Purpose-The purpose of this study is to identify innovation models in the judiciary according to the current integrated theoretical approach for innovation in services. Design/methodology/approach-This study uses a quantitative approach. The authors collected the data through a questionnaire sent to labor court public servants and judges in a Regional Labor Court in the Midwestern region of Brazil. They performed a principal component analysis to identify the factors to map the innovation models present in the court. Findings-Two factors were obtained from the results, which describe innovations in processes and services in the court studied. In terms of the examples of innovations cited by the respondents, one may note that those related to information and communications technology are the most remembered, especially the introduction of the electronic lawsuit. Originality/value-The results can contribute toward a deeper understanding of which vectors of service innovation are affected as well as the nature of the court's underlying structure. Also, the research instrument used allows the identification and analysis of the innovation model for services and thus contributes to its validation.
The propagation of light in nonlinear optical media has been widely used as a tabletop platform for emulating quantum-like phenomena due to their similar theoretical description to quantum fluids. These fluids of light are often used to study 2-dimensional phenomena involving superfluid-like flows, yet turbulent regimes still remain underexplored. In this work, we study the possibility of creating 2-dimensional turbulent phenomena and probing their signatures in the kinetic energy spectrum. To that end, we emulate and disturb a fluid of light with an all-optical defect using the propagation of two beams in a photorefractive crystal. Our experimental results show that the superfluid regime of the fluid of light breaks down at a critical velocity at which the defect starts to exert a drag force on the fluid, in accordance with the theoretical and numerical predictions. Furthermore, in this dissipative regime, nonlinear perturbations are excited on the fluid that can decay into vortex structures and thus precede a turbulent state. Using the off-axis digital holographic method, we reconstructed the complex description of the output fluids and calculated the incompressible component of the kinetic energy. With these states, we observed the expected power law that characterizes the generated turbulent vortex dipole structures. The findings enclosed in this manuscript align with the theoretical predictions for the vortex structures of 2-dimensional quantum fluids and thus may pave the way to the observation of other distinct hallmarks of turbulent phenomena, such as distinct turbulent regimes and their associated power laws and energy cascades.
Optical trapping provides a way to isolate, manipulate, and probe a wide range of microscopic particles. Moreover, as particle dynamics are strongly affected by their shape and composition, optical tweezers can also be used to identify and classify particles, paving the way for multiple applications such as intelligent microfluidic devices for personalized medicine purposes, or integrated sensing for bioengineering. In this work, we explore the possibility of using properties of the forward scattered radiation of the optical trapping beam to analyze properties of the trapped specimen and deploy an autonomous classification algorithm. For this purpose, we process the signal in the Fourier domain and apply a dimensionality reduction technique using UMAP algorithms, before using the reduced number of features to feed standard machine learning algorithms such as K-nearest neighbors or random forests. Using a stratified 5-fold cross-validation procedure, our results show that the implemented classification strategy allows the identification of particle material with accuracies up to 80%, demonstrating the potential of using signal processing techniques to probe properties of optical trapped particles based on the forward scattered light. Furthermore, preliminary results of an autonomous implementation in a standard experimental optical tweezers setup show similar differentiation capabilities for real-time applications, thus opening some opportunities towards technological applications such as intelligent microfluidic devices and solutions for biochemical and biophysical sensing.
Optical trapping is a versatile and non-invasive technique for single particle manipulation. As such, it can be widely applied in the domains of particle identification and classification and thus used as a tool for monitoring physical and chemical processes. This creates an opportunity for integrating the method seamlessly into optofluidic chips, provided it can be automatized. Yet even though OT is well established in multiple scientific domains, a full stack approach to its integration into other technological devices is still lacking. This calls for solutions in tasks such as automatic trapping and signal analysis. In this manuscript, we describe the implementation of an algorithm seeking autonomous particle location and trapping. The methodology is based upon image-processing, allowing for particle location using real time image segmentation. A local thresholding algorithm is applied, followed by morphological techniques for closing shapes and excluding non-bounded regions - after which only the particles remain on the image. Once the centroid is identified, the stage is translated accordingly by piezo-electric actuators, followed by the laser activation. In this way, trapping is achieved, and one may proceed to analyze the forward scattered optical signal, after which a new particle inside the actuators range may be automatically trapped. This development, when compared with existent solutions involving holographic optical tweezers, allows for similar capabilities without using a spatial light modulator, thus dramatically reducing the setup costs of autonomous OT solutions. Therefore, when combined with particle classification techniques, this method is well suited for integration into possible optofluidic chips for autonomous sensing and monitoring of biochemical samples.
To automatically trap, manipulate and probe physical properties of micron-sized particles is a step of paramount importance for the development of intelligent and integrated optomicrofluidic devices. In this work, we aim at implementing an automatic classifier of micro-particles immersed in a fluid based on the concept of optical tweezers. We describe the automation steps of an experimental setup together with the implemented classification models using the forward scattered signal. The results show satisfactory accuracy around 80% for the identification of the type and size of particles using signals of 250 milliseconds of duration, which paves the path for future improvements towards real-time analysis of the trapped specimens.
Optical tweezers use light to trap and manipulate mesoscopic scaled particles with high precision making them a useful tool in a plethora of natural sciences, with emphasis on biological applications. In principle, the Brownian-like dynamics reflect trapped particle properties making it a robust source of information. In this work, we exploit this information by plotting histogram based images of 250ms of position or displacement used as input to a Convolution Neural Network. Results of 2-fold stratified cross-validation show satisfying classifications between sizes or types of particles: Polystyrene and Polymethilmethacrylate thus highlighting the potential of CNN approaches in faster and non-invasive applications in intelligent opto and microfluidic devices using optical trapping tools.
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