Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrothermal actuation of silicon carbide ring resonators

Silicon carbide (SiC) ring resonators have been designed, simulated, and fabricated in order to achieve higher resonant frequency compared to beam resonators. The resonant frequency as a function of the ring radius and central hole radius, as well as the influence of the electrode design on the actuation efficiency have been investigated. Aluminum (Al) electrodes have been fabricated on top of the structures in order to study the electrothermal actuation of the structures. The bimorph Al/SiC ring resonators have been constructed by etching the SiC in inductively coupled plasma. The release of the Si sacrificial layer has been performed with a XeF2 chemical etching. The radial release and area release have been characterized as a function of the central hole dimension at chamber pressure of 1 and 2 Torr, whereby the release rates have been found to increase as the hole dimensions and the etching pressure increases. In addition, the release process has shown to be governed by aperture effects. The rings fabricated with different dimensions have been actuated mechanically and electrothermally, and the resonant frequency detected optically. The resonant frequency has been shown to increase as the ring radius decreases and the hole radius increases, both theoretically and experimentally.
Degradation of beta‐endosulfan by Aspergillus Niger

Abstract The organochlorine insecticide endosulfan is a mixture of two stereoisomers, alpha and beta. The degradation of the more persistent beta isomer was shown using the soil microorganism Aspergillus niger. The relatively rapid rate of degradation of beta‐endosulfan as compared to control, formation of endosulfan diol and absence of formation of the toxic metabolite endosulfan sulfate suggest the possibility of developing a non‐chemical method for degrading endosulfan.
Fabrication of perovskite-type oxide nanopowders as a novel adsorbent in removal of Bromo Thymol Blue dye from aqueous solutions

Nanoparticles of perovskite-type Gd0.5Ca0. 5CrO3 was investigated for its ability to perform as a suitable sorbent for anionic dye from aqueous solution. The nanoperovskite were characterized using X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The effects of various experimental factors; adsorbent dose, contact time, pH and dye concentration were studied by using the batch technique. Experimental results indicate that the prepared Gd0.5Ca0. 5CrO3 nanopowder can remove 91.38% of BTB dye under optimum operational conditions of a dosage of 0.02 g, pH 2, contact time of 25 min and initial dye concentration of 50mg/L. Adsorption isotherms have been modeled by Langmuir, Freundlich, Temkin and Dubinin-Radushkevich (D-R). The Langmuir model displayed the best fit for the isothermal data. The maximum predicted adsorption capacities were 300 mg/g for Bromo Thymol Blue (BTB) dye. The results showed that the sorption kinetics of BTB removal from aqueous solutions onto nanopowder fitted well with the pseudo-secondorder model.
Freezing of Water in Cylindrical Nanopores

Freezing and melting of water in the cylindrical pores of ordered mesoporous silica was studied by differential scanning calorimetry. For MCM-41 silica materials wellresolved melting and freezing peaks were obtained for pore diameters down to 3.0 nm, but not in 2.5 nm pores. The enthalpy of melting and the hysteresis connected with the phase transition in the pores both vanish at a pore diameter �� ∗ ≅ 2.8 nm, indicating that �� ∗ represents a lower limit for first-order freezing/melting of water in the pores. A more complex freezing behavior was observed in partially filled pores of SBA-15 materials (pore diameters 6 to 11 nm), where the adsorbed water film undergoes a delayering transition at a temperature below the freezing point of pore water. Our work suggests that studies of the sorption and phase behavior of water in the pores contribute to a better understanding of the pore lattice deformations caused by water in the pores.
Current limiting properties of an expanding helical arc

The expansion of an arc of helical form is governed by both electromagnetic and aerodynamic forces, themselves functions of the arc current and electric field strength through complex interactions. In attempting to describe the arc's expansion the product of these two quantities, the power input, can be usefully exploited. As the arc's helical length increases, the arc voltage builds up until it opposes the driving voltage, leading to current limitation and interruption. Experimental results showing the evolution of electric field strength with current arc also presented.
Imbalance satellite image colorization with semantic salience priors

Considering the low spatial resolution of remote sensing images, it is unreliable to achieve accurate pixel-level color restoration. Inspirited by human perception, which is sensitive to salience features, in this paper, we propose an approach for colorization of remote sensing images with semantic salience priors. Firstly, based on the DCGAN architecture, we introduce the semantic salience prior, which is designed and learned from existing data set with semantic labels to supervise the training of the network. Then, to eliminate the distortion in foreground color caused by the overwhelming amount of marine or bare land backgrounds, we leverage the idea of focal loss to prevent the vast number of backgrounds from overwhelming the generator. Finally, we evaluate the proposed method on the NWPU-RESISC45 public data set. Both the evaluations and comparisons validate the proposed colorization approach is superior to the state-of-the-art methods on remote sensing images.
Biomimetic and flexible piezoelectric mobile acoustic sensors with multiresonant ultrathin structures for machine learning biometrics

Biomimetic piezoelectric mobile acoustic sensor is fabricated for exceptional accuracy of AI-based biometric authentication. Flexible resonant acoustic sensors have attracted substantial attention as an essential component for intuitive human-machine interaction (HMI) in the future voice user interface (VUI). Several researches have been reported by mimicking the basilar membrane but still have dimensional drawback due to limitation of controlling a multifrequency band and broadening resonant spectrum for full-cover phonetic frequencies. Here, highly sensitive piezoelectric mobile acoustic sensor (PMAS) is demonstrated by exploiting an ultrathin membrane for biomimetic frequency band control. Simulation results prove that resonant bandwidth of a piezoelectric film can be broadened by adopting a lead-zirconate-titanate (PZT) membrane on the ultrathin polymer to cover the entire voice spectrum. Machine learning–based biometric authentication is demonstrated by the integrated acoustic sensor module with an algorithm processor and customized Android app. Last, exceptional error rate reduction in speaker identification is achieved by a PMAS module with a small amount of training data, compared to a conventional microelectromechanical system microphone.
ICP registration based on 3D point clouds feature descriptor

Widely used in 3D modeling, reverse engineering and other fields, point cloud registration aims to find the translation and rotation matrix between two point clouds obtained from different perspectives, and thus correctly match the two point clouds. As the most common point cloud registration method, ICP algorithm, however, requires a good initial value, not too large transformation between the two point clouds, and also not too much occlusion; otherwise, the iteration would fail to converge to a correct result. To solve this problem, this paper proposes an ICP matching algorithm based on the local features of point clouds. With this algorithm, a robust and efficient three-dimensional local feature descriptor is firstly designed by combining the density, curvature, and normal information of the point clouds, then based on the feature description, the correspondence between the point clouds and also the initial registration result are found, and finally, the aforementioned result is used as the initial value of ICP to achieve fine tuning of the registration result. The experimental results on public data sets show that the ICP algorithm boasts good registration precision and robustness, and a fast running speed as well.
Classification and recognition method of fundus images based on SE-DenseNet

With the growth of the aging population, the incidence of eye diseases is getting higher and higher. Traditional manual diagnosis has strong subjectivity and limitations. Computer-aided diagnosis can improve the accuracy of diagnosis while accelerating the diagnosis. The traditional convolutional neural network cannot fully obtain the effective features of the image, which makes the classification accuracy of the image low. The computer-aided diagnosis algorithm proposed in this paper integrates DenseNet and Squeeze-and-Excitation Networks (SENet) in deep learning based on image de-watermarking and data enhancement, while fully extracting and utilizing fundus images features while improving the network's global features information utilization. The experimental results show that the classification accuracy of the model in the fundus image is 0.9528. Compared with other convolutional networks, SEDenseNet achieves the highest accuracy.
Small target detection method with high accuracy for visible colony RGB image formed by bacteria in water

The detection of microorganisms like bacteria in water is extremely important and challenging. Colony detection is an effective solution because visible colonies can be formed by bacteria in water. Aiming at the characteristics of small targets and regular shape in RGB image, a cascade network combining SSD_MOBILENET_V1_FPN with SVM HOG classifier is designed to detect and count the colonies with high accuracy, both error rate and missing rate were less than 3%. To solve the performance bottleneck, a on-chip system device Zynq UltraScale+ MPSoC EV is applied to accelerate colony count cascade network based on FPGA which could support AI computing acceleration, it can detect and count 10 colony images per second with the advantage of portability and low power consumption.
Improved current–voltage characteristics of downstream plasma enhanced chemical vapor deposition SiNx deposited at low temperature by using He as a dilution gas

We have compared the current–voltage characteristics of silicon nitrides prepared from the two gas combinations N2/NH3/SiH4 (N2–SiNx) and He/NH3/SiH4 (He–SiNx) at temperatures between 100 and 350 °C. A downstream plasma enhanced chemical vapor deposition reactor with a non-ECR microwave plasma source has been used. While N2–SiNx with reasonable electrical properties requires deposition temperatures of about 350 °C, the characteristics of He–SiNx even improve at decreasing process temperatures. Almost identical current–voltage characteristics are found for both N2–SiNx and He–SiNx prepared at 350 °C exhibiting an Ohmic behavior at low fields and a Poole–Frenkel (PF) conduction at high fields. At a deposition temperature of 100 °C the He–SiNx with a dielectric strength of 1.13×107 V/cm and an onset field strength of the PF conduction of 6.9×106 V/cm is in contrast to the N2–SiNx with a dielectric strength of 4.3×106V/cm and a PF onset field strength of as low as 2×105 V/cm resulting in many orders of magnit.
Radar image prediction using generative adversarial networks

Doppler radar is the main remote sensing equipment to monitor severe convective weather which has significant threats to social and economic activities. It is important to accurately predict the time and location of severe weather events. In this study, we use a deep learning technique to predict severe weather events based on radar images. Firstly, we transform the prediction problem into a binary classification problem and use Generative Adversarial Networks (GANs) to construct a classifier. Then Doppler radar images are used to train the model. The critical success index, probability of detection, and false alarm ratio are used to evaluate the prediction results. The experimental results show that the GANs model provides satisfactory results.
Music-driven lighting manipulation for stage performance visual design

When creating real or virtual computer graphics performance. The desired visual effect is influenced by a range of factors, i.e. the music effect, illumination, etc. Although there are mature theoretical supports for the current stage design, it still requires rich experience for the designers to realize the idea. And there are still no effective methods to modify the 3D scene illumination considering the music atmosphere. To address this problem, we propose a music-driven Stage lighting design system to automatically control the lighting color according to the background music. Our technique classifies the music with category tags, then formulates the evaluation function with the visual properties and the scene view images to evaluate and obtain harmonious lighting color. Experiments and analysis demonstrate that the advantages of our system provide strong support tools for the stage light designers.
Role of Human Milk Bioactives on Infants' Gut and Immune Health

Exclusive human milk feeding of the newborn is recommended during the first 6 months of life to promote optimal health outcomes during early life and beyond. Human milk contains a variety of bioactive factors such as hormones, cytokines, leukocytes, immunoglobulins, lactoferrin, lysozyme, stem cells, human milk oligosaccharides (HMOs), microbiota, and microRNAs. Recent findings highlighted the potential importance of adding HMOs into infant formula for their roles in enhancing host defense mechanisms in neonates. Therefore, understanding the roles of human milk bioactive factors on immune function is critical to build the scientific evidence base around breastfeeding recommendations, and to enhance positive health outcomes in formula fed infants through modifications to formulas. However, there are still knowledge gaps concerning the roles of different milk components, the interactions between the different components, and the mechanisms behind health outcomes are poorly understood. This review aims to show the current knowledge about HMOs, milk microbiota, immunoglobulins, lactoferrin, and milk microRNAs (miRNAs) and how these could have similar mechanisms of regulating gut and microbiota function. It will also highlight the knowledge gaps for future research.
Timing the Juvenile-Adult Neurohormonal Transition: Functions and Evolution

Puberty and metamorphosis are two major developmental transitions linked to the reproductive maturation. In mammals and vertebrates, the central brain acts as a gatekeeper, timing the developmental transition through the activation of a neuroendocrine circuitry. In addition to reproduction, these neuroendocrine axes and the sustaining genetic network play additional roles in metabolism, sleep and behavior. Although neurohormonal axes regulating juvenile-adult transition have been classically considered the result of convergent evolution (i.e., analogous) between mammals and insects, recent findings challenge this idea, suggesting that at least some neuroendocrine circuits might be present in the common bilaterian ancestor Urbilateria. The initial signaling pathways that trigger the transition in different species appear to be of a single evolutionary origin and, consequently, many of the resulting functions are conserved with a few other molecular players being co-opted during evolution.
An Effective Model of Diabetes Care and Education: The ADCES7 Self-Care Behaviors™

Purpose The ADCES7 Self-Care Behaviors™ (ADCES7) is a robust framework for self-management of diabetes and other related conditions, such as prediabetes and cardiometabolic diseases. It is the position of the Association of Diabetes Care and Education Specialists (ADCES) that at the cornerstone of diabetes self-management education and support, the ADCES7 is the framework for achieving behavior change that leads to effective self-management through improved behavior and clinical outcome measures. The ADCES7 model guides the health care team in effective person-centered collaboration and goal setting to achieve health-related outcomes and improved quality of life. Continued research and evidence are critical to expand this model and broaden its application to other chronic conditions. Given the advances in the science of diabetes management as well as diabetes self-management education and support, ADCES has evaluated the ADCES7 within the framework of these advances, including the digital and dynamic health care landscape. Conclusion This revised position statement blends the updates in research and ADCES’s vision and expansion beyond diabetes to refresh the ADCES7 framework. This revision reflects the perspectives of all members of the health care team as they problem solve with individuals who are at risk for or who have diabetes and related conditions to achieve healthier outcomes.
Harnessing Visible Representation to Mitigate Bias

Numerous attempts to improve diversity by way of changing the hearts of decision makers have fallen short of the desired outcome. One underappreciated factor that contributes to bias resides not in decision makers’ hearts, but instead in their minds. People possess images, or mental representations, for specific roles and professions. Which mental image or representation springs spontaneously to mind depends on the current status quo within a field. Whether or not an individual or groups’ appearance matches visual stereotypes results in perceptually mediated preferences and prejudices, both of which harbor pernicious assumptions about who belongs in a professional setting and why. Leveraging these scientific insights can enact change. Shifting visible exemplars can change people’s mental representations and their heart’s evaluative reactions to others.
Multiresponsive polymers: nano-sized assemblies, stimuli-sensitive gels and smart surfaces

The complex function of living systems is dictated by their inherent cooperative response to multiple external stimuli which induce dynamic changes in their physicochemical properties. Advances in the areas of nano- and bio-technology demand for the development of “smart” synthetic materials that would resemble the living systems in their complex behaviour as a response to applied stimuli. This reversible response directs the formation of hierarchical self-assemblies or stimulates changes in the volume, the shape or the surface characteristics of the system. Progress in this rapidly expanding area can lead to the development of dynamically multiresponsive constructs in the form of polymers, particles, gels or surfaces, for potential use in a wide range of applications such as drug delivery, tissue engineering, self-healing materials, bioseparations, sensors and actuators. This review highlights the recent advances in polymer chemistry to design multiresponsive polymeric materials that recognize independently or synergistically more than one stimulus exhibiting collective responses. Emerging developments, challenges and future trends in this exciting field are also discussed.
X-ray crystallographic structure determination of detigloyldihydroazadirachtin and reassignment of the structure of the limonoid insect antifeedant azadirachtin

The structure of detigloyldihydroazadirachtin (tigloyl = 2-methylcrotonoyl) has been determined by single-crystal X-ray diffraction methods and for the first time permits unambiguous structure assignment of the limonoid insect antifeedant azadirachtin.
Preparation and characterization of nanosized zirconium (hydrous) oxide particles

A method for the preparation of nanosized zirconium (hydrous) oxide particles of narrow size distribution is described. The procedure yields stable dispersions at low temperatures and short reaction times in the absence of surfactants, using inorganic zirconium salts. Crystal structure, particle size distribution, electrokinetic properties, stability, and thermal behavior of the prepared particles were investigated. Colloidal dispersions were treated with ultrasound to study their effect on the crystal structure of the calcined samples.
Oligo(trimethylene carbonate)-based supramolecular biomaterials

A supramolecular polymer system is presented in which relatively short trimethylene carbonate (TMC) prepolymers are linked via reversible quadruple hydrogen bonding ureidopyrimidinone (UPy) moieties. Several UPy-modified bifunctional and trifunctional TMC polymers were synthesized. Tunability of the mechanical and thermal properties was achieved by mixing different trifunctional UPy−TMC with bifunctional UPy−TMC polymers. The concentration of UPy groups influenced the crystallization behavior of the materials. The UPy-modified TMC polymers displayed melting endotherms, which are caused by aggregation of the UPy units into small crystallites. It is assumed that the UPy dimers stack in the lateral direction due to urethane hydrogen bonding. Atomic force microscopy confirmed the presence of fiberlike stacks. These crystallites give the material its mechanical strength and prevent flowing of the material after processing into 3D scaffolds. More importantly, these polymers can be processed quite easily at slightly elevated temperatures. Because of the strong temperature dependence of the reversible nature of the hydrogen bonds, their melt viscosities are low, while at temperatures below 50 °C excellent mechanical properties are found as a result of supramolecular, physical cross-linking. The UPy−TMC polymers are shown to be biocompatible and fibroblasts proliferate well on drop cast films of UPy−TMC. Thus, these novel supramolecular UPy−TMC polymers are very promising, biocompatible, tunable, and easily processable biomaterials for applications such as tissue engineering.
Marangoni flow in liquid crystal interfaces

This letter presents the equation of capillary hydrodynamics for interfaces between viscous isotropic fluids and nematic liquid crystals (NLCs) to establish the existence of a new class of Marangoni flow. The interfacial stress balance equation involves the surface divergence of the surface stress tensor. It is shown that the anisotropic elastic contribution to the surface stress tensor gives rise to bending stresses and tangential forces. When the tensor order parameter that describes the NLC structure has surface gradients, a tensor order parameter-driven Marangoni flow is predicted. The strength of the predicted effect is proportional to the nematic–isotropic interaction energy characteristic of the interface, and the direction of flow is from low energy regions towards high energy regions.
Fabrication of electron beam generated, chirped, phase mask (1070.11–1070.66 nm) for fiber Bragg grating dispersion compensator

We report on the fabrication of a chirped, phase mask that was used to create a fiber Bragg grating (FBG) device for the compensation of chromatic dispersion in longhaul optical transmission networks. Electron beam lithography was used to expose the grating onto a resist-coated quartz plate. After etching, this phase mask was used to holographically expose an index grating into the fiber core [K. O. Hill, F. Bilodeau, D. C. Johnson, and J. Albert, Appl. Phys. Lett. 62, 1035 (1993)]. The linear increase in the grating period, “chirp,” is only 0.55 nm over the 10 cm grating. This is too small to be defined by computer aided design and a digital deflection system. Instead, the chirp was incorporated by repeatedly rescaling the analog electronics used for field size calibration. Special attention must be paid to minimize any field stitching and exposure artifacts. This was done by using overlapping fields in a “voting” method. As a result, each grating line is exposed by the accumulation of three overlapping .