simulator, manipulation skills to solve this multi-step sequential decision making problem in the real world. Our approach has two key properties: (i) the ability to generalize and transfer manipulation skills (over the type, shape, and number of objects in the scene) using an abstract image-based representation that enables a neural network to learn useful features; and (ii) the ability to perform look-ahead planning in the image space using a physics simulator, which is essential for such multi-step problems. We show, in sets of simulated and real-world experiments (video available on https://youtu.be/EmkUQfyvwkY), that by learning to evaluate actions in an abstract image-based representation of the real world, the robot can generalize and adapt to the object shapes in challenging real-world environments.
Evidence of 'Green' Behaviours: Exploring Behavioural Traces of Pro- and Anti-Environmental Behaviours

The current climate crisis requires pro-environmental behaviours to be developed, engaged in, and spread to other people. Behavioural traces, i.e. evidence of other people's pro-environmental behaviour left in the shared environment, have shown to influence people towards being more pro-environmental.In a set of three surveys, we investigate which behavioural traces correspond to a number of pro- and anti-environmental behaviours identified from previous literature, how frequently these behavioural traces are encountered, and their relation with engagement in behaviours.All studies are survey based with a mix of open-ended questions (Surveys 1 & 3) and rating scales (Survey 2). We use network analysis to identify partial correlations within behaviours and traces, respectively, and between behaviours and traces.A total of 91 traces uniquely attributed to 51 pro- and anti-environmental behaviours were identified. On average, behavioural traces are encountered monthly, with a range from never to daily. Noticing traces correlated with engaging in related behaviours for the areas 'Activism' and 'Food', but not 'Building', 'Transport', 'Waste' and 'Stuff'. Participants report that they expect seeing traces more frequently increases the likelihood of adopting the behaviour that produced them. Finally, participants were generally able to infer the causing behaviours when only presented the traces.We show that unique behavioural traces exist for the majority of pro- and anti-environmental behaviours. Traces are noticed and relate to the causing behaviours based on correlational and self-report evidence. Because of the wide variation between behaviours and their traces, further research into specific behaviours is warranted. Use of these findings for interventions are discussed.
Environmental effects on reliability and accuracy of MFCC based voice recognition for industrial human-robot-interaction

Collaborative robots are becoming increasingly important for advanced manufacturing processes. The purpose of this paper is to determine the capability of a novel Human-Robot-interface to be used for machine hole drilling. Using a developed voice activation system, environmental factors on speech recognition accuracy are considered. The research investigates the accuracy of a Mel Frequency Cepstral Coefficients-based feature extraction algorithm which uses Dynamic Time Warping to compare an utterance to a limited, user-dependent dictionary. The developed Speech Recognition method allows for Human-Robot-Interaction using a novel integration method between the voice recognition and robot. The system can be utilised in many manufacturing environments where robot motions can be coupled to voice inputs rather than using time consuming physical interfaces. However, there are limitations to uptake in industries where the volume of background machine noise is high.
ISO 9001 internal decertification motivations: exploring barriers and benefits of certification as withdrawal antecedents

Abstract Thousands of companies worldwide lose their ISO 9001 certification every year. Considering the relevance that this decertification phenomenon has achieved, this study examines the internal motivations for decertification and explores the relationships between motivations and firm’s previous certification barriers and benefits. To achieve this purpose, the study develops a partial least squares structural equation model (PLS-SEM) from the literature and estimates the model based on data collected from a sample of 248 certified organisations. Results reveal that the main antecedents of internal decertification motivations are external factors, namely external decertification motivations and external certification barriers. External barriers do not disappear after certification and have a stronger influence on motivations than (lack of) certification benefits. Internal and external benefits have a small effect on motivations, and internal barriers have no significant effect. In addition, the relationship between external barriers and internal decertification motivations is mediated by external motivations. Similarly, the relation between external benefits and internal motivations is mediated by internal benefits. Currently, there are no other studies on the relationship between internal and external decertification motivations, nor studies identifying the antecedents of both motivations. Thus, the research findings constitute novel contributions to the literature and suggest relevant implications for practice.
The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in Space Weather. The purpose is twofold. On one hand, we will discuss previous works that use machine learning for Space Weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the Space Weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics‐based and machine learning approaches, known as gray box.
Investigating the Effect of Intrinsic Motivation on Alpha Desynchronization Using Sample Entropy

The effect of motivation and attention could play an important role in providing personalized learning services and improving learners toward smart education. These effects on brain activity could be quantified by EEG and open the path to analyze the efficiency of services during the learning process. Many studies reported the appearance of EEG alpha desynchronization during the attention period, resulting in better cognitive performance. Motivation was also found to be reflected in EEG. This study investigated the effect of intrinsic motivation on the alpha desynchronization pattern in terms of the complexity of time series data. The sample entropy method was used to quantify the complexity of event-related spectral perturbation (ERSP) of EEG data. We found that when participants can remember the stimulus, ERSP was significantly less complex than when they cannot. However, the effect of intrinsic motivation cannot be defined by using sample entropy directly. ERSP’s main effect showed that motivation affects the complexity of ERSP data; longer continuous alpha desynchronization patterns were found when participants were motivated. Therefore, we introduced an algorithm to identify the longest continuous alpha desynchronization pattern. The method allowed us to understand that intrinsic motivation has an effect on recognition at the frontal and left parietal area directly.
An RNA-immunoprecipitation via CRISPR/dCas13 reveals an interaction between the SARS-CoV-2 5'UTR RNA and the process of human lipid metabolism

We herein elucidate the function of SARS-CoV-2derived 5'UTR in the human cells. 5'UTR bound host cellular RNAs were immunoprecipitated by gRNA-dCas13 (targeting luciferase RNA fused to SARS-CoV-2 5'UTR) in HEK293T and A549 cells. The 5'UTR bound RNA extractions were predominantly enriched for regulating lipid metabolism. Overexpression of SARS-CoV-2 5'UTR RNA altered the expression of factors involved in the process of the human Mevalonate pathway. In addition, we found that HMG-CoA reductase inhibitors were shown to suppress SARS-CoV-2 5'UTR-mediated translation activities. In conclusion, we deduce the array of host RNAs interacting with SARS-CoV-2 5'UTR that drives SARS-CoV-2 translation and influences host metabolic pathways.
Superconducting nanowire single-photon detectors: A perspective on evolution, state-of-the-art, future developments, and applications

Two decades after their demonstration, superconducting nanowire single-photon detectors (SNSPDs) have become indispensable tools for quantum photonics as well as for many other photon-starved applications. This invention has not only led to a burgeoning academic field with a wide range of applications but also triggered industrial efforts. Current state-of-the-art SNSPDs combine near-unity detection efficiency over a wide spectral range, low dark counts, short dead times, and picosecond time resolution. The present perspective discusses important milestones and progress of SNSPDs research, emerging applications, and future challenges and gives an outlook on technological developments required to bring SNSPDs to the next level: a photon-counting, fast time-tagging imaging, and multi-pixel technology that is also compatible with quantum photonic integrated circuits.
One-year follow-up of the nutritional status of celiac people on a gluten-free diet

Background The gluten-free diet (GFD), the only effective treatment for celiac disease, is usually nutritionally imbalanced. The present work aimed to analyze the evolution of the nutritional status, dietary profile, and symptoms present among celiac people over one year on a GFD while receiving individualized dietary advice. Methods Twenty-seven adults and thirty-one celiac children/adolescents participated in the cohort study. They were followed by 3 visits, at diagnosis (vt0) and after 3 and 12 months (vt3;vt12). Participants filled out dietary and gastrointestinal symptoms questionnaires and received a personalized form from dietitians containing dietary advice and anthropometric and biochemical data evolution. Results Most patients presented normal BMI, fat and muscle mass, and biochemical parameters at diagnosis and vt12. By contrast, all participants consumed protein and lipids in excess and carbohydrates in defect, in both vt0 and vt12. Low intakes of cereals, fruits and vegetables and high of meat were observed, these also remain-ing unchanged after dietary counseling. Symptoms present decreased after vt3 but rebounded in vt12. Conclusions Few changes in dietary pattern and symptom elimination suggested that the intervention was not effective enough. More research is necessary to evaluate whether closer follow up and face-to-face dietary advice improve dietary habits of celiac people. Trial registration: Code PI2016069, Ethical Comitee of the Clinical Investigation of the Basque Country. Registered on 15 July 2016.
Single-Ended Oscillation-Based Test Technique for Passive RF Phase Shifters

Phased array systems have become paramount to the development of next generation wireless communication networks, with errors on phase shifters contributing to beam pointing error. Oscillation-based built-in self-testing (OBIST) of phase shifters may improve on this accuracy. We propose a single-ended oscillation-based test technique for passive RF phase shifters. The method uses a negative resistance oscillator topology, where the phase shifter is used to capacitively load the active negative resistance circuit resulting in a dependency between relative phase shift and output oscillation frequency. Simulation results indicate an average sensitivity of 0.14 MHz/° of phase shift around a nominal 617 MHz output oscillation when testing an X-band reflection-type phase shifter, while the addition of OBT circuitry increases the phase shifter’s mid-band insertion loss by 0.93 dB.
K-MDTSC: K-Multi-Dimensional Time-Series Clustering Algorithm

The increasing capability to collect data gives us the possibility to collect a massive amount of heterogeneous data. Among the heterogeneous data available, time-series represents a mother lode of information yet to be fully explored. Current data mining techniques have several shortcomings while analyzing time-series, especially when more than one time-series, i.e., multi-dimensional timeseries, should be analyzed together to extract knowledge from the data. In this context, we present K-MDTSC (K-Multi-Dimensional Time-Series Clustering), a novel clustering algorithm specifically designed to deal with multi-dimensional time-series. Firstly, we demonstrate K-MDTSC capability to group multi-dimensional time-series using synthetic datasets. We compare K-MDTSC results with k-Shape, a state-of-art time-series clustering algorithm based on K-means. Our results show both K-MDTSC and k-Shape create good clustering results. However, K-MDTSC outperforms k-Shape when complicating the synthetic dataset. Secondly, we apply K-MDTSC in a real case scenario where we are asked to replace a scheduled maintenance with a predictive approach. To this end, we create a generalized pipeline to process data from a real industrial plant welding process. We apply K-MDTSC to create clusters of weldings based on their welding shape. Our results show that K-MDTSC identifies different welding profiles, but that the aging of the electrode does not negatively impact the welding process.
Synthesis and Properties of Multistimuli Responsive Shape Memory Polyurethane Bioinspired from α‐Keratin Hair

Herein, a multistimuli responsive shape memory polyurethane (multiresponsive SMPU) containing disulfide bonds (–S–S–), bioinspired from α‐keratin hair component, was synthesized using a two‐step reaction and activated via four‐varied stimuli, including water, heat, redox agent, and UV‐light. First, the prepolymer was prepared by the reaction between the comonomers of hexamethylene diisocyanate and polycaprolactone‐2000, and once ended, 1,4‐butylene glycol and 3‐mercapto‐1,2‐propanediol (C3H8O2S) have been appended to the prepolymer to initiate the chain‐extension reaction. The chemical structure and properties of the multi‐responsive SMPU were decoded by FT‐IR, XRD, DSC, and Raman spectra. The correlation between microstructure, dynamic‐mechanical‐analysis, and shape‐memory properties of the multi‐responsive SMPU were systemically discussed. As a primary clue, the opening/closing of H‐bonds and disulfide bonds approved that they dictated the shape memory performance. The clear‐cut results clarified that the multiresponsive SMPU endowed composition‐dependent stress relaxation, and its reversible variation of storage modulus was examined by changing temperature. Owing to such molecular switches, the strip pieces were able to represent a multistimuli responsive shape memory effect —induced by water, heat, redox agent (NaHSO3‐H2O2 solutions), and UV‐light. This research paves a feasible road to prepare a novel multistimuli responsive shape memory actuator with high potential features in flexible sensor and robotic applications.
Atmospheric Ammonia Measurements Over a Coastal Salt Marsh Ecosystem Along the Mid‐Atlantic U.S.

Measurements of atmospheric ammonia (NH3) concentrations and fluxes are limited in coastal regions in the eastern U.S. In this study, continuous and high temporal resolution measurements (5s) of atmospheric NH3 concentrations were recorded using a cavity ring‐down spectrometer in a temperate tidal salt marsh at the St Jones Reserve (Dover, DE). Micrometeorological variables were measured using an eddy covariance system which is part of the AmeriFlux network (US‐StJ). Soil, plant, and water chemistry were also analyzed to characterize the sources and sinks of atmospheric NH3. A new analytical methodology was used to estimate the average ecosystem‐scale diurnal cycle of NH3 fluxes by replicating the characteristics of a chamber experiment. This virtual chamber approach estimates positive surface fluxes in continuing strongly stable conditions when mixing with the air above is minimal. Our findings show that tidal water level may have a significant impact on NH3 emissions from the marsh. The largest fluxes were observed at low tide when more soil was exposed. While it is expected that NH3 fluxes will peak when the air temperature maximizes, high tide occurred concurrently with midday peaks in solar irradiance led to a decrease in NH3 fluxes. Furthermore, soil, plant, and water chemistry measurements underpinning the NH3 concentrations and fluxes lead us to conclude that this coastal wetland ecosystem can act as either a sink or a source of NH3. Such measurements provide novel data on which we can base reliable parameterizations to simulate NH3 emissions from coastal salt marsh ecosystems using surface‐atmosphere transfer models.
Uncertainty range of projected soil carbon responses to climate warming in China

Soil carbon is becoming increasingly difficult to predict due to uncertainties in climate warming. The main objective of this research is to estimate the scope of uncertainties in soil carbon, which is the difference between maximal and minimal estimations of soil carbon, caused by uncertainties in climate change in China, by applying the Lund–Potsdam–Jena (LPJ) model and an improved optimization method. Based on an original optimization method (conditional nonlinear optimal parameter perturbation, CNOP‐P), a cost function is revised to evaluate the scope of uncertainties in predicting soil carbon sequestration, avoiding the overestimation of uncertainties. Two types of climate change projections are obtained from 10 global circulation models (GCMs) under the Representative Concentration Pathway (RCP) 4.5 scenario using two methods. For the first type of climate change projection, the upper and lower limits of estimated future soil carbon sequestration are 102.7 and 65.5 Gt, respectively, whereas the reference soil carbon sequestration driven by an ensemble of 10 GCMs is 79.6 Gt. The estimated future soil carbon sequestration ranges from 79.4 to 91.6 Gt for the second type of climate change. The numerical results imply that these two types of climate change, constrained by the projection of 10 GCMs, are the main factors affecting the uncertainty range of estimated soil carbon.
Protein Receptors On Chondrocytes

Here we reviewed various protein receptors and their roles on chondrocytes. In connection with this we summarized that GPCRs, VEGF receptors, integrins, TLRs, LRP receptors, chemokine receptors and growth factor receptors are the main receptors on chondrocytes. We have discussed different receptors involved in chondrocyte differentiation and those activating proliferation in chondrocytes. We conclude that among the myriad of protein receptors expressed by chondrocytes, some (TLRs, LRPs and chemokine receptors) have the important role in osteoarthritis (OA). These receptors could be targeted by pharmaceutical agents to treat intra-articular injuries including osteoarthritis.
The Effect Of Successor Related Factors On Business Continuity On Agrobased Smes In Malaysia

Succession planning is part of human resource plan that is becoming a hot topic as it is very important to support the business continuity. However, many businesses failed to plan their successor well especially the small medium enterprises (SMEs) which also causing the business continuity at risk. According to past researches, small firms mostly found to be unable to sustain and often struggle to retain key employees. In this study, we focused on the successor-related factors relevant to SMEs in determining business continuity. Based on the literature review it was found that the issue received very limited attention. The study also examined the moderation effect of company’s age in the relationship between successor’s related factors and business continuity. The study has distributed 400 questionnaires to Agro related SMEs in Malaysia, and however, 113 sets of questionnaires were successfully returned. Data was analysed using SEM-AMOS and the findings showed that succession planning has positive significant effect on business sustainability while company’s age does not moderate the relationship between succession planning and business sustainability. The findings could assist managers and higher authority of SMEs to plan and recognized the impact of successors related factors in determining the business continuity.
DESIGUALDAD SALARIAL EN LOS SUBSECTORES MANUFACTUREROS EN MÉXICO, 2007-2018

Este documento analiza los determinantes de la desigualdad salarial en la manufactura en Mexico. Entre los determinantes consensuados por la literatura economica se encuentran la apertura comercial, el cambio tecnico, la oferta de empleo y aspectos institucionales del mercado de trabajo. A partir de un modelo con datos de panel, se busca probar la significancia estadistica de estas variables en la determinacion de la desigualdad salarial para los 21 subsectores manufactureros en Mexico, en el periodo 2007-2018; para ello, se estima el indice de desigualdad para cada subsector. Los resultados muestran que la apertura comercial, el cambio tecnico, la oferta de empleo y la reforma laboral inciden de manera directa sobre la desigualdad salarial. Wage Inequality in Mexico’s Manufacture, 2007-2018This paper analyzes the determinants of wage inequality in Mexico’s manufacture. Among the consensual determinants in the economic literature are trade openness, technical change, labor supply, and institutional aspects of the labor market. Therefore, by means of a panel data model, this work seeks to prove the statistical significance of these variables as determinants of wage inequality in the 21 Mexican manufacturing subsectors in the period 2007-2018. The results show that trade openness, technical change, labor supply, and labor reforms impinge positively on wage inequality.
Shielding Performance of Composite Materials Used in Air Vehicles

The metal skin at air vehicles provide an important shielding effectiveness against the effects of high amplitude electromagnetic waves. In recent years, the composite materials with the advantages such as being lighter, causing lower fuel consumption, are used as a replacement of metals. In this paper, electromagnetic shielding performance of composite materials in air vehicle manufacturing industry is investigated. A panel model is used to obtain the shielding performance of these composite materials. Due to the geometrical similarity of air vehicles with a cylinder, a cylindrical shell model is also considered. Analytical calculations for the interaction of an electromagnetic pulse(EMP) with composite materials are carried out for both panel and cylindrical models. Also, the panel and cylindrical models are constructed via Computer Software Technology(CST) program and analytical and simulation results are compared. There is a good agreement with the results.