Radiographic Disclosure of Fournier’s Gangrene

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CoMP Enhanced Subcarrier and Power Allocation for Multi-Numerology based 5G-NR Networks

With proliferation of fifth generation (5G) new radio (NR) technology, it is expected to meet the requirement of diverse traffic demands. We have designed a coordinated multi-point (CoMP) enhanced flexible multi-numerology (MN) for 5G-NR networks to improve the network performance in terms of throughput and latency. We have proposed a CoMP enhanced joint subcarrier and power allocation (CESP) scheme which aims at maximizing sum rate under the considerations of transmit power limitation and guaranteed quality-of-service (QoS) including throughput and latency restrictions. By employing difference of two concave functions (D.C.) approximation and abstract Lagrangian duality method, we theoretically transform the original non-convex nonlinear problem into a solvable maximization problem. Moreover, the convergence of our proposed CESP algorithm with D.C. approximation is analytically derived with proofs, and is further validated via numerical results. Simulation results demonstrated that our proposed CESP algorithm outperforms the conventional non-CoMP and single numerology mechanisms along with other existing benchmarks in terms of lower latency and higher throughput under the scenarios of uniform and edge users

Intraluminal duodenal diverticulum in a child concomitant with an entrapped coin and a duodenal polyp

SummaryIntraluminal duodenal diverticulum (IDD) is a rare congenital anomaly. We present the report of an 8-year-old girl who had an entrapped coin in an IDD for 3 years that was associated with recurrent pancreatitis. Besides, a duodenoduodenal intussusception was found during the course of investigation and it seemed that a concomitant duodenal polyp contributed to the development of the intussusception. In view of the rarity of each of the aforementioned situations and the improbability of these conditions occurring together, this unusual and possibly unique case is therefore reported here

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties

Numerical Analysis on a Perforated Muffler Applied in the Discharge Chamber of a Twin Screw Refrigeration Compressor Based on Fluid-Acoustic Coupling Method

The twin screw compressor has been widely used in the refrigeration systems due to advantages such as compact structure, stable operation, high efficiency and good adaptability. Intermittent gas flow generates gas pulsation that cause serious problems such as structural vibration and noise in the twin screw refrigeration compressor. Because the mechanical noise can be controlled well with the improvement of machining and assembly accuracy, the aerodynamic noise induced by gas pulsation even has become the main noise source of the twin screw refrigeration compressor. In order to reduce the pressure pulsation, a broadband perforated panel muffler applied in the discharge chamber of the twin screw refrigeration compressor is proposed based on the noise spectrum and flow characteristics of the compressor. In order to obtain the noise spectrum of the twin screw refrigeration compressor, the pressure fluctuation in discharge chamber based on a three-dimensional CFD simulation model is calculated, and the acoustical model is established based on fluid-acoustic coupling method. Then the impacts of different structural parameters on the performance of a perforated panel muffler are investigated, including perforation rate, perforation diameter and panel thickness. Through the optimization of the perforated muffler, a better reduction effect of broadband noise can be achieved. Results of fluid-acoustic coupled analysis can provide guidance on the design and optimization of the perforated muffler and noise reduction of the twin screw refrigeration compressor

Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer

Style transfer of polyphonic music recordings is a challenging task when considering the modeling of diverse, imaginative, and reasonable music pieces in the style different from their original one. To achieve this, learning stable multi-modal representations for both domain-variant (i.e., style) and domain-invariant (i.e., content) information of music in an unsupervised manner is critical. In this paper, we propose an unsupervised music style transfer method without the need for parallel data. Besides, to characterize the multi-modal distribution of music pieces, we employ the Multi-modal Unsupervised Image-to-Image Translation (MUNIT) framework in the proposed system. This allows one to generate diverse outputs from the learned latent distributions representing contents and styles. Moreover, to better capture the granularity of sound, such as the perceptual dimensions of timbre and the nuance in instrument-specific performance, cognitively plausible features including mel-frequency cepstral coefficients (MFCC), spectral difference, and spectral envelope, are combined with the widely-used mel-spectrogram into a timber-enhanced multi-channel input representation. The Relativistic average Generative Adversarial Networks (RaGAN) is also utilized to achieve fast convergence and high stability. We conduct experiments on bilateral style transfer tasks among three different genres, namely piano solo, guitar solo, and string quartet. Results demonstrate the advantages of the proposed method in music style transfer with improved sound quality and in allowing users to manipulate the output

Evaluating the implementation of EMR systems from the perspective of health professionals

In health informatics, the updated DeLone and McLean IS success model is reviewed as a mature model in measuring health information system (HIS) success. This research provided an evaluation model to estimate the implementation of electronic medical records (EMR) systems from a health professional perspective by combined the updated DeLone and McLean IS success model, data quality management model, and EMR systems safety attributes. Based on evidence-based management (EBM), this research could be regarded as an empirical example for further EMR systems research since it not only provided a model to measure the Taiwanese EMR systems in two hospitals by implementing a structure instrument and structure equation modeling (SEM) of quantitative methods, but also introduced how to identify the possible effects in such evaluation research