作者:
Gierdziewicz, MaciejChair of Applied Computer Science
Faculty of Automation Electrical Engineering Computer Science and Biomedical Engineering AGH University of Science and Technology Al. Mickiewicza 30 Cracow30-059 Poland
In this paper the generation of a geometrical model for simulation of the transport process inside the bouton of the biological neuron is considered. The transport is modeled in three-dimensional space by using nonlin...
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The transverse momentum per particle, [pt], fluctuates event by event in ultrarelativistic nucleus-nucleus collisions, for a given multiplicity. These fluctuations are small and approximately Gaussian, but a nonzero s...
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The transverse momentum per particle, [pt], fluctuates event by event in ultrarelativistic nucleus-nucleus collisions, for a given multiplicity. These fluctuations are small and approximately Gaussian, but a nonzero skewness has been predicted on the basis of hydrodynamic calculations, and seen experimentally. We argue that the mechanism driving the skewness is that, if the system thermalizes, the mean transverse momentum increases with impact parameter for a fixed collision multiplicity. We postulate that fluctuations are Gaussian at fixed impact parameter, and that non-Gaussianities solely result from impact parameter fluctuations. Using recent data on the variance of [pt] fluctuations, we make quantitative predictions for their skewness and kurtosis as a function of the collision multiplicity. We predict, in particular, a spectacular increase of the skewness below the knee of the multiplicity distribution, followed by a fast decrease.
The experimental realisation of unconventional superconductivity and charge order in kagome systems AV3Sb5 is of critical importance. We conducted a highly systematic study of Cs(V1−xNbx)3Sb5 with x=0.07 (Nb0.07-CVS) ...
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Normal-state transport properties (2–300 K) of the polycrystalline series Sn1.03−δ−xInxTe (0≤x≤0.07; δ≤0.0025) were investigated by means of electrical resistivity, thermopower, Hall effect, and thermal conducti...
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Normal-state transport properties (2–300 K) of the polycrystalline series Sn1.03−δ−xInxTe (0≤x≤0.07; δ≤0.0025) were investigated by means of electrical resistivity, thermopower, Hall effect, and thermal conductivity measurements. The distortion of the valence-band structure by the In-induced resonant level (RL) has a profound influence on the evolution of the normal-state properties with x and on the emergence of superconductivity evidenced by specific-heat measurements down to 0.35 K. In addition to a nearly 40-fold increase in the residual electrical resistivity ρ0 on going from x=0.0 to 0.05, the thermopower α shows a nonlinear, complex behavior as a function of both temperature and x. While Hall measurements indicate a dominant holelike response across the entire composition and temperature ranges, α changes sign below about 100 K and remains negative down to 5 K for 0.0015≤x≤0.0045. Additional measurements under magnetic fields μ0H of up to 14 T further shows that α(μ0H) gradually shifts towards positive values, suggestive of a dominant holelike contribution to α. Superconductivity emerges for x=0.02 at a critical temperature Tc=0.67 K, with Tc increasing with x to reach 1.73 K for x=0.07. The variations in the superconducting parameters with x, notably the specific-heat jump at Tc, confirm the results reported in prior studies and suggests a nontrivial role of the RL on the electron-phonon coupling strength. The striking similarities between this series and the canonical resonant system Pb1−xTlxTe provide an excellent experimental opportunity to gain a deeper understanding of the close interplay between resonant level, anomalous transport properties, and superconductivity.
In recent years, researchers have increasingly sought batteries as an efficient and cost-effective solution for energy storage and supply, owing to their high energy density, low cost, and environmental resilience. Ho...
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The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not scalable enough to maintain stable th...
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The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not scalable enough to maintain stable throughput for an increasing number of stations, yet it remains the default method of channel access for 802.11ax single-user transmissions. Therefore, we propose a new method of CW control, which leverages deep reinforcement learning (DRL) principles to learn the correct settings under different network conditions. Our method, called centralized contention window optimization with DRL (CCOD), supports two trainable control algorithms: deep Q-network (DQN) and deep deterministic policy gradient (DDPG). We demonstrate through simulations that it offers efficiency close to optimal (even in dynamic topologies) while keeping computational cost low.
In this paper, we investigate the coherent control over a complex multilevel atomic system using the stimulated Raman adiabatic passage. Based on the example of Rb87 atoms, excited with circularly polarized light at t...
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In this paper, we investigate the coherent control over a complex multilevel atomic system using the stimulated Raman adiabatic passage. Based on the example of Rb87 atoms, excited with circularly polarized light at the D1 line, we demonstrate the ability to decompose the system into three- and four-level subsystems independently interacting with light beams. Focusing on the four-level system, we demonstrate that the presence of an additional excited state significantly affects the dynamics of the system evolution. Specifically, it is shown that, through the appropriate tuning of the light beams, some of the transfer channels can be blocked, which leads to better control over the system. We also demonstrate that this effect is most significant in media free from inhomogeneous broadening (e.g., Doppler effect) and deteriorates if such broadening is present. For instance, the motion of atoms affects both the efficiency and selectivity of the transfer.
Shake-up phenomena, rooted in the sudden approximation and many-body quantum dynamics, unveil critical characteristics of quantum systems, with wide-ranging applications in molecular spectroscopy and electronic struct...
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Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports...
Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.
Using the electrostatic analogy, we derive an exact formula for the limiting Yang-Lee zero distribution in the random allocation model of general weights. This exhibits a real-space condensation phase transition, whic...
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