Search Results (3,093)

Predicting systemic stress in high-dimensional equity markets remains challenging, owing to non-stationarity, heavy tails, and regime shifts. This paper introduces GWS-STNet (Gaussian-Weighted Swin Spatio-Temporal Network), a deep learning architecture grounded in functional analysis and thermodynamic analogy. A Gaussian-Weighted Swin Operator (${\mathcal{G}}^{\sigma }$) replaces the standard shifted-window attention mechanism with a kernel-regularised counterpart on the Hilbert space ($\mathcal{H}$) of financial spatio-temporal voxels. The principal theoretical contribution is a proof, via the Banach Fixed-Point Theorem, that the window-level attention operator (${\mathcal{G}}^{\sigma }$) is a strict contraction (Lipschitz constant of $\kappa <1$) under a bandwidth of $\sigma <{\left(2\pi \right)}^{-1/2}$, guaranteeing convergence of internal network representations to a unique fixed point. Metabolic Saliency (${\mathcal{S}}_{\mathrm{ms}}$), derived from the exact Jacobian of the Power Mapping Network (PMNet) weighted by pairwise transfer entropy, provides intrinsic, post hoc-free attribution of sector-level stress contributions. Empirical validation on 15 large-capitalisation JSE securities ($T=2857$ trading days, January 2015–December 2025) with Eskom load-shedding stages as exogenous stress injectors shows that GWS-STNet outperforms nine baselines, including classical benchmarks (Random Walk and AR(1)) and state-of-the-art Transformers across RMSE, MAE, the Gaussian Calibration Score (GCS), and the Metabolic Efficiency Ratio (MER), with Diebold–Mariano $p<0.001$. Full article

Technological developments in transportation have moved quickly, often faster than the frameworks used to evaluate their broader societal and environmental implications. This study examines the extent to which Tesla’s automotive activities contribute to long-term societal well-being from a consequentialist utilitarian perspective, focusing on two related developments: (1) vehicles equipped with advanced driver assistance systems (ADAS) and (2) vehicles powered by electricity. Tesla provides a useful focal case in that it brings these two developments together in a single, highly visible setting. Using Tesla as an exploratory qualitative case, the analysis assesses both technologies within a single ethical and sustainability framework, examining how their effects combine across safety, environmental, and broader societal outcomes. Because the two technologies act on many of the same outcomes and stakeholders, they interact: they reinforce one another in some respects and offset one another in others. In the case of road safety, for example, the additional mass of an electric vehicle raises the severity of collisions even as driver assistance works to reduce their frequency. The analysis suggests an overall net positive societal impact, while recognizing the uncertainties and trade-offs that remain. This assessment rests mainly on two considerations: the likely reduction in traffic-related injuries and fatalities associated with wider adoption of ADAS-equipped vehicles, and the expectation that, in most contexts, electric vehicles provide a net environmental benefit, particularly through lower levels of harmful air pollutants relative to internal combustion engines. These benefits are not automatic, however, but depend on broader system conditions, including whether electrification and automation move transportation beyond established patterns of car dependence or reinforce them. The paper concludes by outlining the implications of these findings, while acknowledging the limits of the analysis and pointing to areas for future research Full article

An Aspen Plus model was developed to evaluate the upgrading of municipal solid waste (MSW) fast-pyrolysis oil into transportation-compatible biofuels. MSW-derived bio-oil is compositionally complex, containing oxygenated and nitrogen-containing compounds together with substantial water, resulting in low stability and poor fuel quality. This study compared a one-stage high-temperature hydrotreating (HTH) process with a two-stage configuration incorporating low-temperature esterification (LTE) pretreatment. The assessment focused on upgraded bio-oil yield, fuel quality and hydrogen demand. In both configurations, hydrogen was generated internally by autothermal reforming of a fraction of the bio-oil or esterified oil. The higher heating value increased from approximately 28 MJ/kg for the untreated bio-oil to 40 MJ/kg and 46 MJ/kg for the one and two-stage processes, respectively. The molar H/C ratio increased from 1.11 to 1.26 and 1.88, placing the two-stage product within the range of conventional transportation fuels. A reforming fraction of 0.25 of the esterified bio-oil was sufficient for hydrogen self-sufficiency in the two-stage process, whereas the one-stage process required a fraction of above 0.30. The one-stage process achieved higher carbon retention, upgraded bio-oil yield, and overall fuel efficiency than the two-stage process, with values of 31%, 16%, and 33%, compared to 24%, 13%, and 29%, respectively. However, the two-stage process increased hydrogen retention from 26% to 30%, while its decoupled DMSW carbon retention was 26.41%. Esterification therefore enhanced hydrogen retention and fuel quality at the expense of carbon retention, liquid yield, and overall fuel efficiency, highlighting the quality–efficiency trade-off of the two-stage configuration. Full article

Despite the rapid expansion of unmanned aerial vehicle (UAV) applications across precision agriculture, logistics, infrastructure inspection, disaster response, and aerial surveying, objective and quantitative hardware evaluation criteria for UAV components remain insufficiently developed. This paper proposes quantitative key performance indicators (KPIs) for thirteen core hardware subsystems, including airframe and propulsion, battery and power supply, flight control, wireless communication, imaging (camera), Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) positioning, thermal management, acoustic and vibration characteristics, AI-based autonomous flight, electromagnetic compatibility (EMC), cybersecurity, and reliability and environmental qualification, together with LiDAR payload evaluation criteria. International standardization activities by 3GPP (Release 15/17), IEEE (1936–1958 series), American society for photogrammetry and remote sensing (ASPRS), and national regulatory frameworks are synthesized to define measurable performance metrics and recommended test methods for each subsystem. An integrated KPI matrix maps application-domain-specific performance targets—encompassing surveying (real-time kinematic (RTK) horizontal accuracy ≤ 2 cm root-mean-square error (RMSE), ground sample distance (GSD) ≤ 2 cm/px), infrastructure inspection (LiDAR payload up to 8 kg, beyond visual line-of-sight (BVLOS) latency ≤ 140 ms), and logistics delivery (payload ≥ 2 kg, precision landing ≤ 50 cm)—demonstrating that no universal platform can simultaneously satisfy all domain requirements. A fuzzy-AHP weighting procedure and inter-subsystem coupling analysis are introduced to address size, weight, and power (SWaP) trade-off relationships that purely additive scoring models cannot capture. The proposed evaluation framework is intended to contribute practically to UAV standardization, certification, and quality management across the full design–procurement–operation lifecycle. Full article

Global food trade exposes sharp differences in food safety regulation, especially between the EU and the US. The EU follows a precautionary, hazard-based model, allowing intervention under scientific uncertainty to protect consumers, maintain public trust, and avoid long-term risks. The US applies a science-based, proof-of-harm approach, requiring clearer evidence of risk before limiting market access, supporting innovation and regulatory efficiency. These contrasting philosophies create trade tensions and non-tariff barriers, as seen in disputes over hormone-treated beef, genetically modified organisms, and chlorine-washed poultry. Beyond the transatlantic context, countries adopt precautionary, science-based, or hybrid systems depending on domestic priorities, institutional capacity, and trade commitments. Hybrid models in India, China, and parts of Africa combine precautionary safeguards with evidence-based risk assessment to balance consumer protection and market access. International bodies such as Codex Alimentarius, the WHO, and the WTO help manage regulatory divergence through standards, guidance, and dispute resolution, while recognising precaution under uncertainty. Recent EU agreements with Mercosur and India show pragmatic cooperation through transparency, safeguards, and sanitary and phytosanitary commitments. Overall, effective global food governance depends on hybrid, coordinated, and adaptive approaches that reconcile health protection, trade facilitation, and innovation. Full article

This study examines the effects of economic policy uncertainty (EPU) on three dimensions of financial market dynamics—stock market returns, stock market volatility, and exchange rate volatility—across 15 G20 economies over the period 2006 Q1–2024 Q4. Employing a rigorous second-generation panel econometric framework that accounts for cross-sectional dependence and slope heterogeneity, we apply the Common Correlated Effects Mean Group (CCEMG) and Augmented Mean Group (AMG) estimators as primary estimators, complemented by cross-sectionally augmented ARDL (CS-ARDL) for short- and long-run dynamics and the Method of Moments Quantile Regression (MMQR) for distributional analysis. EPU is significantly associated with lower stock market returns, particularly in advanced economies and at lower quantiles of the return distribution. The impact of EPU on stock market volatility is not strong for mean-based estimators even when global uncertainty (VIX) is controlled for, and global fear dominates domestic policy uncertainty as a driver of volatility. Exchange rate volatility is positively and significantly associated with EPU, particularly in higher-volatility regimes. Subgroup analysis shows that EPU–return effects are stronger in advanced economies, but EPU–exchange rate volatility responses are stronger in emerging markets. These results have important implications for portfolio allocation, hedging strategies and macroprudential policy decisions in G20 economies. Full article

The refugee rentier state seeks to manipulate mass population movements to obtain economic and political/strategic ‘rents’, asserting itself in a way that challenges the premises of Global South marginality, disempowerment and exclusion within the international system. This article develops the concept of the refugee rentier state, which it understands as a political economy framework for analysing how host states transform refugee populations into sources of bargaining power within asymmetric systems of global governance, while showing how this “trading” feeds into economic vulnerabilities and exclusions associated with forced labour arrangements. It shows how refugee hosting can function as a strategic asset through which states extract economic and political/strategic rents from wealthier counterparts. Drawing on Turkey’s engagement with the European Union in the European refugee “crisis”, the article examines how refugee rentier strategies operate within, rather than outside, established migration governance regimes, giving rise to economic informality and partial labour market integration. In applying the rentier state model to EU–Turkey interactions, it situates refugee rentierism within the structural logic of EU migration externalisation and the institutional weaknesses of international refugee protection, while considering how they feed into the vulnerability of displaced populations, and specifically exposure to economic exclusion and marginalisation within transit countries. Full article

Industry 4.0 is widely recognized as a strategic enabler of productivity, flexibility, and competitiveness in manufacturing systems. However, in emerging economies, declared preparedness for digital transformation often exceeds reported implementation. This study examines the readiness–adoption gap of Industry 4.0 in surveyed large manufacturing companies in Valle del Cauca, Colombia. An empirical cross-sectional study was conducted through a structured survey administered to large manufacturing companies in the region. The instrument was grounded in the Technology Acceptance Model and informed by a sociotechnical understanding of Industry 4.0 adoption. It captured perceptions of ICT infrastructure, workforce capabilities, expected benefits, implementation challenges, ease of use, and the reported implementation status of 20 Industry 4.0 technologies. Data from 59 valid responses were analyzed using descriptive statistical techniques, cross-technology comparisons, and an aggregate descriptive synthesis of readiness and adoption indicators. The findings suggest that adoption remains concentrated in foundational technologies such as cybersecurity, cloud computing, and data analytics, while advanced technologies, including artificial intelligence, cyber–physical systems, digital twins, and immersive technologies, show limited implementation despite favorable readiness perceptions. The aggregate synthesis points to a medium-to-high readiness proxy and a substantially lower average adoption level, suggesting the presence of a readiness–adoption gap. The study contributes empirical evidence from large manufacturing companies and derives two practical outputs: a readiness–adoption matrix and a staged roadmap for capability building and technology prioritization. These outputs are presented as context-specific exploratory tools rather than universally validated models. Full article

Confronted with the constraints of global carbon reduction mandates and the widespread implementation of cap-and-trade (CAT) policy, competing manufacturers face critical choices in carbon quota trading, such as engaging in external markets or internal agreements. We develop a duopolistic game model comprising a low-carbon manufacturer (M_G) and a traditional manufacturer (M_T) under a CAT framework. In a perfect carbon quota trading market, manufacturers simultaneously cooperate and compete, facing a strategic choice between external trading through the open carbon market and internal trading agreements. We investigate how the low-carbon development level, carbon quota surplus, and internal carbon price affect their choices of carbon quota trading modes. Analytical results indicate that in the scenario where M_G’s quota surplus is insufficient to fully meet M_T’s demand, both manufacturers can achieve Pareto improvement in their respective profits within a certain range of internal carbon prices. Otherwise, the internal trading agreements may only guarantee an increase in their aggregate profits. A numerical analysis based on the actual situation of China’s steel industry verifies the theoretical conclusions. Full article

Methane ($C{H}_4$) is the second most important anthropogenic greenhouse gas, accounting for approximately 30% of current global warming. Since 2007, atmospheric methane concentrations have been increasing at an accelerating rate, reaching a record 1945.85 ppb in November 2025. The emergence of high-resolution satellite constellations has transformed our ability to detect, quantify, and attribute methane emissions from space. This review provides a comprehensive analysis of the current state of high-resolution global methane mapping, examining: (1) the evolution of satellite missions from coarse-resolution sounders like TROPOMI (5.5 × 7 km) to very high-resolution imagers including WorldView-3 (3.7 m), GHGSat (50 m), and the recently launched Tanager-1 (30 m); (2) advances in retrieval algorithms, including the transition from physics-based matched filter methods to deep learning approaches such as U-Net architectures achieving F1-scores of 78.4% on Sentinel-2 imagery; (3) integration of satellite observations with atmospheric inverse models for flux estimation; (4) the impact of satellite-derived data on policy frameworks including the Global Methane Pledge and EPA’s Super-Emitter Program; and (5) remaining challenges including cloud contamination, detection limit trade-offs, and the need for sustained validation networks. We synthesize findings from over 200 peer-reviewed studies and analyze 42 years of NOAA global methane observations to demonstrate how the convergence of improved spatial resolution, machine learning, and international coordination is enabling unprecedented transparency in global methane monitoring. The review concludes with recommendations for future satellite missions and data assimilation strategies needed to meet the Global Methane Pledge target of 30% emission reductions by 2030. Full article

The classical monetary trilemma implies a binding trade-off among exchange rate stability, capital mobility, and monetary autonomy. Yet, emerging market economies increasingly operate hybrid policy configurations that depart systematically from the trilemma’s corner solutions. This paper proposes a continuous, time-varying measure of such departures—the Hybridity of Regime Index (HRI)—extracted via a dynamic factor model from sub-indices capturing exchange rate hybridity, capital account segmentation, and effective monetary autonomy for a balanced panel of thirty emerging markets over the period 2005–2024. The analysis yields four principal findings. First, a secular increase in average regime hybridity is observed, with a marked acceleration following the financial fragmentation shocks of 2022. Second, moderate hybridity is associated with attenuated output and inflation volatility, and local projections show that high-HRI economies experience milder output contractions in the immediate aftermath of global financial shocks. Third, panel threshold regressions identify an endogenous HRI level beyond which the stabilizing effect reverses: further hybridity amplifies macroeconomic volatility and erodes reserve adequacy. Fourth, the post-2022 geopolitical fragmentation of the international monetary system has amplified the pre-existing trend toward hybridity, with sanction-affected economies exhibiting discontinuous jumps in HRI that push them into the high-vulnerability regime. This paper characterizes this non-linear pattern as a resilience–vulnerability nexus and discusses its implications for early warning indicators and for the assessment of policy responses to the fragmentation of the international monetary system. Full article

Using an international sample of firms from 47 countries/regions over the years 2010–2020, we examine whether third-party verification of carbon emissions information affects the speed at which firms adjust their capital structure toward the trade-off theory’s optimal leverage target. Using alternative estimation techniques and robustness checks, we find that third-party carbon assurance significantly accelerates firms’ leverage adjustment speed. Firms that engage in independent carbon verification adjust more rapidly toward their target capital structure than non-assured firms. We extended our investigation and confirmed that this effect persists across both developed and developing markets. These results support the notion that carbon assurance is associated with lower information asymmetry between firms and lenders, thereby lowering the cost of external debt and facilitating faster capital structure rebalancing. We further investigate whether the relationship differs by assurance provider type by distinguishing between Big Four and non-Big Four assurance providers. The results remain robust when distinguishing between Big Four and non-Big Four assurance providers regardless of the assurer quality, confirming that assured firms adjust their capital structures faster than non-assured firms. The outcomes of this study demonstrate that firms’ sustainability reporting can shape the speed of capital structure adjustment. Full article

Even though hydrogen fuel cell vehicles are essential for decarbonization, getting consumers to accept them remains a tough issue. This research combines normative and cognitive theories to find out if environmental values actually take precedence over logical risk assessments. We analyzed data from 761 consumers in Türkiye, using the structural equation model and Hayes’ PROCESS Macro. The results show that, although environmental concerns strongly increased awareness (β = 0.80) and awareness lowered perceived risk (β = −0.34), perceived risk itself did not significantly predict purchase intention (β = 0.03, p > 0.05). Instead, decisions were motivated by a hope-based path: Concern → Awareness → Usefulness → Intention. We tentatively interpret this as Green Motivation Dominance, whereby environmentally motivated early adopters appear to prioritize environmental benefits over technical hazards; notably, perceived usefulness significantly predicted intention (β = 0.29), and the serial path Concern → Awareness → Usefulness → Intention was significant (effect = 0.25, 95% CI [0.17, 0.35]). This interpretation, however, warrants direct testing in future research. Thus, the main factor driving adoption is the presence of advantages, not the absence of risk. Therefore, policymakers should focus on maximizing benefits rather than just trying to reduce risks. Full article

Public YOLO model releases typically provide high-level architectural descriptions and headline benchmark results but offer limited empirical attribution of performance to individual blocks under controlled training conditions. This paper presents a modular, block-level analysis of YOLO26’s object detection architecture, detailing the design, function, and contribution of each component. We systematically examine YOLO26’s convolutional modules, bottleneck-based refinement blocks, spatial pyramid pooling, and position-sensitive attention mechanisms. Each block is analyzed in terms of objective and internal flow. In parallel, we conduct targeted ablation studies to quantify the effect of key design choices on accuracy (mAP@0.50:0.95) and inference latency under a fixed seed-0, COCO-only, fully specified training and benchmarking protocol. Experiments use the MS COCOdataset with the standard train2017 split (≈118 k images) for training and the full val2017 split (5 k images) for evaluation. The result is a self-contained empirical architectural-attribution reference that supports interpretability, reproducibility, and evidence-based architectural decision-making for real-time detection models. Beyond isolated ablations, we further synthesize the best-performing design choices into combined YOLO26n configurations and compare them against the default baseline. The best combined configuration improves mAP@0.50:0.95 from 0.3933 to 0.3969, while introducing only a marginal latency increase from 0.99 ms to 1.00 ms under TensorRT FP16 benchmarking. This analysis identifies an improved accuracy–latency trade-off and provides an incremental architectural configuration contribution supported by controlled experiments. The study is, therefore, framed as a controlled empirical analysis and configuration-refinement study of YOLO26, rather than as the proposal of a new detector family or a claim of universal detector superiority. Full article

Surface congestion and taxiing delays at high-density airports increasingly constrain aviation sustainability, as ground-phase fuel consumption and emissions constitute a significant share of total airport emissions. Existing studies typically decouple air traffic flow management from ground resource scheduling, hindering coordinated optimization of punctuality, environmental benefits, and resource utilization. This paper proposes a multi-objective optimization method for runway-taxiway systems oriented toward air–ground collaborative decision-making, integrating Calculated Take-Off Time (CTOT) compliance constraints. A tri-objective mixed-integer programming model is formulated to minimize CTOT deviation, total taxiing time, and runway workload imbalance. A hybrid intelligent algorithm, SSA-SCA-NSGA-II, is designed with a bidirectional elite feedback mechanism to address this NP-hard problem. Validation uses real operational data of 58 departure flights during a peak period at Beijing Daxing International Airport. The results demonstrate that the proposed method achieves effective trade-offs on the Pareto front: CTOT compliance rate increased from 77.6% to 89.7–96.6%; total taxiing time decreased from 692 min to 551–635 min; and dual-runway utilization imbalance declined from 5.2% to 1.7–3.8%. These improvements translate into quantifiable sustainability gains: fuel consumption is reduced by 1425–3525 kg and CO₂ emissions by 4503–11,139 kg per peak hour, alongside a 19-percentage point improvement in punctuality that lowers passenger delay costs and reduces controller coordination workload. By simultaneously advancing environmental sustainability (carbon footprint reduction), economic sustainability (fuel and operational cost savings), and social sustainability (service punctuality and labor efficiency), the framework provides a measurable, monitorable, and policy-relevant decision-support tool for green airport surface operations aligned with sustainable development goals (SDGs). Full article