Litchi is popular among consumers due to its delicious taste. However, the extremely short shelf life limits its commercial value. Conventional single-gap dielectric barrier discharge (SG-DBD) treatment may provide non-thermal microbial control, but the curved geometry of spherical fruit can distort the electric-field distribution and lead to non-uniform plasma exposure. In this study, an innovative double-gap dielectric barrier discharge (DG-DBD) formed with a 3D-printed frame was introduced to improve the electric field uniformity over spherical loads. Its effectiveness was supported by electrostatic modeling electrical characterization, and preservation experiment on the spherical fresh produce like litchi. The DG configuration outperformed the single-gap (SG) configuration for decay, browning, and control of yeasts and molds. And fruit spacings of 1-2 cm produced more favorable preservation outcomes than close-packed placement. These results indicate that geometry-aware DBD treatment can improve plasma exposure characteristics and may enhance the postharvest preservation of spherical fresh produce.
Kilonovae, or the electromagnetic signals that emerge from the ejecta of black hole-neutron star (NS) or NS-NS mergers, are expected to be powered by nuclear decays, since the ejecta are promising sites for the nucleosynthesis of heavy, unstable elements. One of the earliest models for the luminosity curves of kilonovae is revisited here, incorporating modifications that include the implementation of a nuclear reaction network with 6757 nuclei and 78,910 reaction rates where 1439 are α decays and 4861 are β− decays of which 938 and 4669 are relevant ranging from (Z=50, A=102) to (Z=110, A=281) and (Z=0, A=1) to (Z=110, A=373) respectively. Just 256 and 1041 are experimental α and β− decay rates, respectively, indicating a strong dependence of heating rates on theoretical decay rates. With the data complemented by theoretical rates for the cases where data are not available, it has been found that the heating rate, ε˙(t), shows deviations over time from the commonly assumed power-law behavior, ε˙(t)∝t−x, where x typically ranges between 1 and 1.4. Small fluctuations from the power-law behavior of ε˙(t) can lead to bumps in the luminosity curves at specific times. The latter can be related to the decays of a few nuclear species that contribute significantly at late times. Comparison of decay rates from empirical formulae and models, and the sensitivity of the heating rates and luminosity curves to these inputs, is presented. Sensitivity of the heating rate results to trajectories with different initial thermodynamical conditions is tested with an ensemble of 120 trajectories.
This qualitative study explains how airline cabin crew reconcile a service identity with safety enforcement during in-flight disturbances. Using a grounded theory design, we conducted two in-depth interviews with thirty female crew members at a South Korean full-service carrier. We analyzed transcripts via open, axial, and selective coding until theoretical saturation. Findings show that service-first display rules, weak recognition of safety authority, uneven sanctions, complaint-weighted evaluations, and ambiguous manuals heighten anxiety and role conflict. We theorize a sequential mechanism in which appraisal alters perceived control, and institutional signals channel incidents into two reproducible pathways, namely Path A, responsibility avoidance (responsibility shifting, symbolic performance, feigned ignorance, indifference), and Path B, uncertainty reduction (manual-based performance, systematic record-keeping, peer coordination, reliance on cockpit authority). We formalize this as the Uncertainty Avoidance-Service Safety Conflict (UASSC) model, reframing uncertainty avoidance from a national trait to an enacted micro-mechanism mediated by organizational rules and evaluation systems. Practical levers that reliably shift decisions from Path A to Path B include increasing the safety weight in complaint systems, improving manual clarity and access, making leadership backing visible at the point of action, digitalizing contemporaneous records, and strengthening legal and media deterrence. The model yields testable propositions and a practice-ready template for managing safety enforcement in service-intensive, safetycritical settings.
Deep learning offers powerful tools for anticipating tipping points in complex systems, yet its potential for detecting flickering (noise-driven switching between coexisting stable states) remains unexplored. Flickering is a hallmark of reduced resilience in climate systems, ecosystems, financial markets, and other systems. It can precede critical regime shifts that are highly impactful but difficult to predict. Here we show that convolutional–long short-term memory (CNN–LSTM) models, trained on synthetic time series generated from simple polynomial functions with additive noise, can accurately identify flickering patterns. Despite being trained on simplified dynamics, our models generalize to diverse stochastic systems and detect flickering in some empirical datasets, including dormouse body temperature records and palaeoclimate proxies from the African Humid Period. These findings demonstrate that deep learning can extract early warning signals from noisy, nonlinear time series, providing a flexible framework for identifying instability across a wide range of dynamical systems.
Soil organic matter (SOM) chemistry in agroecosystems is strongly influenced by land management and climate change, yet it remains unclear how climate-smart practices and warming interact with soil texture to influence SOM transformation and persistence. We applied targeted and non-targeted analyses to investigate the impacts of diverse crop rotations (DCR) and DCR combined with winter warming (DCR + W) on SOM chemistry across two agricultural soils (loamy sand and silt loam). After eight years of treatment, neither DCR nor DCR + W significantly altered total soil carbon (C), despite changes in SOM composition and microbial communities. Across soil types, DCR and DCR + W reduced microbial biomass by 26–34% relative to the conventional treatment and altered microbial community structure. In the silt loam soil, DCR, especially with warming, was associated with greater accumulation of microbial-derived compounds, consistent with potential physicochemical protection in a finer textured soil. Contrastingly, the coarse textured soil showed greater accumulation of plant-derived compounds, although losses of cutin- and suberin-derived biopolymers under DCR indicated treatment-dependent differences in their transformation and retention. Overall, DCR and winter warming altered SOM composition without detectable changes in total soil C, with soil texture heavily influencing molecular responses. These findings demonstrate that soil properties provide important context for how climate-smart practices and a changing climate influence SOM transformation and highlight the value of molecular-level measurements for detecting changes that are not apparent from soil C metrics alone.
