This study investigates the transient magnetohydrodynamic (MHD) flow and heat and mass transfer over an inclined, radiative, and permeable surface under the influence of viscous dissipation and Ohmic heating in a poro...
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This study investigates the transient magnetohydrodynamic (MHD) flow and heat and mass transfer over an inclined, radiative, and permeable surface under the influence of viscous dissipation and Ohmic heating in a porous medium. A regular perturbation technique is employed to solve the governing coupled, nonlinear partial differential equations that describe the velocity, temperature, and concentration fields. The analysis focuses on key physical parameters, including the skin friction coefficient, Nusselt number, and Sherwood number, along with the corresponding field profiles. The results highlight the significant impact of inclination angle, porosity, and thermal radiation on the transport behavior. Specifically, thermal radiation is found to reduce both the temperature distribution and the Nusselt number, indicating a damping effect on thermal transport. Conversely, the presence of a heat source elevates the temperature while enhancing heat transfer. Additionally, the Soret number contributes to an increase in both temperature and solute concentration due to thermal diffusion effects. The findings correspond well with previously published studies. In other words, when the inclined angle, porous parameter, and radiation parameter are not considered, all outcomes closely resemble those of the previously published works. This study offers valuable insights applicable to various industrial processes, including chemical manufacturing and thermal management systems.
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is r...
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.
Printed electronics is a rapidly emerging field due to the development of new types of functional materials and inks that can be deposited using well-known and widely established printing techniques. Commonly, sensor ...
Printed electronics is a rapidly emerging field due to the development of new types of functional materials and inks that can be deposited using well-known and widely established printing techniques. Commonly, sensor structures or interconnects on printed circuit boards (PCBs) are fabricated using wet etching and photolithography steps. To overcome the limitations of those techniques, in this contribution, we use a simple and scalable screen printing process to deposit silver-flake based interdigitated electrode (IDE) structures onto sprayed carbon nanotube (CNT) films. The silver (Ag) structures show a low sheet resistance and resistivity of 0.14 Ω/sq. and 6.1·10 -7 Ω·m, respectively, at a thickness of around 4.4 μm. Attributed to its flexibility and robustness, a polyimide foil (Kapton®HN) was selected as the substrate. The CNT films with printed IDE that are entirely fabricated in the ambient air are then characterized as resistive gas sensors to detect ammonia (NH 3 ). A high response of around 20% is achieved for an NH 3 concentration of 50 ppm.
The knee joint is the largest and one of the most vulnerable and most frequently damaged joints in the human body. It is characterized by a complex structure. All articular surfaces are covered with hyaline cartilage....
The knee joint is the largest and one of the most vulnerable and most frequently damaged joints in the human body. It is characterized by a complex structure. All articular surfaces are covered with hyaline cartilage. This cartilage has minimal regenerative capacity. Under the influence of cyclical micro-injuries, inflammatory mediators, prolonged excessive pressure or immobility, and thus disturbance of tissue nutrition, the cartilage becomes susceptible to damage and is easily covered with villi, cracks and abrasion. As a result, this translates into changes in the friction and lubrication processes within the joint and may affect the generated vibroacoustic processes. In this study, the signals recorded in a group of 28 volunteers were analysed, 15 of them were healthy people (HC) and 13 were people diagnosed with osteoarthritis (OA) qualified for surgery. The study aims to check the usefulness of the EMD (Empirical Mode Decomposition) algorithm in the filtration procedures of vibroacoustic signals. This algorithm is most often used in the analysis of signals that are most often nonlinear and non-stationary. Selected statistical indicators, such as RMS, VMS, variance and energy, were determined for the signals constituting the sum of the IMFs (Intrinsic Mode Functions) 1-8, having a normal distribution in the assessment of damage to the articular cartilage of the knee joint. Statistical analysis was performed for the values of individual indicators obtained. The vibroacoustic signals were recorded using CM-01B contact microphones placed in the central part of the medial and lateral joint fissure for movement in the range of 90°–0°–90° in closed kinetic chains (CKC) in the control group (HC) and the group of patients diagnosed with osteoarthritis (OA).
Osteoarthritis (OA) is currently the most generic form of joint disease. It is a complex process in which degenerative changes occur in the articular cartilage [AC], subchondral bone, and synovial membrane and can lea...
Osteoarthritis (OA) is currently the most generic form of joint disease. It is a complex process in which degenerative changes occur in the articular cartilage [AC], subchondral bone, and synovial membrane and can lead to permanent joint failure. The primary and most commonly used method of diagnosing degenerative changes is classic radiography. Magnetic resonance imaging (MRI) may be used to assess the extent of damage to joint surfaces, but this method is limited by the availability of specialised equipment and the excessive cost of the examination. Arthroscopy, an invasive procedure, is considered the "gold standard" in joint diagnosis. The occurrence of degenerative changes is closely related to the friction and lubrication processes within the joint. The main causes of osteoarthritis are a change or lack of synovial fluid, deformation of the joint bones, local damage to the articular cartilage, and a change in the mechanical properties of the articular cartilage due to water loss from the damaged superficial layer. An alternative, non-invasive method that allows for a delicate assessment of the condition of moving joints is vibroarthrography (VAG). The analysis of vibroacoustic signals generated by moving joint surfaces has an immense potential in the non-invasive assessment of the degree of damage to articular cartilage, meniscus and ligaments and the general diagnosis of degenerative diseases. The purpose of this study is to analyse and statistically compare the basic characteristics of vibroacoustic signals recorded with a CM-01B contact microphone placed on the patella for motion in the 90°–0°–90° range in a closed kinetic chain (CKC) in a control group (HC) and a group of patients diagnosed with osteoarthritis (OA), qualified for the knee alloplasty.
In this work we propose a concept of a transistor level implementation of a simplified iterative methods for computing several basic statistical quantities, such as the mean and the variance. The motivation behind the...
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In this work we propose a concept of a transistor level implementation of a simplified iterative methods for computing several basic statistical quantities, such as the mean and the variance. The motivation behind the presented work is the realization of a calibration algorithm for determining the positions of the V2I (vehicle-to-infrastructure) communication devices in novel automotive applications. Such devices, mounted in fixed points of the road and urban infrastructure (RSU - road side equipment) will be used to support autonomous vehicles moving in urban and suburban environments. The role of the calibration procedure is to determine the positions of the RSU devices in global coordinate system (GCS) and save it in their internal memory blocks. To facilitate the hardware implementation, we introduced some modifications to existing (conventional) iterative algorithms used for the computation of the statistical quantities. For this purpose, we eliminated division operations, substituting them with bit shift operations. Shifting the bits may be easily realized fully asynchronously in hardware, using only a passive commutation field.
Machine learning becomes one of the top fields in world of ICT (Information and communications technology). In the last few decades machine learning has taken a big boost in sports. Sport fans are using machine learni...
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The fourth industrial revolution is predicted to be one major topic in the upcoming decades, affecting nearly every industrial facility. The key enabler for the successful integration of corresponding developments are...
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The paper presents novel solutions for systems used to create air pollutions maps in smart cities. Ability to record pollution levels with a function of a short-term prediction of their fluctuations may be useful for ...
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ISBN:
(纸本)9781538623718
The paper presents novel solutions for systems used to create air pollutions maps in smart cities. Ability to record pollution levels with a function of a short-term prediction of their fluctuations may be useful for cyclists and pedestrians moving through the city. Based on such data they can choose their route through the city in such a way, as to avoid the most polluted areas. Systems of this type are in the range of solutions characteristic for smart cities. Their effectiveness requires a relatively dense wireless sensor network (WSN) composed of miniaturized and cheap intelligent pollution sensors, capable not only of data recording and transmitting, but also of some data processing with the prediction abilities. Sensors of this type require a development of various circuit components that feature small sizes and ultra-low energy consumption. One of the main blocks, in this case, should be an artificial neural network (ANN) implemented at the transistor level. In this work, we present prototype circuits designed by us for the described purposes. The realized blocks include a finite impulse response (FIR) filter, programmable analog-to-digital converters (ADCs) with internal controlling clock generators and main building blocks of a parallel ANN. The specialized chips (ASIC - application specific integrated circuit) with the described components were implemented in the CMOS technology in the full custom style.
Time-varying metasurfaces are emerging as a powerful instrument for the dynamical control of the electromagnetic properties of a propagating wave. Here we demonstrate an efficient time-varying metasurface based on pla...
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Time-varying metasurfaces are emerging as a powerful instrument for the dynamical control of the electromagnetic properties of a propagating wave. Here we demonstrate an efficient time-varying metasurface based on plasmonic nano-antennas strongly coupled to an epsilon-near-zero (ENZ) deeply subwavelength film. The plasmonic resonance of the metal resonators strongly interacts with the optical ENZ modes, providing a Rabi level spitting of ∼30%. Optical pumping at frequency ω induces a nonlinear polarization oscillating at 2ω responsible for an efficient generation of a phase conjugate and a negative refracted beam with a conversion efficiency that is more than 4 orders of magnitude greater compared to the bare ENZ film. The introduction of a strongly coupled plasmonic system therefore provides a simple and effective route towards the implementation of ENZ physics at the nanoscale.
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