This study analyzes the long-run evolution of global airport connectivity from 1996 to 2024 and examines how resilience to major shocks varies across the connectivity distribution. Using an extended Global Airport Connectivity Index (GACI) panel covering 101,049 airport–year observations and 5428 airports, it documents steady expansion punctuated by crises, a redistribution of connectivity across continents, and an increasingly multipolar hub hierarchy. The paper then develops a reproducible, multi-crisis resilience index with three components: depth (magnitude of loss), speed (pace of recovery), and adaptability (post-shock upgrading in the global hierarchy). The index aggregates realized airport performance across multiple major shocks, applies empirical-Bayes regularization to improve comparability across airports with uneven shock histories, and filters micro-fluctuations below 1% to reduce noise. Relating resilience to connectivity uncovers a consistent inverse-U relationship: resilience increases with connectivity up to a mid-scale range and declines among the most globally connected hubs. This pattern is consistent with greater exposure to synchronized shocks and scale-related operational frictions, although the analysis identifies the relationship rather than its underlying mechanisms. Robustness checks include alternative component weightings, pre-COVID estimates, crisis-specific recovery horizons, alternative adaptability definitions, region-specific COVID timing, an alternative fixed-scale connectivity construction, and non-overlapping crisis windows. These checks confirm the stability of the pattern. By embedding airport-level recovery behaviour in a three-decade network context, the GACI–resilience framework provides a systematic basis for comparing hub types and benchmarking realized resilience, and for informing route-development, infrastructure-planning, and policy strategies during pandemics, geopolitical disruptions, and other major shocks.
Partial shading fragments the power–voltage characteristic of series-connected photovoltaic (PV) arrays into multiple local peaks, rendering conventional trackers ineffective and exposing the reliance of metaheuristic global maximum power point tracking (GMPPT) methods on random initialization and empirical tuning coefficients. This paper proposes TSB-GMPPT, a deterministic tanh-surrogate branch-and-bound GMPPT algorithm that eliminates both dependencies. A control-oriented hyperbolic-tangent surrogate of the PV I–V characteristic admits a non-iterative Lambert W asymptotic approximation for the maximum power point voltage. During discovery, a single saturation-region measurement per candidate is sufficient to construct a calibrated optimistic bound, safely pruning the search space before final peak localization. An offline polynomial calibration recovers the remaining parameters from short-circuit current. During discovery, an optimistic branch-and-bound rule maintains an upper power bound for each unconfirmed layer and permanently discards candidates that cannot exceed the incumbent confirmed power, contracting the admissible search space without exhaustive probing. A five-state finite-state machine governs initialization, shading-topology detection, discovery, winner refitting, convergence, and adaptive reinitialization. Experimental validation using a Chroma 62150H solar array simulator and a dSPACE DS1104 platform on five-module benchmark cases and additional eight-module complex multi-peak shading cases demonstrates average tracking efficiencies of 99.94% under partial shading and 99.98% under uniform irradiance, with corresponding average convergence times of 0.25 s and 0.21 s, respectively, for the five-module test set. Further validation under eight-module complex multi-peak shading cases and dynamic irradiance transitions confirms rapid re-tracking, accurate GMPP identification, and robust operation under realistic operating conditions.
This study investigates the integration of crowdshipping (CS) into e-commerce reverse logistics, specifically applied to the parcel transport stage between return points and transhipment facilities. Several barriers have limited CS adoption in forward logistics, such as trust, privacy, and security concerns due to direct customer–crowdshipper interaction, and the inability to offer bundled CS tasks that would enable attractive compensation schemes, but these constraints are mitigated in this transport-stage-focused reverse logistics setting. The proposed CS-integrated system operates in a business-to-business (B2B) context, eliminating customer contact and allowing bundled CS task assignment, thereby providing a more secure and operationally efficient setting for CS deployment. In this study, operational and external costs (i.e., per-kilometre social costs) for both conventional and CS-integrated reverse logistics systems are modelled and simulated across diverse spatial and demographic contexts, system scales, configurations, and levels of CS supply and behavioural dynamics. These simulations generate a large database that identifies the conditions under which the CS-integrated system can achieve operational and/or environmental advantages compared with the conventional system. Using a comprehensive simulation experiment based on systematically generated parameter combinations (45,000 simulation runs) and applying decision tree analysis, we find that the CS-integrated system reduces operational costs in nearly all cases, with savings reaching up to 70 %, particularly under conditions with sufficient CS supply and high participation willingness (i.e., low sensitivity to detour and compensation). However, environmental benefits are more context-dependent, emerging mainly in low-density, large-area settings where detours remain minimal. The study underscores the potential of CS to enhance the cost efficiency of reverse logistics while highlighting the importance of policy interventions that limit detours and induced travel demand in the CS-integrated system to ensure that sustainability gains are fully realised.
This study evaluates the seismic fragility of high-rise steel storage racks under long-distance and short-distance earthquake excitations using Incremental Dynamic Analysis (IDA). Two representative sites were selected to represent long- and short-distance earthquake scenarios. A numerical model of the high-rise rack system was developed and validated against substructure experimental results and benchmark data reported in the literature. The IDA results show that the earthquake scenario significantly influenced the seismic response of the investigated rack. Long-distance ground motions generally resulted in severe damage and collapse at lower seismic intensity levels, particularly for global deformation responses. Long-distance excitation also resulted in a more abrupt transition from severe damage to collapse, indicating a limited deformation reserve once severe damage was reached. In contrast, short-distance ground motions produced more localized damage and a more gradual progression toward collapse. In addition, serviceability assessments showed a higher susceptibility to residual misalignment under long-distance ground motions, highlighting potential risks to automated warehouse operations even at relatively low seismic intensities. Overall, the results indicate greater vulnerability of the investigated high-rise rack to long-distance ground motions in terms of global deformation, damage progression, and residual misalignment.
Existing literature on generative AI (genAI) in business education has examined students’ attitudes, motivation, and the ethics of genAI use, alongside ongoing debate about whether genAI over-reliance may diminish self-regulated learning (SRL). However, how students self-regulate their learning while working with genAI during authentic tasks remains under-explored. This study addresses this gap by examining (RQ1) how students' self-regulatory strategies manifest during genAI-assisted learning and (RQ2) how these strategies change through genAI interaction. A thematic analysis was conducted of 34 metacognitive reflections generated across two scaffolded, four-stage experiential learning activities in a Lean Startup unit. Findings inform a three-stage AI-mediated recursive loop model, comprising monitoring, strategic adoption, and limitation identification, that extends cyclical models of self-regulated learning by showing how regulation may be initiated within the task by genAI-generated suggestions. Within this structured, reflective learning context, students indicated evaluative judgement and metacognitive regulation, providing preliminary evidence that scaffolded genAI activities can elicit active rather than passive engagement. GenAI-supported cognitive deepening was task-contingent, emerging most strongly during applied refinement tasks. The model offers management educators a pedagogical process model for designing scaffolded genAI-assisted activities that elicit monitoring, selective adoption, and critical reflection.
