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The Indian meal moth, Plodia interpunctella, poses a significant threat to global agricultural products. Although pheromone-based technologies show promise for P. interpunctella management, limitations such as single-sex targeting necessitate exploring complementary strategies. Vegetable-based oils represent a potential alternative but their efficacy, sex-specificity, and interaction with sex pheromones remain unclear. To address these questions, we first examined the attraction of P. interpunctella female and male adults to 10 commercially available vegetable oils in semi-field conditions. Sesame, olive, and blended oils were the most attractive, capturing significantly more adults compared to other oils. We then evaluated the effectiveness of these three attractive oils and their combinations with P. interpunctella sex pheromones in a grain warehouse. Traps baited with these oils captured significantly more females and males compared to control traps without attractants; however, the addition of sex pheromones did not improve male capture and significantly reduced female capture, suggesting an inhibitory effect. Finally, we demonstrated that female mating status (mated vs. virgin) did not influence the attractiveness of traps baited with these effective oils. Our findings highlight the potential of standalone traps baited with blended, olive, or sesame oil as a practical, economic, and effective management strategy for P. interpunctella in storage facilities. [ABSTRACT FROM AUTHOR]

In order to cope with ever-evolving and increasing cyber threats, intrusion detection systems have become a crucial component of cyber security. Compared with signature-based intrusion detection methods, anomaly-based methods typically employ machine learning techniques to train detection models and possess the capability to discover unknown attacks. However, intrusion detection methods face the challenge of low detection rates for minority class attacks due to imbalanced data distributions. Traditional intrusion detection algorithms address this issue by resampling or generating synthetic data. Additionally, reinforcement learning, as a machine learning method that interacts with the environment to obtain feedback and improve performance, is gradually being considered for application in the field of intrusion detection. This paper proposes a reinforcement-learning-based intrusion detection method that innovatively uses adaptive sample distribution dual-experience replay to enhance a reinforcement learning algorithm, aiming to effectively address the issue of imbalanced sample distribution. We have also developed a reinforcement learning environment specifically designed for intrusion detection tasks. Experimental results demonstrate that the proposed model achieves favorable performance on the NSL-KDD, AWID, and CICIoT2023 datasets, effectively dealing with imbalanced data and showing better classification performance in detecting minority attacks. [ABSTRACT FROM AUTHOR]

Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs. [ABSTRACT FROM AUTHOR]

Predicting which software modules are fault-prone, and doing so early enough in a project life cycle to be useful to developers, is investigated and discussed.

The metal-semiconductor direct current triboelectric nanogenerator (MSDC-TENG) has attracted increasing attention due to its power density, which is 102−103 times higher than traditional generators. A P-type GaN based MSDC-TENG with a tin electrode is reported in this paper. By adjusting the Mg2+ doping concentration of the P-GaN, it can achieve an open circuit voltage (Voc) of 14.2 V and a short circuit current (Isc) density of ∼25.5 A/m2. This Isc is about 50 times higher than the performance of the Cu/GaN Triboelectric Nanogenerator, and the power density increases by more than 120 times. Under the experimental loading conditions, we also obtained the maximum load current density and the maximum load power of 12.8 and 102.4 W/m2, respectively. This power density is sufficient to drive small electronic circuits. Our results also provide useful insights into the carrier concentration-Isc relation in the Cu/GaN triboelectric nanogenerator. [ABSTRACT FROM AUTHOR]