The empirical foundation of science has often been characterised in terms of the observable-unobservable distinction, developed either syntactically, as a set of statements, or semantically, as aspects of the world represented by models. These approaches have faced well-known challenges: the difficulty of drawing a sharp line between observables and unobservables, the theory-ladenness of observation, and the problem of specifying what, exactly, is observed. We take up these challenges by developing an alternative framework inspired by the phenomenological concept of the life-world. Our proposal offers several advantages: it provides a richer characterization of the foundational level, clarifying the conditions of possibility for inquiry and avoiding overly narrow views that equate the empirical basis with linguistic statements or model components; it stresses the role of the empirical basis not only in individuating phenomena and testing claims, but also in sustaining scientific inquiry throughout; it extends beyond the senses to include cognitive and practical dimensions that shape experience and scientific practice; it foregrounds the role of the observing subject; it highlights the continuity between observation and theory, and between scientific and extra-scientific sense-making; and it integrates the most compelling, if sometimes at first pass conflicting, relevant analytic insights.
The expansion of 5G-enabled Internet of Things (IoT) networks, while enabling transformative applications, significantly increases the attack surface and necessitates security solutions that extend beyond traditional intrusion detection. Existing intrusion detection systems (IDSs) mainly operate in an open-loop manner, excelling at classification but lacking the ability for autonomous, safety-aware remediation. This gap is particularly critical in 5G environments, where manual intervention is too slow and naive automation can lead to severe service disruptions. To address this issue, we propose a novel Self-Healing Intrusion Detection System (SH-IDS) framework that develops a closed-loop cyber defense mechanism. The main technical contribution is the integration of a deep neural network-based threat detector, which offers uncertainty-quantified predictions, with a safety-aware reinforcement learning (RL) engine formulated as a Constrained Markov Decision Process (CMDP). The CMDP explicitly models operational safety as cost constraints, and a new runtime safety shield actively adjusts any unsafe action proposed by the RL agent to the nearest safe alternative, ensuring operational integrity. Additionally, we introduce a composite utility function for the comprehensive evaluation of the system. Empirical analysis on the 5G-NIDD dataset demonstrates the superior performance of our framework: the detector achieves 98.26% accuracy, while the safe RL agent learns effective mitigation policies. Importantly, the safety shield blocked up to 70 unsafe actions under strict constraints, and analysis of the learned Q-tables confirms that the agent internalizes safety, avoiding overly disruptive actions, such as isolating nodes for minor threats. The system also maintains high efficiency with a compact model size of 121.7 KB and sub-millisecond latency, confirming its practical deployability for real-time 5G-IoT security.
This study investigates how ESG-linked debt structures and green finance developments are reflected in maritime debt financings. Using a manually collected dataset of 1,470 debt transactions by 564 shipping companies between January 1998 and August 2024, we compare ESG-linked and conventional instruments. Qualitative content analysis of transaction descriptions shows an emphasis on sustainability-related terminology, particularly references to sustainability, ESG, green, and environmental factors, concentrated in ESG-labelled deals. Quantitative analysis indicates no statistically significant differences in loan amounts or maturities, suggesting that ESG-labelled instruments largely retain the structural features of traditional debt products. Nonetheless, ESG-linked financings are associated with lower interest or coupon rates, consistent with lenders rewarding credible sustainability commitments. Despite their growing visibility, ESG-linked instruments remain a minority of maritime financings, while conventional structures dominate. These findings have important managerial and academic implications, which are discussed herein.
Research on routine dynamics has shown that, due to the improvisational nature of agency and the ever-present situational novelty, every routine enactment is, to some extent, novel. However, extant theorizing, by focusing predominantly on observable patterns of action, underplays the role of routine participants' lived experience in shaping routine enactment. Seeking to address this limitation, we draw upon pragmatist thinking to develop an integrative process model of routine enactment, focusing in particular on the agency-situational novelty interplay. Specifically, we identify the process of inquiry as the general mechanism through which routine participants respond to situational novelty, and distinguish three types of routine enactment: habitual enactment, spontaneous variation, and reconstruction. Our model contributes to routine dynamics research by (a) integrating extant research at a higher level of generality, while being sensitive to the local circumstances of routine enactment; (b) accounting for the interplay between deliberate and pre-reflective, embodied responses to novelty; (c) strengthening the richness and practical relevance of routine enactment explanations by attending closely to what matters most to routine participants when they tackle situational novelty; and (d) opening multiple promising avenues for future study by establishing links with diverse areas of research relating to routine enactment.
Exchange Traded Funds (ETFs) are investment funds traded on stock exchanges that hold financial assets such as commodities, stocks, bonds, currencies, futures contracts, or debt. ETFs allow shareholders indirect ownership of the underlying assets and entitlement to associated profits, such as interest or dividends. This study investigates the relationship between the trading of a dry-bulk ETF in the maritime sector and its effect on the volatility of dry-bulk spot freight rates. Using GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, we analyse asymmetric volatility dynamics across two periods, March 2014 to March 2018 and March 2018 to March 2022, while controlling for global economic factors including West Texas Intermediate (WTI), Brent oil prices, and the S P 500 Commodity Index. Our findings reveal a significant positive correlation between the dry-bulk ETF’s presence and increased volatility in dry-bulk spot rates, with notable differences observed between the Capesize and Panamax vessel segments. The introduction of the dry-bulk ETF has materially influenced spot market dynamics, affecting decision-making processes within the sector. This research contributes novel insights into the financialization of the dry-bulk shipping market, providing valuable implications for shipowners, charterers, regulators, and investors. In particular, higher spot-rate volatility can affect chartering strategy and contract selection (spot vs. period), hedging intensity via FFAs/ETFs, and earnings-at-risk and margining liquidity needs. Understanding how ETFs impact market risk is critical for strategic planning and maintaining stability in the maritime freight market.
