Provably safe navigation for mobile robots with limited field-of-views in dynamic environments

This paper addresses the problem of navigating in a provably safe manner a mobile robot with a limited field-of-view placed in a unknown dynamic environment. In such a situation, absolute motion safety (in the sense that no collision will ever take place whatever happens in the environment) is impossible to guarantee in general. It is therefore settled for a weaker level of motion safety dubbed passive motion safety: it guarantees that, if a collision takes place, the robot will be at rest.     

Photonic Microhand with Autonomous Action

Grabbing and holding objects at the microscale is a complex function, even for microscopic living animals. Inspired by the hominid‐type hand, a microscopic equivalent able to catch microelements is engineered. This microhand is light sensitive and can be either remotely controlled by optical illumination or can act autonomously and grab small particles on the basis of their optical properties. Since the energy is delivered optically, without the need for wires or batteries, the artificial hand can be shrunk down to the micrometer scale. Soft material is used, in particular, a custom‐made liquid‐crystal network that is patterned by a photolithographic technique. The elastic reshaping properties of this material allow finger movement, using environmental light as the only energy source. The hand can be either controlled externally (via the light field), or else the conditions in which it autonomously grabs a particle in its vicinity can be created. This microrobot has the unique feature that it can distinguish between particles of different colors and gray levels. The realization of this autonomous hand constitutes a crucial element in the development of microscopic creatures that can perform tasks without human intervention and self‐organized automation at the micrometer scale.     

Microstructural and microwave dielectric properties of LZT (Li2ZnTi3O8) ceramics sintered in presence of bismuth borate glass for LTCC applications

In the present research, the Li₂ZnTi₃O₈(LZT) ceramics were synthesized throughout solid-state ceramic processing, then mixed with bismuth borate (BiBO) glass prepared based on conventional melt quenching method. Wetting behavior of BiBO glass on the LZT ceramic substrate was monitored by hot stage microscopy. Afterward, dielectric LZT ceramics containing different amounts of BiBO glass (0.25–6 wt%) were sintered at various temperatures. X-ray diffraction and electron back scatter diffraction examinations revealed the presence of two crystalline phases of Li₂ZnTi₃O₈ and Bi₂Ti₂O₇. The maximum value of relative density (above 95%) was obtained in the case of specimens contained more than 5 wt% glass. The microwave dielectric properties of the finally sintered BiBO glass containing LZT ceramics were as follows: dielectric constant (ε_r) = 21.44–25.09, quality factor (Q × f) = 10839–54708 GHz and temperature coefficient of resonant frequency (τ_f) = (? 15.58) ? (? 12.86)ppm/°C.     

Complex structural contribution of the morphotropic phase boundary in Na0.5Bi0.5TiO3 - CaTiO3 system

The correlation between structure and dielectric properties of lead-free (1-x)Na_0.5Bi_0.5TiO₃ - xCaTiO₃ ((1-x)NBT - xCT) polycrystalline ceramics was investigated systematically by X-ray diffraction, combined with impedance spectroscopy for dielectric characterizations. The system shows high miscibility in the entire composition range. A morphotropic phase boundary (MPB), at 0.09?≤?x?<?0.15 was identified where rhombohedral and orthorhombic symmetries coexist at room temperature. The fraction of orthorhombic phase increases gradually with x in the MPB region. Dielectric measurements reveal that the relative permittivity increase with addition of Ca²⁺. This behavior is unusual with this kind of doping. A thermal hysteresis occurred only in the MPB composition which varies in a non-monotonically manner with x, detected by dielectric properties. This phenomenon is related to the crystalline microstructure by a linear relationship between the fraction of each phase and dielectric properties, and, more precisely, to the strong interaction between rhombohedral and orthorhombic phases.     

Palladized dysprosium fluoride nanorods as a new performance catalyst in direct methanol fuel cell

Fuel cells are a new type of batteries that produce electricity from a continuous source of alcohols as long as fuel is inserted. In this study, decorated palladium nanoparticles (PdNPs) on dysprosium fluoride (DyF₃) nanorods (DyFNRs)‐multiwalled carbon nanotubes (MWCNTs) were used for electrooxidation of methanol. DyFNRs were synthesized by the hydrothermal method, and the proposed multifunctional catalyst (DyFNRs/MWCNT‐PdNPs) was identified by several methods such as X‐ray diffraction, elemental mapping images, field emission scanning electron microscopy, energy dispersive analysis of X‐rays, and transmission electron microscopy which demonstrated a uniform distribution and high dispersion of the PdNPs on the supports. The electrocatalytic activity toward methanol electrooxidation on glassy carbon electrode (GCE) with DyFNRs/MWCNT‐PdNPs (DyFNRs/MWCNT‐PdNPs/GCE) was investigated by cyclic voltammetry (CV) and chronoamperometry (CA). Experimental results showed a high improvement in oxidation potential and peak current of methanol electrooxidation by DyFNRs/MWCNT‐PdNPs in comparison to DyFNRs and PdNPs. The values of the catalytic rate constant (k) and physical dimension (D_s) for methanol oxidation on the DyFNRs/MWCNT‐PdNPs/GCE catalyst were calculated 0.008 s^?1 and 1.43, respectively. Moreover, the order of reaction was determined to be 0.43 and 0.13 for CH₃OH and NaOH, repectively. Finally, the synthesized catalyst was evaluated in direct methanol fuel cell (DMFC). The single DMFC with proposed anodic catalyst, DyFNRs/MWCNT‐PdNPs, indicated a power density of 4.4 mW·cm^?2 at a current density of 18 mA·cm^?2 in alcohol (1 M) and NaOH (1 M).     

Photo-electrochemistry of metallic titanium/mixed phase titanium oxide

In this study, the effect of potassium hydroxide concentration in anodization bath, anodization time, and calcination temperature on the photo-electrochemical behavior of metallic titanium/mixed phase titanium oxide is investigated. Further, the phase structure of a titanium oxide photocatalyst prepared on a titanium electrode through a high-voltage anodization method is examined. The study exploits photo-electrochemical, Fourier transform infrared spectroscopy attenuated total reflectance (FTIR–ATR), X-ray diffraction, and Raman spectroscopic methods to obtain better insights into the mechanism of mixed-phase titanium oxide formation. In this regard, the photo-electrochemical properties of the photocatalysts prepared in single excitation energy, violet light (410 nm), were investigated. The anodization time and the potassium hydroxide concentration in the anodization bath have significant effects on the photo-electrochemical properties of the photocatalysts. The experiments show that the effect of potassium hydroxide concentration is a function of the anodization potential applied, demonstrating different patterns as the anodization potential changes. Furthermore, FTIR-ATR, X-ray diffraction, and Raman spectroscopic studies reveal that the extended anodization times decrease the population of OH-containing groups, leading to lower photo-electrochemical performance. On the other hand, the formation of anatase phases becomes more favorable only in the extended anodization times before application of the calcination process. Additionally, the calcination temperature has a significant impact on the anatase to rutile ratio. Finally, increasing potassium hydroxide concentration leads to the formation of an amorphous titanium oxide layer. It can be concluded that the obtained information might have a significant impact on the preparation of titanium oxide and other metal oxide photocatalysts through the high voltage anodization process.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.