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.
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, safety-critical 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.
To address state estimation of target system under data scarcity conditions (i.e., no output measurement data available), this manuscript proposes novel transfer learning-based moving horizon estimator designs. Unlike conventional state estimator/observer designs that require data from the same system, the proposed approach leverages measurement data from a related but different (source) system to estimate the state of the target system, which addresses practical challenges where monitoring data from the target system may be limited, such as during startup, early-stage, or experiencing new operating conditions. More specifically, two novel robust transfer moving horizon estimation methods are developed for the target system with limited measurement data and unknown-but-bounded model parameters. These proposed methods enable accurate state estimation of the target system by effectively transferring data and model structure from the source system. Furthermore, the stability analyses of the proposed methods are provided. The upper bound of the estimation error depends on three types of similarities/differences between source and target systems, including initial conditions, disturbance levels, and model parameters. Finally, the theoretical results are validated via a simulation example and a pilot-scale experimental study.
Agri-food chains, especially fruit, produce about 10% of global GHG emissions, with more than half arising after harvest. Yet current methods, such as life cycle assessment (LCA), lack the spatial, temporal and physiological detail needed to resolve stage-specific drivers of environmental impact. We aim to overcome these limitations by developing a spatio-temporal, physics-based life cycle model that quantifies climate change (CC) and fossil resource use (RU,f) impacts for individual fruits and shipments across a transcontinental maritime supply chain. The proposed framework is demonstrated using a case study of the citrus supply chain connecting production in South Africa to consumption in the Netherlands. We show that variability among fruits and between shipments substantially alters CC and RU,f impact outcomes, demonstrating the need for dynamic rather than static supply-chain assessment. Energy use, maritime transport and food loss dominate impacts, with the farm-to-packhouse and overseas shipping stages contributing up to 0.5 kg CO₂-eq kg⁻¹ and 2 MJ kg⁻¹ of fruit consumed, depending on local conditions and cold-chain management. Stage-specific interventions, such as reducing field heat prior to forced-air cooling or increasing shipping temperatures from −1 °C to 1–3 °C under validated phytosanitary conditions, can meaningfully lower impacts. This dynamic framework offers a more realistic basis for identifying and achieving feasible decarbonization in global fruit supply systems.
