Emergency response decision-making in high-uncertainty socio-technical systems is often hindered by incomplete information, reporting bias, and the trade-off between rapid intervention and unnecessary deployment. In the present study, we propose a novel Bayesian-Stackelberg methodology to support emergency response decision-making under uncertainty by formalizing the strategic interaction between information providers and responders. In this framework, the operator acts as the strategic leader and transmits a signal regarding the severity of an incident; an additional sensor is treated as a non-strategic evidence source; and the responder acts as the follower, deciding whether to engage or wait based on prior information and a posterior-risk threshold. The methodology integrates prior incident probabilities that can be derived from quantitative risk assessment, historical data, or expert elicitation, together with reporting-credibility parameters, and consequence-based cost structures into a closed-form deployment threshold. It further incorporates incentive-design mechanisms to evaluate truthful reporting under a Perfect Bayesian Stackelberg Equilibrium. A hydrogen refueling station is used as a scenario-based operational case study to demonstrate the practical applicability of the proposed approach. Scenario-based and sensitivity analyses are conducted to demonstrate model behavior and examine the influence of key parameters on response decisions. The study provides a transparent, data-informed, and calibration-ready decision-support framework for emergency response under uncertainty, with potential to improve communication credibility, response consistency, and cost-consequence reasoning in complex industrial environments.
This study develops a four-dimensional framework to evaluate how regulatory divergence between ASC 842 and IFRS 16 distorts financial metrics, operational control assessments, and asset valuations during airline consolidations. It argues that comparability problems do not arise from accounting treatment alone, but from the interaction of operational control doctrine, lease form, and acquisition-date valuation in airline mergers. The study uses a comparative case framework centered on Korean Air-Asiana as the primary post-COVID merger case, with Air France-KLM and United-Continental as supporting reporting and merger benchmarks. Particular attention is given to dry versus wet lease classification, right-of-use asset and lease liability recognition, EBITDA distortion, leverage effects, and merger-related lease remeasurement. The study concludes that lease reform improved transparency but did not eliminate analytical inconsistency, especially in cross-border airline competition and distressed merger settings.
For over three decades, the Airline Quality Rating (AQR) has used the same expert weights to preserve longitudinal comparability. We examine whether the published aggregation supports comparison over time and aligns with passenger satisfaction. We use 38 years (1987–2024) of Air Travel Consumer Report data for 25 carriers and evaluate alignment with the American Customer Satisfaction Index (ACSI) in 195 carrier-years from 12 carriers.The AQR combines differently scaled rates without standardization, so reporting conventions override its expert weights: complaints, assigned the lowest weight, account for 79–83% of total absolute weighted-term magnitude in the 2022–2024 industry aggregates. Baggage and complaint definitions changed during the study period, weakening comparability across reporting regimes. The combined on-time measure can conceal a COVID-era reversal between delays and cancellations. Against ACSI, the published AQR has weak alignment (r = 0.188) and explains virtually none of the within-carrier satisfaction variation (R2 = 0.001). An illustrative standardized, era-specific PCA benchmark has a larger in-sample correlation (r = 0.483). Under two ways of bridging the baggage reporting change, standardization accounts for roughly one-half to seven-tenths of this point-estimate increase; the benefit of coefficient re-estimation is not established under carrier-held-out scoring.The PCA composite is a diagnostic benchmark, not a replacement index. The AQR retains cross-sectional ranking information but should not be used alone to infer changes in service quality over time. A defensible longitudinal index must standardize inputs, bridge reporting changes, monitor delays and cancellations separately, and externally revalidate any revised aggregation rule.
Sweetpotato is a crucial food crop globally, valued for both its economic significance and health benefits. However, the prevalence of sweetpotato virus diseases (SPVD) poses a serious threat to the industry, leading to reduced yields and economic losses for farmers. Efficient diagnostic techniques are essential for ensuring food security and consumer health. Traditional diagnostic methods are effective but suffer from complexity, time consumption, and high costs. To address these challenges, a novel real-time end-to-end detector called SPVD-DETR based on the Transformer architecture is proposed in this study. By utilizing the unmanned aerial vehicle (UAV) orthomosaic image, SPVD can be diagnosed in real-time at the field scale. First, aerial survey tasks are customized with automated drone tools to rapidly scan sweetpotato fields, generating high-resolution orthophotos and a stitched orthomosaic image for analysis. Then, the object detector is enhanced by incorporating efficient backbones and hybrid encoder modules such as cascaded group self-attention, attention-based scale fusion, and dynamic upsampling. Extensive ablation studies and comparative results show that SPVD-DETR achieves a good balance between real-time performance and accuracy. Next, the model is fine-tuned on the SPVD image tiles and achieves a detection accuracy of 31.3% mean average precision (mAP) with the fastest inference speed of 90 frames per second (FPS). Finally, the prediction results are mapped back to the orthomosaic image, estimating an overall SPVD incidence rate of 15% with a misdiagnosis rate of 14%. This study introduces a novel paradigm for detecting SPVD at the field scale, promoting automatic and intelligent plant disease detection for large-scale high-throughput phenotyping in precision agriculture.
Let be a quantum graph without quantum sources and be the quantum edge correspondence for . Our main results include sufficient conditions for simplicity of the Cuntz-Pimsner algebra in terms of and for defining a surjection from the quantum Cuntz-Krieger algebra onto a particular relative Cuntz-Pimsner algebra for . As an application of these two results, we give the first example of a quantum graph with distinct quantum Cuntz-Krieger and local quantum Cuntz-Krieger algebras.We also characterize simplicity of for some fundamental examples of quantum graphs, including quantum graphs on a single full matrix algebra, complete quantum graphs, and trivial quantum graphs. Along the way, we provide an equivalent condition for minimality of and sufficient conditions for aperiodicity of in terms of the underlying quantum graph .
