Search

This work proposes, analyzes, designs, and validates superior topologies of UHGH converters that are capable of supporting extremely large conversion ratios up to ∼2000× and output voltage up to ∼4-12 kV for future mobile soft robots from an input voltage as low as the range of a 1-cell battery pack. Thus, the converter makes soft robots standalone systems that can be untethered and mobile. The extremely large voltage gain is enabled by a unique hybrid combination of a high-gain switched magnetic element (HGSME) and a capacitor-based voltage multiplier rectifier (CVMR) that, together, achieve small overall size, efficient operation, and output voltage regulation and shaping with simple duty-cycle modulation. With superior performance, power density, and compact size, the UHGH converters prove to be a promising candidate for future untethered soft robots.

Soft robots have the potential to fundamentally change interactions between robots and the surrounding environment, and between robots and animals, and robots and humans in ways that today's hard robots are incapable of doing. However, to realize this potential, soft robot actuators require extremely high voltage supplies of more than 4 kV. The electronics that can satisfy this need currently are either too large and bulky or unable to achieve the high power efficiency required for mobile systems. To meet this challenge, this paper conceptualizes, analyzes, designs, and validates a hardware prototype of an ultra-high gain (UHG) converter that can support extremely large conversion ratios up to ∼1000× to provide up to 5 kV output voltage from an input voltage of ∼5-10 V. This converter is demonstrated to be able to drive HASEL (Hydraulically Amplified Self-Healing Electrostatic) actuators, a promising candidate to realize future soft mobile robotic fishes, from an input voltage range of a 1-cell battery pack. The circuit topology employs a unique hybrid combination of a high-gain switched magnetic element (HGSME) and a diode and capacitor-based voltage multiplier rectifier (DCVMR) to enable compact magnetic elements, efficient soft-charging in all flying capacitors, and adjustable output voltage capability with simple duty-cycle modulation. Achieving an efficiency of 78.2% at 15 W output power, while providing 3.85 kV output from 8.5 V input, the proposed UGH converter proves to be a promising candidate for future untethered soft robots.

This article presents how over two summers the authors worked with students from Syria, Pakistan, Tanzania, Mexico, Honduras, China, and Vietnam to create their own chalkboard project. They wrote "I am" poems and "I remember" memories. They engaged in the arts, creating their personal comic book heroes and leaving inspired messages in chalk on the sidewalks downtown. They took a chalkboard workshop to the streets of Oshkosh, to a retirement home, to the school district office, and to the university, where they asked and answered thought-provoking questions in a dialogue with university students, teachers, and community members.

Low-voltage ride-through (LVRT) capability is crucial for wind power plants that are grid-connected. A grid code requires wind farms to remain on-grid and inject a specific reactive current when a grid fault occurs. The requirements are satisfied by a cooperative control scheme of a battery energy storage system (BESS) and grid-side converter (GSC) for improving the LVRT capacity of wind turbine generators using permanent magnet synchronous generators. The proposed approach uses the BESS to absorb a power difference that appears in the back-to-back converters during the LVRT operation. Hence, the DC-link voltage is regulated by the BESS. In addition, the GSC control allows reactive current to be injected into the grid according to the grid code requirements. Finally, the wind turbine generators, which use the proposed approach, are built in MATLAB/Simulink. The theoretical analysis of the proposed method is validated by simulation.

Red dragon fruit (Hylocereus polyrhizus), recognized globally for its substantial nutrient content and health benefits, has been extensively studied; studies have particularly focused on the fruit, while the composition of the stem remains less explored. This research focuses on optimizing fermentation parameters for red dragon fruit wine, specifically examining yeast-strain selection, juice-to-water dilution ratios, and yeast concentrations. Saccharomyces cerevisiae RV002 emerged as the optimal strain due to its robust performance and adaptability under adverse conditions. The study identified a 50% dilution ratio as ideal for maximizing clarity and the sensory attributes of the wine, whereas dilution ratios exceeding 90% significantly reduced ethanol content below acceptable commercial standards. An optimal yeast concentration of 1 g/L was found to balance microbial suppression and alcohol yield effectively; deviations from this concentration led to microbial contamination or impaired fermentation dynamics. Fermentation markedly altered the biochemical properties of Hylocereus polyrhizus, reducing sugar and vitamin C levels while increasing polyphenol content and antioxidant activity, thereby enhancing potential health benefits. These findings underscore the transformative effects of microbial activity on the substrate’s chemical landscape and highlight the potential of tailored fermentation strategies to enhance the utility and value of underutilized fruits in sustainable agricultural practices.

Significance: Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. Aim: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Approach: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. Results: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within <2% residual error margin. Conclusions: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification. [ABSTRACT FROM AUTHOR]

Red dragon fruit (Hylocereus polyrhizus), recognized globally for its substantial nutrient content and health benefits, has been extensively studied; studies have particularly focused on the fruit, while the composition of the stem remains less explored. This research focuses on optimizing fermentation parameters for red dragon fruit wine, specifically examining yeast-strain selection, juice-to-water dilution ratios, and yeast concentrations. Saccharomyces cerevisiae RV002 emerged as the optimal strain due to its robust performance and adaptability under adverse conditions. The study identified a 50% dilution ratio as ideal for maximizing clarity and the sensory attributes of the wine, whereas dilution ratios exceeding 90% significantly reduced ethanol content below acceptable commercial standards. An optimal yeast concentration of 1 g/L was found to balance microbial suppression and alcohol yield effectively; deviations from this concentration led to microbial contamination or impaired fermentation dynamics. Fermentation markedly altered the biochemical properties of Hylocereus polyrhizus, reducing sugar and vitamin C levels while increasing polyphenol content and antioxidant activity, thereby enhancing potential health benefits. These findings underscore the transformative effects of microbial activity on the substrate's chemical landscape and highlight the potential of tailored fermentation strategies to enhance the utility and value of underutilized fruits in sustainable agricultural practices. [ABSTRACT FROM AUTHOR]