Notes

Metabolic rate regarding Photosynthetic Microorganisms.
The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.Light beams carrying Orbital Angular Momentum (OAM), also known as optical vortices (OV), have led to fascinating new developments in fields ranging from quantum communication to novel light-matter interaction aspects. Even though several techniques have emerged to synthesize these structured-beams, their detection, in particular, single-shot amplitude, wavefront, and modal content characterization, remains a challenging task. Here, we report the single-shot amplitude, wavefront, and modal content characterization of ultrashort OV using a Shack-Hartmann wavefront sensor. These vortex beams are obtained using spiral phase plates (SPPs) that are frequently used for high-intensity applications. The reconstructed wavefronts display a helical structure compatible with the topological charge induced by the SPPs. We affirm the accuracy of the optical field reconstruction by the wavefront sensor through an excellent agreement between the numerically backpropagated and experimentally obtained intensity distribution at the waist. A939572 SCD inhibitor Consequently, through Laguerre-Gauss (LG) decomposition of the reconstructed fields, we reveal the radial and azimuthal mode composition of vortex beams under different conditions. The potential of our method is further illustrated by characterizing asymmetric Gaussian vortices carrying fractional average OAM, and a realtime topological charge measurement at a 10Hz repetition rate. These results can promote Shack-Hartmann wavefront sensing as a single-shot OV characterization tool.Patient similarity research is one of the most fundamental tasks in healthcare, helping to make decisions without incurring additional time and costs in clinical practices. Patient similarity can also apply to various medical fields, such as cohort analysis and personalized treatment recommendations. Because of this importance, patient similarity measurement studies are actively being conducted. However, medical data have complex, irregular, and sequential characteristics, making it challenging to measure similarity. Therefore, measuring accurate similarity is a significant problem. Existing similarity measurement studies use supervised learning to calculate the similarity between patients, with similarity measurement studies conducted only on one specific disease. However, it is not realistic to consider only one kind of disease, because other conditions usually accompany it; a study to measure similarity with multiple diseases is needed. This research proposes a convolution neural network-based model that jointly combines feature learning and similarity learning to define similarity in patients with multiple diseases. We used the cohort data from the National Health Insurance Sharing Service of Korea for the experiment. Experimental results verify that the proposed model has outstanding performance when compared to other existing models for measuring multiple-disease patient similarity.Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85-93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.Ensuring a production-ready state of the application under development is the imminent feature of the Continuous Delivery (CD) approach. In a blockchain network, nodes communicate and store data in a distributed manner. Each node executes the same business application but operates in a distinct execution environment. The literature lacks research focusing on continuous practices for blockchain and Distributed Ledger Technology (DLT). Specifically, it lacks such works with support for both design and deployment. The author has proposed a solution that takes into account the continuous delivery of a business application to diverse deployment environments in the DLT network. As a result, two continuous delivery pipelines have been implemented using the Jenkins automation server. The first pipeline prepares a business application whereas the second one generates complete node deployment packages. As a result, the framework ensures the deployment package in the actual version of the business application with the node-specific up-to-date version of deployment configuration files. The Smart Contract Design Pattern has been used when building a business application. The modeling aspect of blockchain network installation has required using Unified Modeling Language (UML) and the UML Profile for Distributed Ledger Deployment. The refined model-to-code transformation generates deployment configurations for nodes. Both the business application and deployment configurations are stored in the GitHub repositories. For the sake of verification, tests have been conducted for the electricity consumption and supply management system designed for prosumers of renewable energy.We propose an imaging method based on optical fiber bundle combined with micro-scanning technique for improving image quality without complex image reconstruction algorithms. In the proposed method, a piezoelectric-ceramic-chip is used as the micro-displacement driver of the optical fiber bundle, which has the advantages of small volume, fast response speed and high precision. The corresponding displacement of the optical fiber bundle can be generated by precise voltage controlling. An optical fiber bundle with core/cladding diameter 4/80 μm and hexagonal arrangement is used to scan the 1951 USAF target. The scanning step is 1 μm, which is equivalent to the diffraction limit resolution of the optical system. The corresponding information is recorded at high speed through photo-detectors and a high-resolution image is obtained by image stitching processing. The minimum distinguishable stripe width of the proposed imaging technique with piezoelectric-ceramic-chip driven micro-scanning is approximately 2.1 μm, which is 1 time higher than that of direct imaging with a CCD camera whose pixel size is close to the fiber core size. The experimental results indicate that the optical fiber bundle combined with piezoelectric-ceramic-chip driven micro-scanning is a high-speed and high-precision technique for high-resolution imaging.The rapid expansion of a country's economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system's IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system's infrastructure is far too low-cost and easy to install.This paper exhibits a high-gain, low-profile dipole antenna array (DAA) for 5G applications. The dipole element has a semi-triangular shape to realize a simple input impedance regime. To reduce the overall antenna size, a substrate integrated cavity (SIC) is adopted as a power splitter feeding network. The transition between the SIC and the antenna element is achieved by a grounded coplanar waveguide (GCPW) to increase the degree of freedom of impedance matching. Epsilon-near-zero (ENZ) metamaterial technique is exploited for gain enhancement. The ENZ metamaterial unit cells of meander shape are placed in front of each dipole perpendicularly to guide the radiated power into the broadside direction. The prospective antenna has an overall size of 2.58 λg3 and operates from 28.5 GHz up to 30.5 GHz. The gain is improved by 5 dB compared to that of the antenna without ENZ unit cells, reaching 11 dBi at the center frequency of 29.5 GHz. Measured and simulated results show a reasonable agreement.Advances in technology provide an opportunity to enhance the accuracy of gait and balance assessment, improving the diagnosis and rehabilitation processes for people with acute or chronic health conditions. This study investigated the validity and reliability of a smartphone-based application to measure postural stability and spatiotemporal aspects of gait during four static balance and two gait tasks. Thirty healthy participants (aged 20-69 years) performed the following tasks (1) standing on a firm surface with eyes opened, (2) standing on a firm surface with eyes closed, (3) standing on a compliant surface with eyes open, (4) standing on a compliant surface with eyes closed, (5) walking in a straight line, and (6) walking in a straight line while turning their head from side to side. During these tasks, the app quantified the participants' postural stability and spatiotemporal gait parameters. The concurrent validity of the smartphone app with respect to a 3D motion capture system was evaluated using partial Pearson's correlations (rp) and limits of the agreement (LoA%).
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