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Filtering high-variability and high-severity defect reports from large test report databases is a challenging task in crowdtesting. Traditional optimization algorithms based on clustering and distance techniques have made progress but are limited by initial parameter settings and significantly decrease in efficiency with an increasing number of reports. To address this issue, this paper proposes a method that integrates reinforcement learning with genetic algorithms for crowdsourced testing report optimization, called Reinforcement Learning-based Genetic Algorithm for Crowdsourced Testing Report Optimization (RLGA). Its core goal is to identify distinct, high-severity defect reports from a large set. The method uses genetic algorithms to generate the optimal report selection sequence and adjusts the crossover probability (Pc) and mutation probability (Pm) dynamically with reinforcement learning based on the population's average fitness, best fitness, and diversity. The reinforcement learning component uses a hybrid SARSA and Q-Learning strategy to update the Q-value table, allowing the algorithm to learn quickly in early iterations and expand the search space later to avoid local optima, thereby improving efficiency. To validate the RLGA method, this paper uses four public datasets and compares RLGA with six classic methods. The results indicate that RLGA outperforms BDDIV in terms of execution time and is less sensitive to the total number of test reports. In terms of optimization objectives, the test reports selected by RLGA have higher levels of defect severity and diversity than those selected by the random choice, BDDIV, and TSE methods. Regarding population diversity, RLGA effectively enhances the uniformity and diversity of individuals compared to random initialization. In terms of convergence speed, RLGA is superior to the GA, GA-SARSA, and GA-Q methods. [ABSTRACT FROM AUTHOR]

In this article, the impact of 20 MeV proton irradiation on NiOx/β-Ga2O3 p–n diodes has been investigated. After 20 MeV proton irradiation with a fluence of 2 × 1012 p/cm2, the forward current density (JF) decreased by 44.1% from 93.0 to 52.0 A/cm2, and the turn-on voltage (Von) increased from 1.55 to 1.68 V based on current–voltage (I–V) measurements. Moreover, the capacitance–voltage (C–V) measurements indicated that the net carrier concentration in the β-Ga2O3 lightly doped drift region was reduced from 1.95 × 1016 to 1.86 × 1016 cm−3 after proton irradiation. The effect of proton irradiation on NiOx/β-Ga2O3 interface trap states was also acquired utilizing the frequency-dependent conductance technique. The results indicated that the time constant increased from 0.04–0.15 to 0.13–0.23 μs after proton irradiation. Meanwhile, it was found that the density of interface trap states increases from 7.49 × 1010–1.27 × 1010 to 7.23 × 1011–1.70 × 1012 cm−2 eV−1 with an increase in trap activation energy from 0.080–0.111 to 0.088–0.121 eV after proton irradiation. This work provides an important reference for further improving the performance of NiOx/β-Ga2O3 p–n diodes through the design of subsequent anti-radiation hardening. [ABSTRACT FROM AUTHOR]

Compared to conventional condensation dehumidification systems, a solar liquid desiccant air conditioning system (SLDAC) offers distinct advantages, enabling independent control of temperature and humidity while operating at low carbon levels. This paper proposes a low-carbon SLDAC thermal mass exchange model that uses an indirect evaporative cooling liquid dehumidifier and photovoltaic thermal and air source heat pump for combined driving of the solution regeneration process. The system simulation model was created using MATLAB and TRNSYS software. An experimental comparison was conducted between the dehumidification module and the regeneration module, and the system's performance was simulated with an airflow of 150 m3/h in the Tianjin area. The results indicate an average dehumidification efficiency of 44.03% and a regeneration efficiency of 40.80% throughout the cooling season. During the cooling season, the power generation of the system met the power demand and produced a surplus of 85.36 kWh, achieving overall self-sufficiency in power consumption. The optimized system's operating parameters were proposed, with regeneration temperatures of 57 °C in June and September and 65 °C in July and August. During the cooling season, the system's regeneration capacity exceeded its dehumidification capacity for 84.90% of the operating time. As a result of optimization, the system's carbon emissions were reduced by 59.35%. [ABSTRACT FROM AUTHOR]

An antenna‐filter‐antenna (AFA) based frequency selective surface (FSS) with excellent spatial filtering performance is proposed. This FSS design incorporates two back‐to‐back microstrip patch antennas, which not only ensures a high roll‐off rate at the passband edge and excellent flatness within the passband, but also achieves an ultra‐low profile. Significantly, the upper and lower band edges demonstrate a substantial drop from −3 to −10 dB within the 0.006 and 0.02 GHz ranges, respectively, resulting in a sharp roll‐off skirt. Furthermore, the frequency of radiation null can be controlled by manipulating the length of the quarter‐wavelength open‐ended stub, thereby possessing exceptional out‐of‐band suppression. A prototype of the proposed FSS with low profile is fabricated and measured. The simulations and measurements of the AFA‐based FSS show that a quasi‐elliptical bandpass filtering response with superior performance is achieved. [ABSTRACT FROM AUTHOR]

In this article, the effects of high-energy proton irradiation on top-gate graphene field-effect transistors (GFETs) were investigated by using 20 MeV protons. The basic electrical parameters of the top-gate GFETs were measured before and after proton irradiation with a fluence of 1 × 1011 p/cm2 and 5 × 1011 p/cm2, respectively. Decreased saturation current, increased Dirac sheet resistance, and negative drift in the Dirac voltage in response to proton irradiation were observed. According to the transfer characteristic curves, it was found that the carrier mobility was reduced after proton irradiation. The analysis suggests that proton irradiation generates a large net positive charge in the gate oxide layer, which induces a negative drift in the Dirac voltage. Introducing defects and increased impurities at the gate oxide/graphene interface after proton irradiation resulted in enhanced Coulomb scattering and reduced mobility of the carriers, which in turn affects the Dirac sheet resistance and saturation current. After annealing at room temperature, the electrical characteristics of the devices were partially restored. The results of the technical computer-aided design (TCAD) simulation indicate that the reduction in carrier mobility is the main reason for the degradation of the electrical performance of the device. Monte Carlo simulations were conducted to determine the ionization and nonionization energy losses induced by proton incidence in top-gate GFET devices. The simulation data show that the ionization energy loss is the primary cause of the degradation of the electrical performance. [ABSTRACT FROM AUTHOR]