Miracle-dz/UniFAR-FTUs huggingface dataset:
An open-source architecture for simulation, execution and analysis of real-time robotics systems

The specification and analysis of the timing are an integral part of a robotics system that requires to be highly reliable. Especially since the demand for robots, which are applied in collaborative environments, is increasing drastically, robots need to be even more reliable and safe. In this paper, we propose a workflow for timing specification and analysis of real-time sensitive component-based robotics systems. Further, we introduce CoSiMA, a C++ based architecture that combines technologies, which are well-known in the domain of robotics. CoSiMA offers the ability to model, simulate, deploy, and analyze a robotics system on different robotic platforms. In addition to that, it offers a real-time safe mechanism to collect execution time data of a system, run in simulation or on the real hardware, to investigate and ensure the desired behavior of the robot. In order to depict the proposed workflow, we implemented an experimental system using CoSiMA, which lets the humanoid robot COMAN perform a Zero Moment Point-based walk on a straight line.
Morphological evolution of MnO2 based nanostructures by tuning the reaction time

The present paper illustrates a morphological evolution of Manganese dioxide (MnO2) nanostructures by varying the reaction time of hydrothermal process. To establish the morphological evolution, three samples were synthesized for three different reaction times (3 hrs, 5 hrs, and 7 hrs). Morphological characterizations of the synthesized nanostructures were performed using FESEM. Further, Raman spectra are presented to validate the crystal structure of the different nanostructures. It is evident from the FESEM images that microsphere/nanosheets based hierarchical structures, nanosheets intertwined with nanorods, and, nanorods based urchin like nanostructures were obtained by tuning the hydrothermal reaction time to 3 hrs, 5 hrs, and 7 hrs, respectively. The underlying morphological evolution mechanism is discussed and correlated with the characterization results. Moreover, three planar devices (based on microsphere/ nanosheets based hierarchical structures, nanosheets intertwined with nanorods, and nanorods based urchin like nanostructures) were fabricated by taking electrical contacts from top of the nanostructure layer and subsequently the device resistances were measured. Further, the electrical characterization finding is correlated with the material characterization results of the synthesized nanostructures for the three samples.
Chemical bath deposited n-ZnO nanostructures on p-Si substrate for photo-detecting applications: Impact of annealing temperature

In this current work, the zinc oxide (ZnO) nanostructures are grown by employing chemical bath deposition (CBD) method. The as grown samples are annealed at 400°C and 600°C for 30 min in argon environment. A comparative study has been performed to study the systematic change of nanostructure morphology, chemical composition, crystallite structure and photo-sensing effects due to thermal annealing of the CBD grown ZnO nanostructure. The 400°C annealed sample exhibit nanosphere structure with significant enhancement in photo responsivity under UV illumination of 13mW/cm2 at 365 nm. The present study suggests that the responsivity of the CBD grown ZnO nanostructure can be improved by thermal annealing at 400°C.
Secure and lightweight biometric-based remote patient authentication scheme for home healthcare systems

Recently, the home healthcare system has emerged as one of the most useful technology for e-healthcare. Contrary to classical recording methods of patient's medical data, which are, based on paper documents, nowadays all this sensitive data can be managed and forwarded through digital systems. These make possible for both patients and healthcare workers to access medical data or receive remote medical treatment using wireless interfaces whenever and wherever. However, simplifying access to these sensitive and private data can directly put patient's health and life in danger. In this paper, we propose a secure and lightweight biometric-based remote patient authentication scheme using elliptic curve encryption through which two mobile healthcare system communication parties could authenticate each other in public mobile healthcare environments. The security and performance analysis demonstrate that our proposal achieves better security than other concurrent schemes, with lower storage, communication and computation costs.
A copy-move forgery detection system using approximation image local binary pattern

During the past few years, digital image forgery detection system has been received a significant attention in the field of analyzing and understanding digital images. A copy-move forgery is introduced in images by copying a part of an image and put it in the same image or in the other image. In this paper, we proposed a system that detects a copy-move forgery in the images. The method involves feature extraction, feature matching, and duplicate block identification. In this paper, AILBP (Approximation image local binary pattern) method is being applied for feature extraction. The number of experiments is initiated on a standard standalone image. The experimental results indicate that the proposed system is effective enough to give high performance in terms of speed and accuracy.
Experimental Validation of a Novel PI Speed Controller for AC Motor Drives With Improved Transient Performances

A proportional–integral (PI) controller is the most frequently used control technique for adjustable speed-motor drives. The main advantage of a PI controller is its ability to ensure zero steady-state error under low-frequency disturbances and model uncertainties as long as the closed-loop system is stable. However, the transient performances, under a PI regulator, are highly dependent on the machine parameters, the external disturbances, and the input constraints. This brief proposes the design process of a novel PI speed controller that is capable of preserving almost the nominal transient performance under the parameter variation and external disturbances such as the load torque. The controller also suppresses the effect of input saturation that may occur because of the armature current limitation. Simulation and experimental tests are carried out to verify the validity of the proposed design process. The results revealed that the proposed PI controller is effective regarding nominal performance recovery and input constraints handling.
Effect of nanoparticles on breakdown, aging and other properties of vegetable oil

Vegetable based natural ester oil is considered as the alternate natural source of traditional mineral insulating oil in electrical transformers and performs dual functions of insulating and cooling materials. In order to maintain both improved electrical and thermal properties, the composite of aluminum oxide and zinc oxide (AhO3+ZnO) nanoparticles was dispersed into soya bean oil, and a blend of sunflower and olive oil with percentage concentration of 0.005g/L to form the nanooils. The purpose of this research is to compare the findings of various tests such as, breakdown voltage, tan delta, flash point, pour point, water content, viscosity and FTIR, performed on nanooil samples before and after aging with that of mineral oil. In case of aged soya bean nano oil, the breakdown voltage, flash point and viscosity are 26%, 22% and 5% higher, and the pour point, water content and tan delta are 1%, 12% and 0.5% lesser than those in mineral oil, respectively. Similarly, in case of aged nano blend of sunflower and olive oil, the breakdown voltage, and flash point are 23% and 41% higher, and the viscosity, pour point, water content and tan delta are 2%, 5%, 15% and 0.7% lesser than those in mineral oil, respectively. All these results showed that the addition of Nanoparticles to the vegetable oil improved electrical and thermal properties more than mineral oil.
Hydrogen Soaking, Displacement Damage Effects, and Charge Yield in Gated Lateral Bipolar Junction Transistors

The effects of 70-keV and 1-MeV electron irradiations on gate-controlled lateral PNP (GLPNP) transistors are evaluated with and without molecular hydrogen (H2) soaking. At a given ionization dose, 1-MeV electron irradiation causes more degradation of current gain in GLPNP transistors that have not been soaked in H2 than 70-keV electrons. This is because linear bipolar transistors are sensitive to both ionization and displacement damage effects, and because 1-MeV electrons induce significant displacement damage in Si-based bipolar junction transistors and 70-keV electrons do not. In H2-soaked transistors, the degradation is much larger than in unsoaked devices, and similar amounts of degradation are observed for 70-keV electron irradiation and 1-MeV electron irradiation. This occurs because ionization–induced release, transport, and reactions of hydrogen in the bipolar-base oxide greatly enhance interface-trap buildup and dominate device response in H2-soaked devices, and because charge yield ratios for 70-keV and 1-MeV electron irradiations differ by less than ~20%.
DWI denoising using overcomplete Local PCA Decomposition

Diffusion Weighted Images normally show a low SNR due to the presence of noise from the measurement process which complicates and potentially bias the estimation of the diffusion parameters. In this paper, a new denosing method is proposed which takes into consideration the multicomponent nature of DW images. This new filter reduces random noise in multicomponent Diffusion MR images by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with similar state of art methods using synthetic and real clinical MR images showing an improved performance in all cases analyzed
Predictors of Organizational Commitment in Nursing: Results from Portugal.

OBJECTIVES This work sought to assess the predictor role of work engagement and social support from the supervisor and coworkers on affective commitment with the organization in nursing staff from southern Portugal. METHODS A cross-sectional study was conducted with participation from 215 nursing professionals from three public hospitals in southern Portugal. RESULTS Of the respondents, 77.21% were women. Statistically significant and positive correlations were observed between affective organizational commitment and the three dimensions of engagement: vigor (r=0.48), dedication (r=0.49), and absorption (r=0.48). Likewise, support from the supervisor and support from coworkers were positively related with affective commitment (r=0.45 and r=0.24, p<0.01). The linear and hierarchical regression model showed the following significant predictors: support from the supervisor (beta=0.28), vigor (beta=0.26), and absorption (beta=0.17). CONCLUSIONS Social support from the supervisor and work engagement (vigor and absorption) are important determinants of the affective bond of nursing professionals with their health organizations. Training and formation of the supervisors in leadership and coaching styles, and labor resources at work would permit increasing the levels of affective commitment in the health units.
ADMM hardware decoder for regular LDPC codes using a NISC-based architecture

The Alternate Direction Method of Multipliers (ADMM) approach is an original method for LDPC decoding based on the linear programming (LP) technique. It introduces a novelty at the error correction performance level. Nevertheless, this method can be toughly implemented due to its high computational complexity. In this paper, an implementation of the ADMM LP decoding algorithm on an FPGA target is presented. Its hardware resource cost is evaluated and compared with the state of the art LDPC decoders using the belief propagation (BP) decoding approach. The preliminary logic synthesis results show that an LP based hardware decoder for LDPC codes should be viable for applications with tough error correction requirements. However, additional research works are required to reach equivalent hardware complexity and throughput performances that are similar to traditional BP based LDPC decoders.
Deep Learning models for turbulent shear flow

Deep neural networks trained with spatio-temporal evolution of a dynamical system may be regarded as an empirical alternative to conventional models using differential equations. In this thesis, such deep learning models are constructed for the problem of turbulent shear flow. However, as a first step, this modeling is restricted to a simplified low-dimensional representation of turbulence physics. The training datasets for the neural networks are obtained from a 9-dimensional model using Fourier modes proposed by Moehlis, Faisst, and Eckhardt [29] for sinusoidal shear flow. These modes were appropriately chosen to capture the turbulent structures in the near-wall region. The time series of the amplitudes of these modes fully describe the evolution of flow. Trained deep learning models are employed to predict these time series based on a short input seed. Two fundamentally different neural network architectures, namely multilayer perceptrons (MLP) and long short-term memory (LSTM) networks are quantitatively compared in this work. The assessment of these architectures is based on (i) the goodness of fit of their predictions to that of the 9-dimensional model, (ii) the ability of the predictions to capture the near-wall turbulence structures, and (iii) the statistical consistency of the predictions with the test data. LSTMs are observed to make predictions with an error that is around 4 orders of magnitude lower than that of the MLP. Furthermore, the flow fields constructed from the LSTM predictions are remarkably accurate in their statistical behavior. In particular, deviations of 0.45% and 2.49% between the true data and the LSTM predictions were obtained for the mean flow and the streamwise velocity fluctuations, respectively.
Efficient parameter-extraction of SPICE compact model through automatic differentiation

A novel parameter extraction method for compact MOSFET models is proposed. The proposed method exploits automatic differentiation (AD) technique that is widely used in the training of artificial neural networks. In the AD technique, gradient of all the parameters of the MOSFET model is analytically calculated as a graph to reduce computational cost. On the basis of the calculated gradient, the model parameters are efficiently optimized. Through experiments using SPICE models, the parameter extraction using the proposed method achieved 7.01x speedup compared to that using the numerical-differentiation method.
Exclusive enteral nutrition protects against inflammatory bowel disease by inhibiting NF-κB activation through regulation of the p38/MSK1 pathway

Although enteral nutrition therapy for inflammatory bowel disease has been confirmed to be an effective treatment method, the exact mechanism responsible for the effects of enteral nutrition remains unclear. The aim of the present study was to investigate the protective effect of exclusive enteral nutrition (EEN) against colitis, and to elucidate the potential mechanisms by inhibiting p65 activation via regulating the p38/mitogen- and stress-activated protein kinase-1 (MSK1) pathway. Experiments were performed by establishing dextran sulfate sodium (DSS)-mice colitis and picrylsulfonic acid solution (TNBS)-induced rat colitis, and the results demonstrated that EEN treatment attenuated body weight loss, colon length shortening and colonic pathological damage caused by colitis. EEN also inhibited inflammatory cells infiltration and decreased myeloperoxidase and inducible nitric oxide synthase activities. Furthermore, EEN significantly reduced the production of pro-inflammatory mediators in serum and the colon. Mechanically, EEN suppressed activation of p65 by inhibiting the p38/MSK1 pathway. In conclusion, the present study demonstrated that EEN attenuated DSS- and TNBS-induced colitis by inhibiting p65 activation via regulating the p38/MSK1 pathway, thus suggesting that EEN is effective in the treatment of colitis.
Life-History Multistability Caused by Size-Dependent Mortality

Body size is a key determinant of mortality risk. In natural populations, a broad range of relationships are observed between body size and mortality, including positive and negative correlations. Previous evolutionary modeling has shown that negatively size-dependent mortality can result in life-history bistability, with early maturation at small size and late maturation at large size representing alternative fitness optima. Here we present a general analysis of conditions under which such life-history bistabilities can occur, reporting the following findings. First, alternative fitness optima can be found for any arbitrarily chosen forms of mortality functions, including functions according to which mortality smoothly declines with size. Second, while bistabilities occur more readily under negatively size-dependent mortality, our analysis reveals that they can also emerge under positively size-dependent mortality, a feature missed in earlier work. Third, any sharp drop of mortality with size facilitates bistability. Fourth, if the mortality regime involves more than one such sharp drop, multistable life histories can occur, with alternative fitness optima straddling each of the drops. Paradoxically, our findings imply that, fifth, a species-poor predator community capable of creating a rugged mortality regime is conducive to evolutionary multistability, which could act as a stepping stone toward prey life-history diversification, whereas a species-rich predator community that results in a smoothly varying mortality regime may prevent diversification through this pathway.
Realization of MEMS-based silicon cantilever using bulk micromachining

Micro cantilevers are an important component of smart sensors as they detect the stress on surfaces caused by various physical/ chemical or biological parameters. The proper design of the cantilever dimension helps to achieve optimum sensitivity of sensor. This work presents the design simulations of Si cantilever beam using Finite element method. The design is implemented by fabrication of a pair of cantilevers on a single trench. Deep Reactive-Ion Etching (DRIE) was employed to release the Sicantilever from bulk silicon. The released structure was characterized by using Laser Doppler Vibrometer (LDV). The Eigen frequency of the cantilever measured by LDV is found to be similar to the designed specifications. The design and fabrication aspects have been discussed in the paper along with analysis of the Eigen frequency achieved.
Community-based allocation and migration strategies for fog computing

When moving cloud computing towards the edge in order to provide low latency and location-based Internet services, there are additional considerations that need to be introduced in the traditional cloud approaches to adapt them to fog computing. This paper discusses a proposal for resource allocation and migration strategies adapted specifically to fog computing. The proposal is based on the concept of virtual and physical communities and it has already shown its efficiency when applied to cloud computing. Simulation-based scenarios provide the performance of our community-based proposal following the geographical trajectory of mobile users.
Effect of Varying Levels of Glare on Contrast Sensitivity Measurements of Young Healthy Individuals Under Photopic and Mesopic Vision

Contrast sensitivity (CS), the ability to detect small spatial changes of luminance, is a fundamental aspect of vision. However, while visual acuity is commonly measured in eye clinics, CS is often not assessed. At issue is that tests of CS are not highly standardized in the field and that, in many cases, optotypes used are not sensitive enough to measure graduations of performance and visual abilities within the normal range. Here, in order to develop more sensitive measures of CS, we examined how CS is affected by different combinations of glare and ambient lighting in young healthy participants. We found that low levels of glare have a relatively small impact on vision under both photopic and mesopic conditions, while higher levels had significantly greater consequences on CS under mesopic conditions. Importantly, we found that the amount of glare induced by a standard built-in system (69 lux) was insufficient to induce CS reduction, but increasing to 125 lux with a custom system did cause a significant reduction and shift of CS in healthy individuals. This research provides important data that can help guide the use of CS measures that yield more sensitivity to characterize visual processing abilities in a variety of populations with ecological validity for non-ideal viewing conditions such as night time driving.
E2F1/SP3/STAT6 axis is required for IL-4-induced epithelial-mesenchymal transition of colorectal cancer cells

Colorectal cancer (CRC) is a type of cancer with a mortality rate among the highest worldwide owing to its high rate of metastasis. Therefore, inflammation-associated metastasis in the development of CRC is currently a topic of considerable interest. In the present study, the pro-inflammatory cytokine interleukin-4 (IL-4) was identified to promote the epithelial-mesenchymal transition (EMT) of CRC cells. However, the enhancing effect of IL-4 was more evident in HCT116 cells compared with in RKO cells. Accordingly, an increased expression level of STAT6 was observed in HCT116 cells compared with RKO cells. Further investigations identified that E2F1 was required for maintaining the level of signal transducer and activator of transcription 6 (STAT6) in HCT116 cells. Mechanistically, E2F1 induced specificity protein 3 (SP3) directly by binding to the promoter of the STAT6 gene and activating its transcription in CRC cells. As a result, phosphorylation-activated STAT6 increased the expression of several EMT drivers, including zinc finger E-box-binding homeobox (Zeb)1 and Zeb2, which serve a critical function in IL-4-induced EMT. Rescue experiments further confirmed that IL-4-induced EMT relied on an intact E2F1/SP3/STAT6 axis in CRC cells. Finally, analysis of clinical CRC specimens revealed a positive correlation between E2F1, SP3 and STAT6. The ectopically expressed E2F1/SP3/STAT6 axis indicated a poor prognosis in patients with CRC. In conclusion, the E2F1/SP3/STAT6 pathway was identified to be essential for IL-4 signaling-induced EMT and aggressiveness of CRC cells.
Genetic algorithms and unsupervised machine learning for predicting robotic manipulation failures for force-sensitive tasks

Recent advances in the state-of-the-art force-torque sensors have improved the close-loop control of robotic manipulators. However, it is still challenging to perform a force-sensitive pick-and-place task, unless a considerable number of sensors monitor the process. The predictive capability of failures in conventional robotic object-sorting systems are limited. Using fifteen force-torque samples from the University of California-Irvine (UCI) database, we demonstrate the viability of failure prediction using an unsupervised Machine Learning (ML)-based method, whose learning parameters were optimised via Genetic Algorithms (GAs). This hybrid algorithm was deployed for discriminating between manipulation failure and successful object placement. GA was used to avoid overfitting or overtraining. The proposed model could detect robotic manipulation failures with 91.95% classification accuracy, thus improving on the performance of previous classification methods. This study validates the use of GAs and unsupervised ML to predict the extent of success for force-sensitive object placement using information on forces and torques alone.